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Demand Analysis For Media

Table of Contents

I. WHY DEMAND ANALYSIS

1. Case Discussion: Viacom Golden Years Media

2. Importance and Special Problems of Demand Estimation for Media

Industries

A. Examples for the Problems of Forecasting Demand

(1) Type I Errors

(2) Type II Errors

B. Why Do People Consume Media?

(1) Media for Information

(2) The Need for Entertainment

(3) Media Use for Social Relations

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C. How Media Companies Organize their Demand Research

II. ANALYTICAL/STATISTICAL MODELS

1. Psycho-Physiology Testing

A. Heart Rate

B. Electrodermal Activity

C. Facial Electromyography

D. Respiratory Sinus Arythmia Irregularity

E. Electroencephalographic (EEG) Activity

2. Statistical Inference

3. Econometric Demand Estimation

A. Estimation of Demand Curves

(1) Demand Estimation for Newsprint

(2) Live Entertainment

(3) Estimating the Effects of General Economy on

Advertising

(4) Competing Video Games

(5) Modeling Film Box Office

3. Conjoint Analysis

4. Diffusion Models

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5. Case Discussion

III. EMPIRICAL SAMPLING OF AUDIENCE/CONSUMERS

1. Sampling Methods

A. Personal Interviews

B. Mail and Phone Surveys

C. Focus Groups

D. Using the Internet as a Survey Tool

(1) User-Level Measurement

(2) The Data Meter

(3) Cookies

(4) Mouse Activity Measurement

E. Expert Surveys: Comb Analysis

F. Expert Surveys: Delphi

G. Surveys of Trendsetters and Opinion Makers

H. Automatic Audience Metering

(1) Diary System

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(2) Telephone Surveys

(3) Automated Metering

2. Next-Generation People Meter: The Digital Meter System

3. Metering Alternatives: Cable Box and TiVo Box

4. Audience Metrics

A. Nielsen Audience Formulas

(1) HUT

(2) Rating

(3) Share

(4) Gross Rating Points

(5) CUME

(6) Average Quarter Home Audience (AQH)

(7) Average Frequency (AF) of Exposure

(8) Cost Per Thousand (CPM)

(a) Why Are CPM Prices Different for Different

Media?

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(9) Quads

(10) “Q”

IV. DEMAND EXPERIMENTS

1. Test Marketing

2. Uncontrolled Studies

3. Controlled Studies of Actual Purchases

4. Laboratory Purchase Experiments

V. MEASURING ACTUAL SALES

1. Books: Bestseller List

2. Music Sales

3. Direct Sales: Measuring Film Audiences

4. RFID Tracking

VI. MEASURING TRAFFIC

1. 3 Approaches to Measuring Internet Audiences

A. Site-Level Measurement

B. Ad-Level Measurement

C. User-Level Measurement

2. Data Mining

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3. Case Discussion: How to Measure the Usage of the “Golden Years”

Internet Portal?

VII. SELF-REPORTING

1. Measuring Circulation

A. Producer Self-Reporting

B. Circulation Verification

C. Problems with Measuring circulation

VIII. CONCLUSIONS

1. Tools Covered

2. Issues Covered

3. Is This What Media Firms Need?

DEMAND ANALYSIS

“Nobody knows anything.”William Goldman, Oscar-winning screen-writer for

films including: Butch Cassidy and the Sundance Kid; All the President’s Men;

The Stepford Wives, A Bridge Too Far, The Great Waldo Pepper.

Is Goldman right when it comes to understanding audiences, customers and

buyers? In an absolute sense, yes. We do not know what the users of a new media

product want which is why so many of them fail in the marketplace. But perhaps

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one should define the task more modestly: to be exactly right might be impossible,

but maybe one can increase the probability. To succeed against competitors one

need not be always right - but just a little less wrong than they are. Over time, this

leads to a better track record. And this is the subject of this chapter. How media

and communications firms can improve the assessment of the demand for their

products and services.

Why Demand Analysis

Why is demand analysis important in the media value chain, and why does

every media firm and industry need it?

One of the major characteristics of media companies is the uncertainty and

instability of the demand for their products.

I.2. THE IMPORTANCE AND SPECIAL PROBLEMS OF

DEMAND ESTIMATION FOR MEDIA INDUSTRIES

• Every industry and firm wants to know: Who are the potential buyers?

• What is the buyer’s willingness to pay?

• What is their price sensitivity?

• What product features are valued?

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• What do customers like about competing products?

• How to position the company’s product?

• How to identify promotional effectiveness

• How to identify market segments and select target markets

• What the pricing strategy should be

• How to deploy the sales force

• How to select and manage distribution channels

Demand for any product is difficult to determine. It is easy enough, in a class on

introductory economics, to graph a hypothetical demand curve that shows for each

price the quantity demanded. (Figure 1 ). But in the real world it is hard to

determine the actual nature of demand and the factors that go into it. Which of the

potential demand curves below correspond to the real product of a real firm in a

real market?

 

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“Assume a Demand Curve”

P

Q

But Where Exactly Is It?

 

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Figure 1

Demand analysis is particularly important (and difficult) for media and

information firms. Media has particular elements that distinguish it from other

industries. Recall at the basic economic characteristics of media, discussed earlier.

High Investment Needs And Uncertainty

Media content is expensive to produce, is competitively unique, and has a short

shelf life. Demand estimation is essential to reduce the risk of a project.

Distribution materials are highly expensive.

Long-term planning horizons are crucial. Distribution networks with strong

economics of scale require investment far ahead of actual demand.

HIGH UNCERTAINTY. In media, an “ 80-20 rule” often applies, wherein 80%

of the time the product is not profitable.

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For profits, the odds are longer still. Ten percent of products account for 90% of

the profits. And 2% of products account for 50 % of the profits.

Such performance does not follow a normal distribution but an exponential one. It

can be schematically shown by “Zipf’s Distribution,” exemplified by the equation

y=1/x (Figure 2). Zipf’s distribution shows a demand that is extremely high for a

few products and very low for the many products in the “long tail”. In contrast, the

normal distribution shows a peak.

Figure x.x Zipf’s Distribution for Product Demand vs a Normal Distribution

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One implication of such an exponential distribution is that, in statistical terms, the

average (or mean) audience is much higher than the most probable audience (the

median). Therefore knowing the average performance from the past does not help

in predicting the chances of a new product.

Preferences are unstable. Content suppliers must be able to rapidly respond to

changing audience tastes. Each discrete media product is fairly unique, such as a

film, a book, and as a song. Therefore, a separate demand exists for each of

hundreds of thousands of new products each year. In other cases, products are

intangible and hard to evaluate in advance such as software.

Media products such as broadcast TV or online content have, and are often given

away rather than sold to identifiable users. To monetize this audience, the media

company must be able to identify and quantify it for advertisers. Excess supply.

Each year, thousands of new products enter the market while many of the past

media products remain in the market, too. This leads to price deflation, with

increasing demand on content to be “free”.

Technology Change

For media, technology has a particularly rapid product cycle. Consumers have

often no experience with new products in advance.

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The subjective value of information. Information is an experienced good, but its

value is only determined after consumption. Therefore, it is difficult to determine

consumer references prior to consumption.

Network Effects: The products and services preferences of individuals is

interdependent with that of others. There is a “network effect.” For example,

benefits to users of a web 2.0 website will rise with the numbers of others on the

network. Similarly, the audiences of a TV show often share the experience with

their peers. This leads to extremes of success because users dynamically influence

each other. Think about how Facebook quickly became so popular and how

Friendster declined. When a product or service catches on, it becomes a self-

reinforcing process. Conversely, a product that does not generate such positive

feedback and which loses it, drops out.

Where the average utility of a product increases with the number of other

participants, the demand for it will increase with the number of users. The more

people are on the network or share the experience, the more people are willing to

pay. This leads to an unusual shape of a demand curve. Whereas classically the

number of users drops as prices rise, i.e. the demand curve keeps falling to the

right (in the graph), we now have a situation where, as the number of users rise,

people are willing to pay more because the service becomes more valuable to

them. See Figure x.x

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Figure x.x Demand Curve

For these and other reasons, demand analysis is particularly important in the

media and information field. And it is also particularly difficult!

A. EXAMPLES FOR THE PROBLEMS OF FORECASTING MEDIA

DEMAND

Statisticians speak of “Type I” and “Type II errors”. Type I errors are “false

positives”: the wrong action is taken, mistakenly accepting a positive but wrong

prediction ( “hypothesis”) that demand would be high. This happens all the time.

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Forecasts overestimate the demand for products rather than underestimate6.

In the fields of content and technology eternal optimism governs. But it comes

with frequent and expensive flops. These mistakes can be fatal.

In 2004, DreamWorks grossly over-estimated the DVD sales for “Shrek 2.”

This resulted in retailers returning literally millions of unsold copies. Partly as a

result DreamWorks fell far short of earnings forecasts and was soon sold.13.

The same is true in networks communication services. When videophones

were introduced in 1963 AT&T estimated that there would be 10 million picture

phones in use by U.S. households by 1980. But the real number was closer to zero.

Similarly, at one time, satellite phones were expected to be the hot next thing. The

Wall Street Journal gushed in1998 that “The consensus forecast by media analysts

is of 30 million satellite phone subscribers by 2006.” The reality, however, was

vastly more modest. Today, such phones, aside from some subsidized national

security uses, are mostly used as rental units on cruise ships.

                                                                                                                         6 Carey, John & Elton, Marin. “Forecasting demand for new consumer services: challenges and alternatives.” New Infotainment Technologies in the Home. Demand-Side Perspectives, Lawrence Erlbaum Associates, New Jersey, pp. 35-57. 13 Marr, M. “How DreamWorks misjudged DVD sales of its monster hit,” The Wall Street Journal, May 31, 2005 from Post-Gazette. 15, June 2005. http://www.post-gazette.com/pg/05151/513324.stm

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Type II Errors

Type II errors are “false negatives:” the action should have been taken but was not.

The hypothesis that demand would be high was rejected incorrectly. For example,

in 1877 Western Union, the largest telegraph company in the world believed that

there was no market for a new product: the telephone. Within a few years it was

eclipsed by AT&T, and died a lingering death. AT&T, vastly underestimated the

prospects of mobile phones. In 1981 a McKinsey study for AT&T predicted that

there would be only 900,000 cell phones in use world-wide by the year 2000.

AT&T took the advice and left the field. However, in reality there were more than

one billion cell phones by that year. The company had to spend billions to get

back in.

Such errors abound, even by industry insiders. In 1916 the film star Charlie

Chaplin opined that “The cinema is little more than a fad. What audiences really

want to see is flesh and blood on the stage.18”

And when TV started to be successful, movie mogul Daryl Zanuck, the 20th

Century Fox studio chief, was equally out of touch. “[Television] won’t be able to

hold on to any market it captures after the first six months. People will soon get

                                                                                                                         18 http://data-katalog.com/index.php?newsid=50975

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tired of staring at a plywood box every night.” Computer experts could be equally

un-imaginative. “I think there is a world market for maybe five computers.”

Thomas Watson, Chairman of IBM opined in 1943. A few years later IBM

became the world leader in computer sales. A generation later, the president of the

worlds largest computer maker, Ken Olsen of Digital Equipment Corporation

predicted that “There is no reason anyone would want a computer in their home.”

Bill Gates, founder of Microsoft, predicted in 1992 that “640 kilobytes of memory

should be enough for anybody.”

In this chapter we will investigate ways to reduce the likelihood of such

misjudgements. However, as we proceed with looking into various techniques for

estimating demand, we must also always keep asking the questions: Should media

companies use demand estimation techniques, in the same way as a car

manufacturer or an airline, and then fill this demand? Or shouldn’t media

creations be based on artistic originality, news judgment, and public

responsibility?

There is therefore a great deal of criticism directed towards audience

research as a substitute for a creative judgment.. Garrison Keillor, the noted

American radio performer complains about the “Guys in suits with charts” who

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have changed public radio into an audience-driven enterprise.25 He argues that the

focus on audiences has ruined radio’s “intellectual and moral growth, passion,

variety, and pleasure.”26 Others believe that the entire exercise of demand

estimation is tautological: Isn’t it the case that media creates its own demand, by

influencing people and their preferences? There has long been a debate whether

peoples’ demand is shaping media content, or, to the contrary, whether media

content has been shaping peoples’ demand. Do “powerful media” or “powerful

audiences” determine media content?27 Social science and communications

research have not resolved this question.

The “powerful media” perspective dominates sociological communications

studies and goes back at least to the famed 1930s Frankfurt School of sociology

and its exponents Horkheimer, Adorno, Marcuse, Fromm, and later Habermas,

when the study of modern communications began and developed into a new branch

of social sciences.32

In contrast, evolutionary theorists believe in the “powerful audience” believe with

inherent preferences. The desire for entertainment is inherent in humankind, and                                                                                                                          25 Stavitsky, Alan,“Guys in Suits with Charts: Audience Research in U.S. Public Radio,” Journal of Broadcasting and Electronic Media 39, no. 2 (Spring 1995): 177-189 26 Stavitsky, Alan. “Guys in Suits with Charts: Audience Research in U.S. Public Radio.” , Journal of Broadcasting and Electronic Media 39,,no. 2 (Spring 1995): 177-189 Last accessed on June 30, 2010 at http://www.aranet.com/library/pdf/doc-0088.pdf. 27 Livingstone, Sonia M. , “The Rise and Fall of Audience Research: An Old Story With a New Ending,” Journal of Communication 43, no.4 (Autumn 1993): 5-12 32 Czitrom, Daniel. Media and the American Mind. Chapel Hill: University of North Carolina Press, 1983, p. 122-146.

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not pushed on consumers. The desire for “play” is an intrinsic human character, a

crucial feature for the skill development needed for humans and their survival.34

Children’s’ hide-and seek or dolls are part of acculturation and skill training for

predator evasion, battling adversaries, or rearing children. In adulthood, too,

organized entertainment takes a similar role of “pretend play”, instructing people

about how to behave, and allowing them to gain experience that they can use in

future challenging social and work situations.35

An academic synthesis of the two perspectives is the “Cultural Studies”

approach, where media “texts” are shaped by their creators but are not passively

accepted by the audience, who instead are involved in the “encoding” process of

meaning, which depends on the cultural background of the audience, who form

“Interpretive Communities.”

Economists, whether neo-classical or behavioralists, tend to take demand

behavior as given, deep-seated.

Beyond these deep-seated motivation, media use can have a very practical

foundation.Why do people seek information?

• It increases individual productivity

                                                                                                                         34 Vorderer, Peter & Klimmt, Cristoph & Ritterfeld, Ute. “Enjoyment: At the Heart of Media Entertainment,” Communication Theory 14, no. 4 (November 2004): 388-408 35 Steen, Francis F. & Owens, Stephanie A., “Evolution’s Pedagogy: An Adaptationist Model of Pretense and Entertainment,” Journal of Cognition and Culture 1, no. 4 (2004): 289-321.

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• It is status-enhancing (the knowledgeable individual tends to be

awarded respect)

• Curiosity and a desire to understand how things work

• Participation in the community

• Confirmation of held beliefs from watching content that confirms

their stereotypes39

• Information reduces uncertainty, which can be personally stressful4041

When it comes to entertainment, as distinguished from “information”, the

underlying motivations are:

• Escape from reality

• Relativism

• Mood management – calming as stimulating

• Social experience – sharing, interacting, competing

• Challenge and ploy as survival training

Escapism provides a brief withdrawal from everyday life. There is a desire

for alternative lives, especially those of the rich and famous.42 Or, people may

                                                                                                                         39 Hamilton, David L., & Rose, Terrence, “Illusionary Correlation and the Maintenance of Stereotypic Beliefs, “Journal of Personality and Social Psychology 39, no. 5 (1980): 832-845. 40 Brashers, Dale E. “Communication and Uncertainty Management,” Journal of Communication 51, no. 3 (September 2001): 477-497 41 Sotirovic, Mira, “How Individuals Explain Social Problems: The Influences of Media Use,” Journal of Communication 53, no. 1(March, 2003): 122-137,

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actually like a depressing movie because it makes them feel better about their own

lot in comparison.

Entertainment also enhances one’s well-being by modifying one’s own

stimulus environment. People often desire entertainment to maintain positive

moods. (Zillmann, 1988a, 1988b).44 Persons in states of bad mood such as

boredom may choose arousing stimuli.45 In contrast, people in states of stress

select potentially calming.46

People who seek interactive entertainment such as computer games desire

competition and achievement.47 Entertainment also provides creates an opportunity

to share an experience such as romance or adventure.People choose entertainment

as a means of unconscious learning – for various social and professional

                                                                                                                                                                                                                                                                                                                                                                                                       42 Vorderer, Peter & Klimmt, Christoph & Ritterfeld, Ute , “Enjoyment: At the Heart of Media Entertainment,” Communication Theory 14, no. 4 (November 2004): 388-408 44 Vorderer, Peter & Klimmt, Christoph & Ritterfeld, Ute , “Enjoyment: At the Heart of Media Entertainment,” Communication Theory 14, no. 4 (November 2004): 388-408 45 Zillman, Dolf. (1988). Mood Management: Using Entertainment to Full Advantage. In Donohew,Lewis & Sypher, Howard , & Higgins, E.Tory (Eds.), Communication, social cognition, and affect (pp. 147-171). Hillsdale, NJ: Erlbaum. 46 Zillman, Dofl. (1988). Mood Management: Using Entertainment to Full Advantage. In Donohew,Lewis & Sypher, Howard , & Higgins, E.Tory (Eds.), Communication, social cognition, and affect (pp. 147-171). Hillsdale, NJ: Erlbaum. 47 Vorderer, Peter & Klimmt, Christoph & Ritterfeld, Ute, “Enjoyment: At the Heart of Media Entertainment,” Communication Theory 14, no. 4 (November 2004): 388-408

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situations.49 Children play out various social roles such as hunter, team member, or

care giver.  50

Adolescents watch gore horror movies to demonstrate to their peers mastery

over fear.33 Empathetic people also get gratification from watching sad films.

Feeling sad from tearjerker films may be pleasurable sensations for many

viewers.53 In addition, sharing share grief with a viewing partner serves to

increases intimacy.54

People, especially men, enjoy viewing sports games because of its

suspense.55 It is also a way to stay connected with the experiences of one’s peers.56

Those who believe in inherent media preferences often study it purely empirically,

looking at audience behavior rather than engaging in theories to explain this

behavior. George Gallup, the famed pollster, was among the first to perform

audience preference research. For investigating media audiences, the central figure

was Paul Lazarsfeld. He emigrated to the United States and started an institute at

                                                                                                                         49 Steen, Francis F. & Owens, Stephanie A. , “Evolution’s Pedagogy: An Adaptationist Model of Pretense and Entertainment,” Journal of Cognition and Culture 1, no. 4 (2004): 289-321. 50 Steen, Francis F. & Owens, Stephanie A., “Evolution’s Pedagogy: An Adaptationist Model of Pretense and Entertainment,” Journal of Cognition and Culture 1, no. 4 (2004): 289-321. 53 Oliver, Mary. “Exploring the Paradox of the Enjoyment of Sad Films”, Human Communication Research 19, no. 3 (March 1993): 315. 54 Oliver, Mary. “Exploring the Paradox of the Enjoyment of Sad Films”, Human Communication Research, 19, no. 3 (March 1993): 315. 55 Gan, Su-lin, et al. “The Thrill of a Close Game: Who enjoys it and who does it?”, Journal of Sport & Social Issues 21, No. 1 (February 1997): 53-64. 56 Gan, Su-lin, et al. “The Thrill of a Close Game: Who enjoys it and who does it?”, Journal of Sport & Social Issues 21, No. 1 (February 1997): 53-64.

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Columbia University to research radio audiences.58Lazarsfeld’s statistical

techniques were adopted by media firms and led to what might be called the

“Nielsen Approach,” which centers on the audience. Media firms look at audience

preferences and seek to satisfy them in order to be commercially successful,

For purposes of media management, both major perspectives are correct.

Media audiences have preferences that can be analyzed. This is referred to as

“Media Research.” However, these preferences can also be influenced by means of

“Media Marketing.” This chapter deals with the former. Later we will deal with

“Media Marketing.”

How Media Companies Organize their Demand Research

Large media companies engage in substantial audience research at every

step. Viacom’s research, as described in its Annual Report, focuses on audience

interest in its programs, the effectiveness of expenditures by its advertisers, and the

effectiveness of the company’s own promotion.74

                                                                                                                         58 Czitrom, Daniel. Media and the American Mind. Chapel Hill: University of North Carolina Press, 1938, p. 122-146. 74 Viacom 2006 report [could not find full citation]

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For a new product where the investments and the consequent expenses are great,

media companies need to several distinct types of research.75

1. Concept testing

Evaluating the appeal of an idea or a concept by checking people’s

reactions.

2. Positioning studies

Figure out a target market for the concept.

3. Focus group tests

A sample group is tested to get feedback about the product for its fine

tuning, as it is being produced.

4. Test demonstrations

Previews The product is shown to test audiences to study their

reactions, often to fine-tune marketing strategy. Test screenings are

an example.

5. Tracking surveys

Surveys to measure changes of users and audiences over time.

6. Advertising testing

Measuring the effectiveness of an advertising campaigns.

7. Use surveys

                                                                                                                         75 Marich, Robert Marketing to Moviegoers Burlington, MA : Elsevier Focal Press, 2005

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Surveying the users media experience. Exit surveys are an example.

I.1. CASE DISCUSSION

“Viacom Golden Years Media” (A Hypothetical Case)

– Viacom is one of the world’s largest media companies. It is a major provider

of TV channels offered to cable and satellite video platforms. These cable TV

channels target different audiences. One can arrange some of the channels by

the age or other characteristics of the target audience.

– Noggin (pre-schoolers)

– Nickelodeon (tweens)

– The N (teens)

– MTV, MTV2 (15+)

– mtvU (college, 18+)

– Comedy Central (20+)

– Spike TV (30+)

– Nick at Nite (50+)

– TV Land (50+)

– BET (African Americans)

– Logo (gay)

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Viacom is now considering to add a “Golden Years” channel aimed at a retiree

audience of senior citizens, aged 70+, supplemented by a magazine and an online

site. How would Viacom estimate and measure its audiences, their content

preferences, their consumption preferences (important to potential advertisers) and

their willingness to pay for Viacom’s Golden Years products?

We will analyze Viacom’s options throughout this chapter.

ANALYTICAL AND STATISTICAL MODELS

Media researchers utilize a number of techniques for analyzing demand.

These range from hands-on physiological/medical method which aims to measure

the audience’s physiological response to a media experience.

II.1 PSYCHO-PHYSIOLOGY TESTING

A. Measuring a viewer’s heart rate (HR)76

                                                                                                                         76 Ravaha, Niklas, “Contributions of Psychophysiology to Media Research and Recommendations,” Media Psychology 6, No. 2 (May 2004): 193-235.

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77

B. Electrodermal Activity (EDA): Skin conductance of electricity increases when

sweat increases due to arousal. EDA skin sensors measures responses to various

stimuli such as sudden noise, emotionally charged visuals, pain, arousal, anxiety,

fear, guilt, etc.78

                                                                                                                         77 Niklas Ravaha, “Contributions of Psychophysiology to Media Research: Review and Recommendations, ” MEDIA PSYCHOLOGY, Vol. 6 No. 2, 2004, pp. 193–235. 78 http://www.bsu.edu/web/00t0holtgrav/317/physio.pp#6--source not found

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[137] 79

This graph depicts EDA measures of “before”, “during”, and “after” responses to

an emotional picture and a calm picture.

C. Facial electromyography (EMG): EMG detects the electrical potential

generated by muscle cells when they contract in response to stimuli.80

D. Respiratory sinus arrhythmia irregularity: an index that measures reaction

of the parasympathetic nervous system (PNS), which can be related to emotion.81

The rhythm of the heart is primarily under the control of the vagus nerve, which

inhibits heart rate and the force of contraction. When we inhale, the vagus nerve

activity is impeded and heart rate begins to increase. When we exhale this pattern

is reversed.

                                                                                                                         79 http://web.axelero.hu/lavender/kpt/hallgatokhoz/vassy/weboldal/H7KLFI1.JPG 80 http://www.Wikipedia.org--source not found 81 http://www.biosvyaz.com/Htm_En/Sl_En/Sl02E03.gifhttp://www.biosvyaz.com/Htm_En/Sl_En/s102E03.gif

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E. Electroencephalographic (EEG) Activity: Measures brainwaves using

electrodes.83 Emotions are observed by frontal EEG activity.84

Usually no single psycho-physiological method is enough. Often several

methods are used to identify different responses.86

STATISTICAL INFERENCE:

On the other extreme from the physiological investigation is statistical model

building. It includes:

A. Statistical inference

B. Econometric Modeling

C. Conjoint Analysis

D. Diffusion Models

---------------------------------

There are two ways to measure an audience. One can measure the entire

group we are interested in. An example of this would an analysis of all visitors to a

website or subscribers to a magazine. Alternatively, one could observe a smaller

                                                                                                                         83 http://www.blackwellpublishing.com/abstract.asp?aid=161&iid=4&ref=0956-7976&vid=10--source not found 84 http://www.blackwellpublishing.com/abstract.asp?aid=161&iid=4&ref=0956-7976&vid=10--source not found 86 Ravaha, Niklas Ravaha, “Contributions to Psychophysiology to Media Research: Review and Recommendations,” MEDIA PSYCHOLOGY, Vol. 6 No. 2, 2004, pp. 193-235.

30

group, a “sample”, as a representative of the larger population. This is cheaper,

faster, and more practical. But it may provide incomplete and unrepresentative

coverage.

There are several types of samples. In a simple “random sample” each unit

in the population has an equal chance of being selected.93 A “stratified random

sample” is when one defines sub-groups and selects random samples from each

sub-population group and pools them. A “convenient sample” uses accessible

observations, for example choosing students on-campus for an academic marketing

research study by a professor.94 The “judgment” sample is chosen according to the

estimate of someone familiar with the characteristics of the overall population.95

After choosing a sample and measuring its response, the researchers must

address the question how the sample relates to the overall population. Suppose one

takes three independent samples of 5,000 people from the same population (the

overall US population) and asks all sample groups the same question: Did you

watch last week the “Golden Age Channel”? There is an excellent chance that each

sample will not be quite representative.

                                                                                                                         93 Fridah, Mugo , Sampling in Research, available from http://www.indiana.edu/~educy520/sec5982/week_2/mugo02sampling.pdf [full cite] 94 Fridah, Mugo , Sampling in Research, available from http://www.indiana.edu/~educy520/sec5982/week_2/mugo02sampling.pdf 95 Fridah, Mugo , Sampling in Research, available from http://www.indiana.edu/~educy520/sec5982/week_2/mugo02sampling.pdf

31

Sampling results are likely to differ due to the “luck of the draw.” One would

expect that the three samples would yield similar but not identical estimates. For

example, we might find that the results of Sample 1: p=25%, Sample 2: p=27%,

and Sample 3: p=24%. Which one is right? If we take a lot of samples, we will find

that their results are distributed in a normal distribution. This follows the “Central

Limit Theorem”.

[no source mentioned in the powerpoint]

 

     

Population:  300  Million  people  

Sample  1  

5000  people  

Sample  2  

5000  people  Sample  3  

5000  people  

32

This means that the real proportion of people watching the program will be the

same as the peak of the distribution of the samples, with a lot of sampling. But

frequent and repetitive sampling is not practical. Due to constraints of time and

money researchers typically take one sample and follow up with “inferential

statistics” to state how certain they are of the result. The 65, 95, 99 Percent Rule is

often used. One starts with the center of the distribution. One “standard deviation”

from the center will include 65% of all sampling results, two will include 95%, and

three will include 99%. In other words, 95% of samples will lie within 2 standard

deviations from the observation.

A z-score is a statistical measure of comparing a single data point to a data set that

is normally distributed. It uses the element of standard deviations from the mean

for comparison. A z-score can be calculated using the following formula. z = (X -

µ) / SD,  where z is the z-score, X is the value of the element, µ is the population

mean, and SD is the standard deviation.

A z-score of + 1 or – 1 represents data that is 1 standard deviation greater or lesser

than the mean, . About 68% of data in a normal distribution falls between 1

standard deviation from the mean (z-score between -1 and 1.) About 95% of data

falls between 2 standard deviations from the mean (z-score between -2 and 2.)

About 99% of data falls between 3 standard deviations from the mean (z-score

between -3 and 3.)]

33

Standard deviation tells you how tightly all the various sampling results are

clustered around the means in the set of data. When the examples are pretty tightly

bunched together and the bell-shaped curve is steep, standard deviation is small.

When the examples are spread apart and the bell curve is relatively flat, that tells

us we have a relatively large standard deviation. The following is the standard

deviation formula.

Where X represents each response in the sample and x is the mean of the sample.

For example [provide a numeric example]. If the sample mean is .25 with a

standard deviation of .02, there is a 65% chance that the true population mean is

between .23 and .27 (one standard deviation from .25). There is a 95% chance the

34

population mean is between .21 and .29 (two standard deviation). And a 99%

chance it is between .19 and .31 (three standard deviation).

Case Discussion: How Many Viewers Tuned into the “Golden Years Channel”

Last Week?

Suppose a “Nielsen panel” has 5000 people, of whom 1250 say they

watched at least some of GYC last week. To find the percent watching GYC, one

first finds the results of the sample:

We also need to consider the probability that the sample did not exactly match the

overall population, and get some idea of the precision of our statistical estimate.

We use the following formula to estimate the potential error:

p =

: audience share found in the sample (proportion answered favorably)

p: true audience share in the population

35

e: margin of error; potential error, due to sample being “off”

“p” = proportion that answered favorably

“q” = (1-p) those who answered unfavorably

z-score: indicates how far an item is deviated from its distribution mean

In the above equation for e (the margin of error) only the sample size has any

effect on its magnitude (p and q are results of the survey). Therefore, the larger the

sample size is, the smaller the potential for error.

We determine the variables to be equal to the following:`

e = 1.965000

75.25. × = .012 or 1.2%

p = .25 (25%) of the sample watched

q = .75 (75%) did not watch

n = 5000 (sample size)

z = 1.96 (95%) confidence level, from a table of confidence level and

corresponding z-scores.

36

If we assume there are 100 million TV households in the US, then the number of

American households that watched GYC would be between 23.8M and 26.2M

(25M+/-1.2M) with a 95% certainty.

For Details see Appendix C: Sampling

B. Confidence Intervals

The method of the previous section of using proportions works well for

yes/no type of questions (watched / didn’t watch). However, it does not work well

for continuous types of data, such as determining the average amount of time spent

watching a particular program. For this type of calculation one uses a Confidence

Interval method.

As before, a Confidence Interval method is used for estimating the mean of

the population by finding the mean of the sample and adding/subtracting an

appropriate margin of error. The Confidence Interval equation is as follows:

37

The margin of error (e) is determined by the standard deviation (SD and the

sample size (n). The difference is that instead of a “z score” one now looks up a “t

score,” and that the standard deviation is the numerator.

The t-score is a parameter used to set the desired level of confidence. The t-score

is determined by the level of confidence, e.g., (.90, .95, .98) that is desired, and the

sample size n. It can be found in used tables by inputting values for n and r.

If Nielsen claims its results are within the 95% confidence (r = .95), what

does that mean? It means that if its study was repeated 100 times, then in 95 of the

cases the result would be in the interval.

Case Discussion: Confidence Interval Example

38

Suppose that Nielsen’s audience researchers conducted a poll to determine

how much time Americans spent watching GYC. They interviewed 1320 people

that watched at least some of GYC. Based on the interviews, the average amount

of time spent watching GYC per month was 120 minutes ( X = 120). Nielsen will

need to show the level of confidence of its results.

Now we need to find the values for t and SD. [A240-A242] To determine the value

for t, we first need to choose a value for confidence level r. Suppose we choose r =

.95, which means that the test will be reproducible 95% of the time. Given the

sample size (1320) and the level of confidence desired (95%), from the t-tables, we

find t = 2.086. [Please re-calculate from table for n=1320, not 9000. Maybe 9000

is a typo]

The standard deviation (SD) of the sample is calculated as follows:

39

Assume for this example that the sample’s SD = 50. One can then state, with a

confidence level of .95, that the average time spent by a household watching GYC

was 120 minutes, with a confidence interval of:

[verify arithmetic]

This means that we can assume, with a 95% of certainty, that the people who

watched some of GYC, watched on average between about 117 and 123 minutes of

GYC.

So far, we have used relatively simple statistical tools with a simple variable, such

as yes/no, or a single value such as number of minutes. We can expand this to

analytical methods using multiple variables..

II.3 Econometric Demand Estimation

A major form of statistical analysis is known to economists and business

analysts as ‘econometrics.’ Econometrics is the estimation of statistical relations of

several variables. It typically uses cross-section observations over multiple data

points, or a time series analysis. It allows us to synthesize large amounts of

information. It also provides a framework for systematic thought through explicit

assumptions, which is known as modeling.

40

An example is the explanation of how sales – which is the “dependent”, or

“explained,” or “left-hand variable” are related to several “independent”, or

“explanatory” or “right-hand” variables

Given adequate data, an econometric technique can identify the key

variables that may affect demand, such as price, competition, and advertising

effect, etc. It also uses “control variables” to adjust for factors that might have

affected sales, such as the state of the economy, a growth in population or the

season.99

Unit sales = a + b 1 price + b2 advertising + b i control variables + e

The a and the several b coefficients are parameters that are estimated in the

“regression.” The e is in the unexplained residual, also known as the error term.

Where enough localized or individualized data on buyers are available, one can

also add demographic variables such as age, education and gender. Psychographic

variables can also be added reflecting the buyer’s lifestyle such as their activities,

interests, and opinions (known as AIOs).

There are several issues with regression analysis. First, data is often insufficient

and unreliable. Second, one needs to assume a specific mathematical model for the

                                                                                                                         99 Nagle, Thomas T. & Holden, Reed K. , The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

41

relationship between the variables, for example between price and sale. If there is

only a little historical variation in prices, it cannot reveal the effect of price

changes. Also, the results can only be valid over the range levels for which data

was available. And predicions based on the regression analysis requires the

assumption that future behavior is like that of the past.102 There are other more

technical problems with econometric analysis. They include serial correlation,

multicollinearity, homoscedasticity, lags, and exogeneity103. Consequently, this

raises questions about the results of just about any econometric demand estimation.                                                                                                                          102 Nagle, Thomas T. & Holden, Reed K. , The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 103 Serial Correlation: The correlation of a variable with itself over successive time intervals. Technical analysts use serial correlation to determine how well the past price of a security predicts the future price. also called autocorrelation.( http://www.investorwords.com/4496/serial_correlation.html) Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In this situation the coefficient estimates may change erratically in response to small changes in the model or the data. Multicollinearity does not reduce the predictive power or reliability of the model as a whole; it only affects calculations regarding individual predictors. (http://en.wikipedia.org/wiki/Multicollinearity)

Homoscedasticity- In statistics, a sequence or a vector of random variables is homoscedastic if all random variables in the sequence or vector have the same finite variance. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity. The alternative spelling homoskedasticity or heteroskedasticity is also used frequently. The assumption of homoscedasticity simplifies mathematical and computational treatment. Serious violations in homoscedasticity (assuming a distribution of data is homoscedastic when in actuality it is heteroscedastic) result in overestimating the goodness of fit as measured by the Pearson coefficient. Pronounced /HO mo skee das TI city/. The adjective, 'homoscedastic' is pronounced /HO mo ski DAS tic/. (http://en.wikipedia.org/wiki/Homoscedasticity)

Lags- In statistics and econometrics, a distributed lag model is a model for time series data in which a regression-like equation is used to predict current values of a dependent variable based on both the current values of an explanatory variable and the lagged (past period) values of this explanatory variable. (http://en.wikipedia.org/wiki/Distributed_lag)

Exogeneity- Although several slightly different definitions exist, it is possible to classify two forms of exogeneity- predeterminedness and strict exogeneity. A predetermined variable is one that is independent of contemporaneous and future errors in that equation. A strictly exogenous variable is one that is independent of all contemporaneous, future and past errors in that equation. (Introductory Econometrics and Finance, Chris Brooks, 2nd Edition, June 9 2008, ISBN-10: 052169468X)

42

Is the conclusion just a fluke? Are the results stable over time, and robust to the

addition of other variables? And most of all, can one infer causality? Typically, all

one can claim is a statistical association. For example, the sales of automobiles

may be strongly correlated to the sales of real estate. But do they cause real estate

to fluctuate? More likely is that both are affected by overall economic conditions

Or, the casualty might be the other way, that falling real estate prices lead people to

spend less on new cars. .

The most common form of a regression technique is called Ordinary Least

Squares (OLS). One can estimate OLS regression using readily available statistical

software packages such as STATA, SAS, EXCEL, Minitab, etc.105 OLS estimation

results in parameter estimates for the coefficients b ,0 b 1 , b 2 ,… 1b k that “best” fit the

data. The “best fit” is defined as the loswest sum of the squares of the difference

between the actual value of the data and the value predicted by the equation when

it uses the parameter estimates.

                                                                                                                         105 http://www.chass.utoronto.ca/~murdockj/eco310/F03_310_six.pdf---source not found

43

A statistical measure known as the “R-square” reflects the overall fit of the

model: what percent of the observed variation in the dependent variable is

explained by the independent variables. An R2 above 0.8 would indicate a fairly

good fit of the model.

One can find the Standard Error (s.e.), which indicates how precisely the

coefficient is estimated. s.e. is used in the calculation of test-statistics (t-statistics)

and in constructing confidence intervals. Basically, a t-statistic tests the hypothesis

that the coefficient is (statistically) = 0β (null hypothesis). The formula is: t = (b-

0β )/s.e. b . The rule of thumb when interpreting results is that t-statistics must be

greater than 2 (or less than minus 2) to be a statistically significant difference

(reject null).

Often, relations among variables are not linear but exponential. For

example, higher income may lead to a higher consumption, but not in a linear

44

fashion such as that when one doubles or quadruples income one consumes more

in the same proportions. In such situations a ‘logarithmic’ model allows to

determine exponential growth rates and other non-linear relations.

Sales = a (price)b1 (advertising)b2 (other variables)bi .Here, the coefficients,

b, are the exponents of the variables. To calculate those b’s, one takes the natural

logarithms (ln), which transforms the exponential relation into a linear one.

ln sales =ln a + b1ln price + b2advertising + biln other+u

The coefficients of the logarithmic models are mathematically the “elasticities”, or

the “sensitivity” of the explained or left-hand variable Sales, with respect to

changes in the explanatory, or right-hand, variables of price, advertising, etc.) . 109

The elasticity of sales with respect to price (‘price elasticity’) is defined as the

percentage change in sales as a result of a percentage change in price. This can be

expressed as: .

When demand is said to be elastic, it means that a relatively small difference in

price will affect the quantity demanded considerably. In contrast, where elasticity

                                                                                                                         109 http://www.amosweb.com/cgi-bin/awb_nav.pl?s=wpd&c=dsp&k=elasticity+and+demand+slope

45

is low, it would take a relatively high change in price to make much difference in

the quantity demanded.

Beyond the OLS and the logarithmic models, there are many other

specifications for econometric demand estimation. They include Inverse, Stone-

Geary, Quadratic, Discrete, Dynamic, Inter-temporal, Engel, Log-linear, Semi-log,

Constant Elasticity, Two Stage Least Square, etc.

Let us now look at examples for econometric estimations.

A. Estimation of Demand Curves: Measuring Price Sensitivity

(1) The Demand for Newsprint

This example demonstrates how the initial model and its specification affect

the result and the predictive value of a model. A newspaper company wants to

estimate the demand for the “newsprint” paper which it uses for its daily print

46

run.110 This is of great importance to newspaper companies and commercial

printers who want to evaluate future prices for their most expensive input - paper.

The higher demand is, the higher prices will be. These estimates are also crucial

for paper and forestry companies which must make long-term investments in the

planting and growing of new trees. There are several approaches to forecast

newsprint demand. One model is that of the United Nations’ Food and Agriculture

Organization (FAO), The FAO model is simple. It is based on price, national

income (GDP) and previous consumption. When estimated, the results are as

follows:

Newsprint Consumption = -0.02(newsprint price) + 0.45(GDP per capita) +

0.46(lagged demand of the previous year).111

Thus, since GDP has been rising, demand should also have been rising.112 But in

fact, the demand for newsprint declined after 1987, despite rising GDP, and the

FAO model did not predict this scenario.113

A second model is the “Regional Plan Association (RPA) Model.” The

RPA demand equation is based on several different variables:

                                                                                                                         110 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information technology. 111 Hetemaki, Lauri & Oberstainer, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information Technology. 112 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information technology. 113 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information technology.

47

Newspaper Consumption = -0.22(newsprint price) + 1.23(GDP per capita) +

1.0(population) - 0.02(technological change) - 0.95(print media price index)

+ 0.28(capital price)-0.07(TV/radio price)- 0.06(computer price) +

0.1(demand calibration dummy).114

In this approach, the “print media price index” shows other input prices in the

print industry, which affects the printing and publishing costs, and in turn, overall

newsprint demand.115 The price variables of TVs, radios, and computers reflect the

potential substitution impacts of electronic media. Technological change represents

innovations in products.116 The “demand calibration dummy” adjusts for the the

recession in the US economy.117

Here is the comparison of how the FAO and RPA models described the past

and projected the future.

                                                                                                                         114 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information Technology. 115 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information technology. 116 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information Technology. 117 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information Technology.

48

118

An alternative econometric approach is the “Bayesian Model.” It

incorporates other information (e.g. subjective expert knowledge) into econometric

forecasting models. In deriving the “Bayesian prior,” experts are asked to give

quantitative responses to three factors of the US newsprint market122:

1. Economic and lifestyle development

2. The trend from paper media to electronic media

3. Future changes in the weight and size of newspapers

After the first scenarios are created, they are discussed and improved by the

participants. The process continues until the experts come to a consensus on the

ideal scenario.

                                                                                                                         118 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information technology. 122 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information Technology.

49

A third approach is the Newspaper Circulation Model (NCM). Basically, it

refers to newspaper circulation rather than to prices of economic variables to

explain changes in the newsprint market. The equation for this model is:

ttnewstnewstnews dcircd µγγγ ++Δ+= − )ln()ln()ln( 1,2,10,

Where(d tnews , ) = the quantity of newsprint consumption in the US in year t.

Δ (Circ tnews , ) = the change in the volume of newspaper circulation.

d tnews , = the “lagged” dependent variable, reflecting the demand of the previous

year.

tµ = the error term.125

Since 1987, there has been a decline in the volume of newspaper circulation

(Circnews,t).126 A 1% increase in newspaper circulation (∆Circnews,t) would lead to a

very large increase (3.1%) in demand for newsprint.

127                                                                                                                          125 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information Technology. 126 Hetemäki, Lauri & Obersteiner, Michael , “US Newsprint Demand Forecasts to 2020,” supported by Fisher Center for the Strategic Use of Information technology.

50

128

In conclusion, both GDP and newsprint price proved to be insignificant

determinants of demand for news print.131

132

(2) The Demand for Live Entertainment

A second example for econometric demand estimation is to try to determine

what affects people’s demand for live entertainment. A researcher tried this for the

English city of Leeds, looking for the impact of a variety of factors in explaining

the frequency of attendance of live entertainment events. 133 These variables

included individuals hours of television watched per week, hours of radio watched

per week, the number of people in a party for an evening out, the idea of a

                                                                                                                                                                                                                                                                                                                                                                                                       127 Hetemäki, Lauri & Obersteiner, Michael , US Newsprint Demand Forecasts to 2020 128 Hetemäki, Lauri & Obersteiner, Michael , US Newsprint Demand Forecasts to 2020, p. 30 131 Hetemäki, Lauri & Obersteiner, Michael , US Newsprint Demand Forecasts to 2020, p. 32. 132 Hetemäki, Lauri & Obersteiner, Michael , US Newsprint Demand Forecasts to 2020, p. 30 133 Cameron, Samuel. “Determinants of the Demand for Live Entertainment: some survey-based evidence.” Economic Issues 11, no. 2 (2006): 51-64.

51

reasonable price of a ticket for an evening out, gender, income, employment, age

and education.

Demand for Live EntertainmentsDependent Variable = 1 If attend > 12 or more events per year; 0 otherwise. Estimation method: ML

Variable Coefficient Standard  Error

LEEDS  (dummy =  1  for  Leeds) -­‐.940 1.405

TVHRS  (hours  of  TV watched  per  week) .036 .032

RADIOHRS  (hours  of  radio  watched  per  week) -­‐.009 .022

ALONE  (dummy=1 if  regularly  attends  events  alone) -­‐.515 1.616

NUMPARTY  (number  of  people  in  a  party  for  an  evening  out) .076 .108

URGE  (maximum  price  would  ever  pay  for  a  ticket    divided by  RSNPRICE)x100

-­‐.005 .005

RSNPRICE  (idea  of  a  reasonable  price  for  a  ticket  for  an  evening  out) -­‐.172 .100

FEMALE  (dummy=1 if  female) -­‐17.915 7.928

SINGLE  (currently  single) 1.658 1.355

GROSSINC  (gross  income  of family  unit) .000 .000

NOCCUP  (no  current  occupation) -­‐.611 1.300

DEGPLUS  (highest  qualification  is  a  degree) -­‐.351 .875

AGE -­‐.272 .158

AGESQ .003 .002

The study finds that, interestingly, income effects were not noticeable

(GROSSINC coefficient = 0.000). The coefficient for price (RSNPRICE = -172)

was negative. The study showed it to be positive for femails, however.136 Age did

not have a significant effect either – as people get older they may go less to rock

                                                                                                                         136 Cameron, Samuel. “Determinants of the Demand for Live Entertainment: some survey-based evidence.” Economic Issues 11, no. 2 (2006): 51-64.

52

concerts but may go more to operas.137 Events attendance are significantly

correlated to the hours of TV watched every week, but for female participants

only.138 [

(3) The Effects of the General Economy on Advertising Volume

A third example seeks to estimate the effects of the overall economy on

advertising volume. This is of great importance to publishers, networks, and

advertising agencies. In the past, advertising has been closely related to economic

growth.

140

                                                                                                                         137 Cameron, Samuel. “Determinants of the Demand for Live Entertainment: some survey-based evidence.” Economic Issues 11, no. 2 (2006): 51-64. 138 Cameron, Samuel. “Determinants of the Demand for Live Entertainment: some survey-based evidence.” Economic Issues 11, no. 2 (2006): 62 140 Quarterly Survey of Advertising Expenditure 2002 (Advertising Association, AC Nielsen MMS, and WARC), Economic Trends ONS

53

An econometric study of eight major countries found that advertising

spending declined by 5% for each 1% reduction in GDP.141 A strong correlation

between the economy and advertising was found for Germany, Spain, Italy, and

Finland. Only a moderate correlation was found in the UK and France, and a low

correlation in Japan where advertising spending was least affected. Among

different media, print media was most affected by changes in GDP. On average,

there was a 15% decline for a 1% decline in GDP. In the US a lower effect was

observed for newspaper (decline 5.5%), and for magazines (decline by 2.5%).

Electronic media was less affected by GDP. A 1% decline of GDP caused

advertising spend on TV to decline by 4% (3% in the U.S.) and that for radio to

decline by 8% (2.5% in the U.S.).

(4) Demand for Video Games

Nintendo and Sega compete in the home video game market. Both They face

a demand for their products that is determined by both firms’ current prices and of

their advertising expenditures.142 The demand model for each firm is:

                                                                                                                         141 Picard, R. “Effects of Recessions on Advertising Expenditures: An Exploratory Study of Economic Downturns in Nine Developed Nations”, Journal of Media Economics 14, no. 1 (2001): 1-14 :142 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 375-384. Published online 18 November 2002 in Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296.

54

Qit – firms i’s demand at time t

Pit – firm i’s price at time t

Ait – firm i’s advertising expenditures at time t

α - parameter for brand specific effects

η and ß – own price and advertising elasticities

ε and γ- cross-price and cross-advertising elasticities

144

The parameters are as follows:

145

                                                                                                                         144 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 375-384. Published online 18 November 2002 in0.0 Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296. 145 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 375-384. Published online 18 November 2002 in Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296.

55

The study indicates network effects are a function of network size, such as

the customer base, and network strength (the marginal impact of a unit increase in

network size on demand.146)

This study also finds that the firm with a smaller customer base (Nintendo)

has larger network strength than the firm having a larger customer base such as

Sega.147 The study finds that Sega’s price sensitivity is relatively smaller than

Nintendo’s (Sega’s coefficient estimate for price-network size interactive effect is

0.06, compared to Nintendo’s 0.10.149), This is probably so because customers are

more willing to pay more for a product with a larger network of users.150 Similarly,

Sega’s advertising seems to be more effective compared to that of Nintendo,

because the company can maintain its demand with less advertising

expenditures.151

152

                                                                                                                         146 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 375. Published online 18 November 2002 in Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296 147 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 375. Published online 18 November 2002 in Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296 149 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 381. Published online 18 November 2002 in Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296 150 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 375-384. Published online 18 November 2002 in Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296. 151 Shankar, Venkatesh & Bayus, Barry L. . “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry”, Strategic Management Journal 24, no. 4 (April 2003): 375-384. Published online 18 November 2002 in Wiley InterScience (www.interscience.wiley.com): DOI:10.1002/smj.296. 152 Hetemäki, Lauri & Obersteiner, Michael , US Newsprint Demand Forecasts to 2020

56

(5) Modeling Film Box Office

A fifth example of the use of econometrics is an estimation of a film’s box

office revenues. One can estimate a film’s anticipated revenues based on a variety

of factors, such as the performance of previous movies belonging to the same

genre, the track record of the same actors and directors, the previews, etc. There

are various computer models designed to make predictions based on data analyses

rather than “gut” feeling. The past relations of the variables are used to predict

future outcomes. Examples are the Motion Picture Intelligencer (MPI)154 and

MOVIEMOD.

MPI is used as a tool to help strategy based on the ticket-buying behaviors of

past movies.155 MIP factors in advertising expenditures, the number of theaters

used in a release, the time of the year of the release, and any foreseeable

competition from other movies. It

In contrast, the MOVIEMOD model needs no actual sales data, but uses data

from focus groups.156 Subjects are exposed to different sets of information stimuli

                                                                                                                         154 Wood, Daniel B. “Can Computer Help Hollywood Pick Hits?”, Christian Science Monitor 89, no. 27 (January 3, 1997): 1 155 Wood, Daniel B. “Can Computer Help Hollywood Pick Hits?”, Christian Science Monitor 89, no. 27 (January 3, 1997): 1 156 Eliashberg, Jehoshua & Jonker, Jedid-jah & Sawhney, Mohanbir S. & Wierenga, Berend. “MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures.” Marketing Science 19, no. 3 (Summer 2000): 226-243

57

and are shown the movie. They then fill out post-movie evaluations, including their

word-of-mouth intentions.157

In a Dutch application of MOVIEMOD, managers used the model to

identify a final plan that allegedly resulted in an almost 50% increase in the test

movie’s revenue performance. The box-office sales were within 5% of the

MOVIEMOD prediction.160

Analysts can also use these models to predict which movie scripts will be

hits and which will be flops.161 But the equations, methods, and data behind the

models are proprietary and undisclosed,162 and their success rate is murky. An

occasional bulls-eye prediction will be touted, but it may be purely by chance.

This is not to say, however, that modeling will not raise the probabilities of

predictions somewhat.

Another forecasting model to predict movie admissions is ARIMA. Its

formula is as follows:

LB Test:

                                                                                                                         157 Eliashberg, Jehoshua & Jonker, Jedid-jah & Sawhney, Mohanbir S. & Wierenga, Berend. “MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures.” Marketing Science 19, no. 3 (Summer 2000): 226-243 160 Eliashberg, Jehoshua & Jonker, Jedid-jah & Sawhney, Mohanbir S. & Wierenga, Berend. “MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures.” Marketing Science 19, no. 3 (Summer 2000): 226-243 161 “Part V: Can Computer Models Help Select Better Movie Scripts?”, in Revenge of Nerds, Knowledge @ Wharton. University of Pennsylvania, 29 November 2006 162 “Part V: Can Computer Models Help Select Better Movie Scripts?”, in Revenge of Nerds, Knowledge @ Wharton. University of Pennsylvania, 29 November 2006

58

€

LB = n n + 2( ) Pk2

n − k⎛

⎝ ⎜

⎞

⎠ ⎟

k=1

k

∑

n = # of observations

k = lag length

pk2 = sample autocorrelation coefficent

The ARIMA model allows the data to “speak for themselves” and does not

require the need to identify each and every factor that influences the dependent

variable.

Even if the performance of the individual films cannot be forecasted due to

the exponential distribution of box-office success, the level of total cinema

admissions can still be estimated, at least in the short term, based on economic and

competitive technology176 If a distributor has enough films in its portfolio that

average out individual flops and hits, it can predict its overall level of sales.

Although films are valuable in the distribution of their renewals, annual overall

audiences vary much less.

                                                                                                                         176 Hand, Chris , “The Distribution and Predictability of Cinema Admissions,” Journal of Cultural Economics 26 (2002): 53-64

59

Case Discussion: How can Viacom use econometric techniques to estimate the

demand for its Golden Years Channel? A simple demand model could be specified by Viacom:

The likelihood of watching the Golden Years Channel = a + β 1 ln age of viewers

+ β 2 ln income of viewers + β 3 ln education of viewers + γ 1adventure shows +

γ 2 romance shows + γ 3 sports shows + γ 4 documentaries/news shows+

y 1primetime + y 2 daytime + y 3 late night + f iother factors + u

The coefficients that are estimated are:

β 1 = own-price elasticities to age, income, education

γ = cross elasticity to other types of channels

Some of the “other factors” could be dummy variables for yes/no for

sociodemographic factors such as “rural location,” “Latino” or “living single.”

Measuring the price elasticity of demand: this is discussed in detail in the Chapter

on “Pricing.”

II.4 CONJOINT ANALYSIS

Conjoint analysis methodology is a standard market research tool regularly

used since 1971. Developed initially by Paul E. Green and Vithala R. Rao 178,

                                                                                                                         178 Green, Paul E. & Rao, Vithala R., “Conjoint Measurement for Quantifying Judgmental Data”, Journal of Marketing Research 8, no. 3 (August, 1971): 355-363

60

conjoint analysis is used to measure the trade-offs people make in choosing among

products and services.181

The foundation of this technique is the assumption that a product can be

disaggregated into individual attributes. For example, a TV set has attributes such

as size, price, model, style, etc.183

In a conjoint analysis the respondent is asked to make choices between different

levels of two product attributes.186 This enables the researcher to identify the value

(utility) that a consumer attaches to each product attribute. 187 The questionnaires

data are then subjected to a statistical analysis. The utility is a measurement of a

consumer’s relative strength of preference for each level of each product attribute.

There are index numbers that measure how valuable or desirable a particular

feature is to the respondent.188 The value of a product is equal to the sum of the

utility the consumers derive from all the attributes of the product.189 This enables

the researcher to predict the prices which the consumer would pay for a product of

various combinations of attributes. It helps identify the combination of attributes

                                                                                                                         181 P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 183 P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 186 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 187 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 188 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 189 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php

61

to price and target buyers. It can also be done before the product is developed.190 It

works best for products that are evaluated by consumers based on their attributes

rather than products chosen based on their “image” (e.g. beer or cigarettes).191

Trade-off analysis does not simulate the actual purchase experience. The consumer

is encouraged to put much more attention on specific product attributes than in a

real life situation.192

The first step is to collect data with an interaction questionnaire. Participants

are asked to respond to different product attributes. The questionnaire selects

several product attributes and their levels in order to test a variety of product

combinations.

A sample question is as follows: if two cable channels were the same in

every way, how important would the difference between two features shown below

be to you?

10% more than you’d expect to pay

vs.

10% less than you’d expect to pay

ο ο ο ο ο                                                                                                                          190 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 191 P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 192 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

62

Extremely Extremely

Unimportant Important

194

In the last questionnaire step, participants are given profileswith all the examined

attributes of the test product or service, and are asked to express the likelihood they

would purchase it on a 100 point scale.195

This statistical analysis is calculation intensive and requires a computer

program. One computer package is Adaptive Conjoint Analysis (ACA), which

generates an optimal set of trade-off tasks for each participant.

The following example about an MP3 Player is an example of how conjoint

analysis attributes importance to products. It is based on a scale of 1 to 10.207

Attribute:

Quality: 8.24

Styling: 6.11

                                                                                                                         194 DBA POPULUS http://www.populus.com/techpapers/conjoint.php 195 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 207 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

63

Price: 2.67

User Friendliness: 7.84

Battery Life: 4.20

Customer Service: 5.66

Other attributes include sound quality, size and weight, and storage capacity.

The following chart shows a product attribute levels and how this produces a

higher degree of acceptance likelihood.209

210

Acceptance likelihood is calculated by adding up the sums of the attribute level

utilities contained in the product profile.211

                                                                                                                         209 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 210 P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 211 P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php

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Case Discussion: How could Viacom make use of conjoint analysis for its

“Golden Years” Channel? It may ask a sample of people aged 65+ which package they would value.

These packages vary along the following attributes:

• Price of GY channel (from $1 to $4)

• Movies shown on the GYC (frequency from 1 to 4) Golden Magazine free

added (yes/no)

• Other channels on cable system (from 10 to 40)

The following table shows the importance of each attribute in the Golden Years

channel package:

213

                                                                                                                         213 Source: Allison, N. & Bauld, S. & Crane, M. & Frost, L. & Pilon, T. & Pinnell, J. & Srivastava, R.& Wittink, D. & Zandan, P., Conjoint Analysis: A Guide for Designing and Interpreting Conjoint Studies, Chicago, IL: American Marketing Association, 1992Allison et al. (1992), Conjoint analysis, American Marketing Association

65

214

Participants are asked to choose from pairs of package attributes. Each profile

describes 2-4 attributes. Participants are asked which of two profiles they prefer

more.215 Utilities are then calculated by a statistical program.216 The overall utility

is calculated for each package.

Respondent’s utilities for selected GYC packages

Package Configuration Utilities Overall Utility

Nr. Other Channels

Free Golden Age Magazine

Movie Aired Frequency Price

1 40 channels Yes 2 per day $4

.471 + .769 + .271 + .035= 1.546

2 40 channels No 3 per day $2

.471 + .231 + .311 + 2.17= 1.23

3 30 channels Yes 1 per day $2

.403 + .769 + .103 + .217= 1.492

                                                                                                                         214 Source: According to P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 215 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 216 Kotler, Philip, Marketing Management, Prentice Hall, 1997

66

4 30 channels No 4 per day $2

.403 + .231 + .315 + .217= 1.166

5 20 channels Yes 4 per day $1

.125 + .769 + .315 + .738= 1.947

6 20 channels No 3 per day $1

.125 + .231 + .311 + .738= 1.405

7 10 channels Yes 2 per day $1

.001 + .769 + .271 + .738= 1.779

8 10 channels No 3 per day $1

.001 + .231 + .311 + .738= 1.281

217

The first package would have been the most attractive in terms of content,

but the price of the channel is set too high.218 The configuration package number 5

has the lowest price, 20 extra channels, the Golden Age magazine, and a movie

frequency of 3 per day. It is the most preferred by the senior consumer.219

II.5 DIFFUSION MODELS

Generally, adoption of a new product follows an S-curve pattern.

                                                                                                                         217 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 218 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 219 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php

67

The S-curve helps to illustrate and predict how a new product will be accepted by

the population. The S-shaped curve of adoption rises slowly at first when there are

few adopters. The general formula of the S-curve is:

Cumulative Sales =

€

a1+ be−kt

Where t is time and a, b and k are constants 220

This is called a “logistic” function or an “epidemic model.” The product will

spread like a “virus” epidemic.222 Examples for diffusion are the adoption of Blu-

Ray DVD, or the audiences word-of-mouth of a hit movie. Another example is the

                                                                                                                         220 McBurney, Peter & Parsons, Simon & Green, Jeremy, “Forecasting market demand for new telecommunications services: an introduction.” Telematics and Informatics 19, no. 3 (2002): 225-249. 222 Wilson, Ralph. “The Six Simple Principles of Viral Marketing.” WilsonWeb. 1 February 2005. Last Accessed on 31 May 2007 at http://www.wilsonweb.com/wmt5/viral-principles.htm.

68

use in a Chinese research center to analyze the diffusion of Internet Protocol

version 4 (IPv4)223.

y(t) = N{1+0 exp [-kt]}

Different S-shapes occur with different parameters. One has to determine,

from early data, what the parameters are, for a projection of the rest of the S-curve.

Forecasting a product’s demand is not easy. It is difficult to find the acceleration

point, and to find the “saturation level,”224 tone must compare the product to be

forecast with an earlier and hopefully similar product.

                                                                                                                         223 Guo, Jin li, “S-curve networks and a new method for estimating degree distributions of complex networks”, http://arxiv.org/pdf/1005.2122 224 Carey, John & Elton, Marin, “Forecasting demand for new consumer services: challenges and alternatives.” New Infotainment Technologies in the Home: Demand-Side Perspectives, New Jersey: Lawrence Erlbaum Associates, pp35-57

69

The Historical Diffusion Index (HDI) explains technology’s diffusion

indexed on its predecessors.

HDI = !"#"$%&$'(#  !×!""!"#"$%&$'(#  !

For example, can the diffusion of DVD players be compared to the diffusion

of VHS players 15 years earlier? VHS was in 95% of US households in 2008,

which was its Maximum Market Demand. DVD was in 75% of households in

2008. This means the HDI225 = (75*100)/95 = 79%. Thus, the DVD market is still

21% below its potential. Second, the VCR reached a 75% penetration after twelve

years, while DVD took only six years. Hence the DVD penetration rate has been

two times faster than that of VCR. Since the VCR took four years to rise from 75%

to 95%, the DVD is likely to take only 4/2=2 years to reach 90%. One can make

similar comparisons with DVDand Blu-Ray DVD. But maybe consumers do not

value high-definition much over standard-definition quality, and both diffusion

speed and maximum market demand may be lower.

Thus, the problem with the diffusion approach is that there are many

differentiating variables and they make comparisons among products unreliable.

Case Discussion: How would “Golden Years” estimate and measure its

audience?                                                                                                                          225 The Historical Diffusion Index explains in this context a technology’s diffusion indexed on its predecessors’.

70

One approach would be to first look at the potential audience. First, one would

decompose the population by age and age of cohorts size.

Second, one would look at the viewing behavior of different age cohorts, by

factoring in the viewing of each cohort by hours per week and (???) it with the size

of each cohort..

This would show

71

Advertisers value age cohorts differently. Younger audiences are preferred because

they have less rigid consumption routines, greater susceptibility to advertising and

offer a longer payback period for investment in customer acquisition.

This means that the hours of TV need to be computed by three value to advertisers.

Modeling the Market (IV): Value of TV Hours to Advertising by Cohort (CPM x#

of ads x# of hours)

72

Fifth, commercials need to be placed in to this audience value distribution. Each

channel has a peak age cohort A where it is viewed the most. Audiences decline at

a rate B away from the peak cohort. The media firm can control A and B through

programming decisions. C is the size of the audience, and is a function of A, B,

and the presence of other channels.

Modeling the Market (V): Competitor Analysis

Modeling the Market (VI): Analysis of Under-Served Niches

73

Sixth, one identifies the potential market niches in the above chart. Look for:

A. No domination by a strong brand, identifiable by a low peak of the audience

triangle (in one example, for example, The History Channel has a low peak while

Nickelodeon has a high peak.)

B. Distance of competitors from target cohort. For example, the space right of 55

years and older to be targeted.

Modeling the Market (VII): Estimating Market Shares

A company must make assumptions of its market share. For example, competitors

that target the same cohort share that cohort equally. But the share declines with

distance from the cohort. The audience for a channel depends on its positioning of

its peak at cohort i, with other channels j in the market. For each cohort, its share is

determines by the distance of that cohort from its peak audience cohort.

Management Decision Process

To optimize Revenues T, choose a combination of target peak audience

cohort i, and the extent of audience specialization (coefficient b). How steeply

peaked will the audience triangle be? An estimation model will make it possible to

check out multiple niches, find the optimal niche, and therefore the optimal

specialization.

74

The important point is to think systematically and break down the question

of channel strategy into smaller elements. This is what analytical or statistical

modeling is about interpreting data.

Good analysis requires good data and its interpretations. This is the next

topic: Getting the Data.

III. DATA COLLECTION

III.1 COLLECTION METHODS

There are numerous ways to collect data. They include:

A. Personal Interviews

B. Mail Surveys

C. Phone Surveys

D. Focus Groups

E. Psycho-Physiology Testing

F. Test Marketing

G. Internet Surveys

H. Retailer Surveys

I. Conjoint Analysis

J. Delphi Surveys

75

K. Trendsetters & Opinion Leaders

L. Automatic Metering

A. Personal Interviews

Personal surveys are usually conducted by market research firms such as Simmons,

Dun & Bradstreet, Arbitron, NFO, or Gallup Horizon Research. There are similar

organizations around the world.

Personal interviews present both pros and cons. While interviews can be in-

depth, they are also expensive and need a reliable survey team. The sample is often

biased by a self-selection of subjects who agree to participate, and by a

convenience factor of accessibility to the subjects Follow-up research is time-

consuming.

Another problem with personal surveys is the truthfulness of responses.

Basically people will dissemble about their incomes, their taste, and their actual

consumption patterns. They can be forgetful.226 There is an “interviewer effect,” in

which the age, gender, attractiveness, status, intonation, gestures, etc. of the

interviewers have an impact on responses.227

                                                                                                                         226 Underwood, Mick , The Communication Studies Project, “Audience Measurement”—Source not found. 227 Underwood, Mick , The Communication Studies Project, “Audience Measurement”---Source not found.

76

Direct questioning may lead consumers to state a price, but it’s typically a

lower price than they would actually pay, part of a subconscious bargaining

behavior, like an early offer. On the other hand, sometimes they may state a higher

price to please the interviewer.228

B. Mail Surveys

Mail surveys are low-cost, and their greater anonymity increases candor.

However, their low response rates lead again to self-selection bias, and researchers

cannot probe or clarify answers . Mail are often used for new magazine concepts,

even before the magazine is actually published, in order to validate the concept and

to get feedback on price and features. For instance, Vogue regularly conducts

surveys for its editorial concepts and advertisement229.

In a Direct Mail Test Grid, several variables can be tested including the text

of the promotions, the price, and the type of payment 230(e.g. one year

subscriptions, trial subscriptions, etc.) Different permutations of various copy

approaches, prices, payments, and mailing lists can be created; responses are

received and then analyzed.231

Example: Test Variable for Magazine                                                                                                                          228 Nagle, Thomas T. & Holden, Reed K. , “The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 229 http://www.vogue.com.cn/ 230 Kobak, James. “Testing a New Magazine Through Direct Mail,” How to Start a Magazine-source not found. 231 Kobak, James. “Testing a New Magazine Through Direct Mail,” How to Start a Magazine-source not found.

77

For example, by comparing the acceptance rates for the two different prices,

with the other variables held constant, and the survey sent to very similar mailing

lists, one may find that the response to the price at $17 was only 10.2% less than

the response rate at a price of $15.233 The price elasticity is hence η = -10.2/ 2/17 =

-.87. This means that the price sensitivity is slightly elastic and that the lower price

point raises a somewhat higher revenue.

Similarly, one can determine the percent difference between the acceptance rates

for different payment offers (pre-requirement or trial subscription, etc.) with all

other factors held constant.

Market research companies also provide socio-demographic information

about every postal code.237 Combining test results of mail surveys with the

demographic characteristics of the responders postal code helps a magazine to

determine best target area code, and which characteristics to focus on such as

income, race, gender, or age.238

C. Telephone Surveys

Telephone surveys are cheap, and allow follow-up questions and clarifications.

They are not truly anonymous, since the interviewer is in possession of the phone

                                                                                                                         233 Kobak, James. “Testing a New Magazine Through Direct Mail,” How to Start a Magazine-source not found. 237 Kobak, James. “Testing a New Magazine Through Direct Mail,” How to Start a Magazine 238 Kobak, James. “Testing a New Magazine Through Direct Mail,” How to Start a Magazine

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number and could track the name and address of respondents. Here, too, there is a

self-selective bias. In poorer countries, many households have no residential

phone.

Example: Telephone survey for office software

A software firm developed a product for law firms that manage storage and

billing for legal documents.239 In a random sample, 603 attorneys were each

contacted by phone and asked for the likelihood of purchase at $2,000, $4,000,

$6,000, or $8,000. There were about 150 responses per price point.240 The firm’s

original intended price was $500, but surveys showed that even at $2,000, 49% of

the firms said they would have bought the package. Demand was thus found to be

highly inelastic at high prices (see Figure A).241

                                                                                                                         239 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 240 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 241 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

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242

The price increase from $4,000 to $8,000 did not significantly alter the proportion

of law firms willing to buy, but raised sale revenue substantially.

243

Based on those survey figures, what should the firm charge? The

preliminary conclusion is that the firm should charge $8,000 but also try to have a

lower-quality product at about $4,000. However, this assumes no competition other

wise, the prices of competing products will be constraint. A firm can’t get away

                                                                                                                         242 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 243 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

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with charging $8,000 if a competitor offers a similar product at $500. Still, the

willingness to pay is revealed in this survey.244

D. Focus Groups

A fourth type of surveys is to recruit a group and uses it collectively for

depth interviews. The demographic makeup is either random or selected.245 Test

audiences are often used for film, in advance of release. There are two types of

such film “previews”: for production and for marketing. Production previews help

filmmakers fine-tune the movie while it is being made, whereas marketing

previews study an audience’s reactions to complete films and assess marketing

strategy.246 This approach is often used for big budget films. In China, for example,

the film “Aftershock,” (an Imax production) used test audiences to gauge likely

public reaction.248

Many popular movies have been altered after viewing by test audiences.

Originally, Glen Close’s character in “Fatal Attraction” as the vindictive spurned

woman survived but audiences hated her, and the ending was therefore changed to

                                                                                                                         244 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 245 Friedman, Robert. “Motion Picture Marketing”, in Squire, Jason. Ed. The Movie Business Book, Third Edition, Fireside 2004, pp. 282-298. 246 Friedman, Robert. “Motion Picture Marketing”, in Squire, Jason. Ed. The Movie Business Book, Third Edition, Fireside 2004, pp. 282-298. 248 Landreth, Jonathan. “'Aftershock' to rattle China exhibitors, filmgoers”, Hollywood Reporter, July 21, 2010. http://www.hollywoodreporter.com/hr/content_display/world/news/e3i4c15c030a696fa1460ec4b5248fc0c28

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see her die.249 In the movie, ET, the lovable alien space traveler character

originally died before test audiences rescued him and sent him back to his galaxy.

Rather than getting home In “Pretty Woman”, Julia Roberts initially rejected

Richard Gere 250 The audience insisted on a happy ending that was tacked on.

Thankfully, test audiences do not always prevail. “Wizard of Oz” test audiences

complained that “Somewhere over the Rainbow” slowed down the movie, but the

song stayed anyway and became a classic.251

Director Ron Howard describes the director’s perspective on test audiences.

“It’s much easier to embrace the whole testing process when you know that you

ultimately control the final cut on your movies. But it’s frightening if you’re in a

position where you’re going to show the movie at a preview and somebody else

(i.e. the studio) is going to take the results of that preview re-cut the film based on

that, maybe consulting you or maybe not. That’s terrifying.”252

The actual testing usually done by specialists with no particular axe to grind,

National Research Group (NRG) is a film testing company for Hollywood,

specializing in test screening.

                                                                                                                         249 Bay, Willow. “What if ET died? Test audiences have profound effect on movies.” Entertainment Weekly. 28 September 1998 250 Bay, Willow. “What if ET died? Test audiences have profound effect on movies.” Entertainment Weekly. 28 September 1998 251 Bay, Willow. “What if ET died? Test audiences have profound effect on movies.” Entertainment Weekly. 28 September 1998 252 Bay, Willow. “What if ET died? Test audiences have profound effect on movies.” Entertainment Weekly. 28 September 1998

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Often, such screenings use audience perception analyzers, little hand-held

transmitters that resemble TV remote controls.253 They are linked to software and

hardware that records, tabulates, and analyzes responses and their intensity.254

E. Using the Internet as a Survey Tool

The internet is an excellent tool to communicate and survey. Viacom’s

children’s cable channel, Nickelodeon, for example, uses the internet as a survey

tool. Before production of a new version of the TV series “Rugrats” began,

Viacom quizzed children and parents about what they wanted.255 Similarly, Elle,

the well-known fashion magazine, conducts regular online surveys each month in

China, and offered samples of cosmetics to its readers in return256.

There are several approaches to take internet measurement. In 1995, the

company Media Metrix installed the first meter of internet use, the “PC Meter.”257.

The data meter requires user cooperation. Incentives were therefore offered to

users who were willing to use the browser.259

                                                                                                                         253 Smith, Denise, “Instant Analysis Technology Helps Rate Commercials” St. Louis Post – Dispatch, June 3, 1996. pg. 03 254 Smith, Denise, “Instant Analysis Technology Helps Rate Commercials” St. Louis Post – Dispatch, June 3, 1996. pg. 03 255 King, Tom, “Hollywood Journal: Nickelodeon Comes of Age – At 20, Nick Woos Big Stars, Takes on Old Studios; Building a Better ‘Rugrat’” Wall Street Journal. Dec 1, 2000. pg. W.8 256 http://www.365diaocha.com/answer/survey/displayPage.jsp?projectId=3373 257 Coffey, Steve., “Internet Audience Measurement: A Practitioner’s View”, Journal of Interactive Advertising 1, no. 2 (Spring 2001): 11

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To automatize the process, of tracking internet traffic on the user level

“cookies” are a major tool used. Cookies are electronic files to tag individual users

with a unique identification. It allows websites to recognize individuals.261

Another technique is mouse activity measurement. Three main mouse

activities are measured: number of clicks, time spent moving the mouse in

milliseconds, and time spent scrolling.262

Internet surveys have their own pros and cons. As in mail surveys, self-

selection can create bias errors, and respondents may have to install special

software.263

We will discuss internet tracking further below.

F. Expert Surveys: Comb Analysis

Comb analysis is a technique for lookup at congruence of perspectives of

different parties in the value chain. For example, one can compare the product

                                                                                                                                                                                                                                                                                                                                                                                                       259 Srivastava, Jaideep & Cooley, Robert & Deshpande, Mukund & Tan, Pang-Ning, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data” , SIGKDD Explorations 18, no. 2 (January 2002): 13. 261 Deck, Cary A., “Tracking Customer Search to Price Discriminate.” Electronic Inquiry, April 1, 2006. 262 Claypool, Mark, & Brown, David & Le, Phong & Waseda, Makoto , “Inferring User Interest,” in IEEE Internet Computing 5, no. 6 (November 2001): 35. 263 Watt, James H. & Lynch, Michael . “Using the Internet for Audience and Customer Research,” in T.J. Malkinson (Ed.), Communicating jazz: p. 127. New Orleans: IEEE.

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criteria of producers and retailers (most important reasons for product selection. 265)

For instance, if Dell wants to know why it is selling fewer computers to the Best

Buy electronics retail chain than HP, it can use comb analysis. There are three

steps in conducting a comb analysis. First, researchers ask the retailer to rate (e.g.,

on a 1-5 scale), the importance to its customers of various purchase criteria such as

price, brand, reputation, service, product innovation.267

Second, research the producer, in this case Dell, to score the same criteria.

Contrasting Dell’s strategy with those of its retailers, we observe that Dell seems to

over-invest in design and under-invest in price cuts.

The comb analysis indicates that Dell needs to lower its price – the most

important purchase criteria – to be competitive with HP. But, it can also cut the

cost of design. In summary, if Dell lowers the effort on design, which is the least

important purchase criteria, it could lower the price to Best Buy and become more

competitive with HP.

G. Expert Surveys: Delphi

The Delphi methodology was created in the 1950s by the RAND

Corporation. The goal is to reach expert consensus by experts on a certain topic.

                                                                                                                         265 Koch, Richard, The Financial Times Guide to Strategy. London: FT Prentice Hall, 2000, p. 193. 267 Koch, Richard, The Financial Times Guide to Strategy. London: FT Prentice Hall, 2000, p. 193.

85

The Delphi Method combines quantitative and qualitative data. It uses a group

process consisting with about 10-15 respondents, who are selected for their

expertise and experience. Thus anonymity is preserved. The researchers solicit

written responses to questions, while preventing direct communication between the

respondents.

In the first round of questions, questions have several answers with scores

ranging from one to ten. In the second and subsequent rounds, participants are

provided with information on how the entire group rated the same questions, and a

summary of comments made by each participant. Then the participants receive the

same questions again. The Delphi rounds continue until a predetermined level of

consensus is reached or no new information is gained.268

But how good are expert forecasts? Lord Kelvin, one of the world’s

foremost physicists, proclaimed in 1895, “Heavier-than-air flying machines are

impossible.” Marechal Foch, leader of the French military, proclaimed in 1911 that

“Airplanes are interesting toys that are of no military value.” Astronomer Royal

Richard Wooley, predicted in 1956, “Space travel is utter bilge.” Lord Rutherford,

then the world’s foremost physicist of nuclear particles stated in 1933, “Anyone

who expects a source of power from transformation of these atoms is talking

                                                                                                                         268 McBurney, Peter & Parsons, Simon & Green, Jeremy, “Forecasting market demand for new telecommunications services: an introduction.” Telematics and Informatics 19, no. 3 (2002): 225-249.

86

moonshine.” Going to the other extreme, John von Neumann, a celebrated

scientist, predicted in 1956,that “A few decades hence, energy may be free, just

like unmetered air.”

Case Discussion: Golden Years Channel: Delphi Survey A Delphi survey of GYC’s prospects would select the experts, which may

include gerontologists, marketers specializing in retirees, and social workers. A

sample of Delphi survey question includes:

• “On a scale of 1-10, do retirees get enough TV shows?”

• “Would they resent such shows since it reminds them that they are old?”

• “How many hours a week would they watch such shows on average?”

H. Surveying Trendsetters and Opnion

Professional criticts and their review are prime examples for opinion. There

are two alternative perspectives on the role of critics. One view holds that critics

are opinion leaders who influence audience demand. The opposing viewpoint

claims that critics are mostly predictors of their respective audiences. In other

words, critics are selected by most popular media to represent the taste of their

audience and they use “leading indicators” rather than opinion shapes. 276 Research

                                                                                                                         276 Eliasburg, Jehoshua & Shugan, Steven M. ,“Film Critics: Influencers or Predictors”, Journal of Marketing 61, no. 2 (Apr 1997): 68-78

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findings conclude that positive or negative critics’ reviews are a statistically

insignificant predictor of box office performance for the early weeks (weeks 1-4).

They are, however, a statistically significant predictor of box office performance

for later weeks as well, and for cumulative box office. These findings do not

support the “opinion leader” perspective, which would predict that the greatest

influence of the review should be immediately following the review. But it does

support the “predictor” hypothesis.

J. Automatic Audience Metering

The purpose of audience research is:

• To let media companies know who their audience is, and how it responds

• To let them know how much to charge for advertising

• To let advertisers know who they are reaching

There is a lot of money at stake for major advertisers (2006).277 Just for broadcast

television in the U.S., the major advertisers in 2006 were:

Procter & Gamble $4.6B

General Motors $4.4B

Time Warner $3.5B

Verizon $2.5B

                                                                                                                         277 Schiekofer, The Media Marketplace. New York: Mediacom—source not found.

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AT&T $2.5B

Ford $2.4B

Disney $2.3B

Johnson & Johnson $2.3B

Daimler Chrysler $2.2B

Glaxo Smith Kline $2.2B

Early TV audiences were measured using the “diary system”. It was used

four times a year during “sweep” periods for local stations. Essentially, the viewers

record TV viewing. Diaries have many problems. The sample is biased, or misses

responses from children, travelers, and TV viewing in bars; the process is quite

slow; and there is an opportunity for the sampled viewer to lie to boost favored

programs, or to disavow the viewing of programs they’d rather not admit to. It is

difficult to record channel surfing, when a viewer flips through many channels.

While in the past the response rate was typically around 70% for diaries, today it is

difficult to get a 25% response rate for a diary. If the people who do not respond

view TV differently from those that do, then the ratings are biased and wrong.

For radio, where similar issues exist, Arbitron covers over 250 local radio

markets in the U.S. with one to four ratings reports per year and is diary-based.283

                                                                                                                         283 Belch, George E. & Belch, Michael A. ,Advertising and Promotion: An Integrated Marketing Communications Perspective, Irwin/McGraw-Hill, Fourth Edition, 1998

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It makes up to 5 million per year to locate potential “diary keepers.” In addition,

2.6 million surveys are mailed per year with a cash premium and letter on return of

the diary. However, such surveys are slow and there is an under-report of high-

income households. Another rating service is Statistical Research Inc.’s RADAR

(Radio’s All-Dimension Audience Research) studies, sponsored by the major U.S.

radio networks.284 Symmetric Resources provides rating services for 200 smaller

radio markets. Others are Strategic Radio Research’s AccuRatings.285

Another way TV audiences were tracked were telephone surveys, are used

for TV “overnight” ratings. It is fast, but the sample can be biased. Also,

respondents often run out of patience, and answers may be incomplete.

This encouraged the development of automatic systems of audience

monitoring. A passive audience meter called the Dynascope was developed in

1965. It was a movie camera that took pictures of both the TV viewer and the TV

show every 15 seconds.287 1.5 million pictures were analyzed. It found that when

the TV set was on, in 19% of the time no one was in the room actually watching,

and 21% of the time the person was engaged in a different activity.288

                                                                                                                         284 Belch, George E. & Belch, Michael A.,Advertising and Promotion: An Integrated Marketing Communications Perspective, Irwin/McGraw-Hill, Fourth Edition, 1998 285 Belch, George E. & Belch, Michael A. ,Advertising and Promotion: An Integrated Marketing Communications Perspective, Irwin/McGraw-Hill, Fourth Edition, 1998 287 Larson, Erik , “Watching Americans Watch TV,” The Atlantic Monthly 269, no. 3 (March 1992): 66-74 288 Larson, Erik , “Watching Americans Watch TV,” The Atlantic Monthly 269, no. 3 (March 1992): 66-74

90

Another method was Kiewit’s “hot bodies” system, which scanned the room

for people with an infrared sensor. But Kiewit’s scanner readings were distorted by

a “big-dog effect.”289

The more practical approach was the Nielsen People Meter. It is an

electronic system placed in 5,000 randomly selected household in the U.S. and

positioned on each TV set in a sample household. It provides an instant measure

and there is no “lying.” But children, travelers, and bar viewing are still not

captured, and often nobody is actually in the room watching. It also requires the

viewers to identify themselves, so as to differentiate between different members of

a household, which still requires cooperation. The sample may be biased, as older

people have a higher refusal rate to participate and young men are the most willing

to employ the meter.

290

                                                                                                                         289 Larson, Erik , “Watching Americans Watch TV,” The Atlantic Monthly 269, no. 3 (March 1992): 66-74 290 http://tvbythenumbers.com/2009/12/01/nielsen-to-have-internet-meters-in-place-prior-to-2010-11-season/34921

91

Also, the greater audience fragmentation makes results less reliable. The

percentage of standard deviation tends to grow as rating figures become smaller.

For example, a “true” ratings of 6 (6% of TV households), in a sample of 3,000

will show as a sample ratings between 5.2 and 6.8 (±.8) in 95% of the sample (i.e.,

the relative error is 0.8/6 14%). But the same error at a 95% confidence level for a

“true” rating of only 2 will be (±.5), i.e., a relative error of ± 25%. And for a small

cable channel with “true” rating of 3(±.2), the relative error is ±65%.

291

Another automated system is Nielsen Broadcast Data Systems (BDS). BDS

is used to track radio songs and play of ads. Nielsen launched BDS in the United

States in 1989. It tracks airplay of recordings by radio stations, including by format

and provides access to stations’ playlists. It’s the basis for the Billboard Top 100

Singles292. It surveys stations (~1,000 major stations in 128 markets) by

computerized radio scanners, and music is then computer matched. It recognizes

the “fingerprints” of songs submitted to BDS (over one million). Play frequencies

are then combined with Arbitron audience estimates of a station at that particular

                                                                                                                         291 http://www.webspin-design.comassets/Newsletter/Sept03/nr-reach-trend-top.gif--source not found. 292 Poltrack, David. “Media Audience Research” Course. Columbia University Business School. Fall 1998

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time of the day, for information on actual audience for ads and frequency exposure.

BDS can then estimate audiences for a given title or advertisement in each market.

Radio/artist managers request over 10,000 reports each day. Some songs are big on

radio but not in sales.293

Case Discussion: People Meter for Golden Years Channel (GYC)

In theory, GYC could benefit from the fast and relatively accurate TV

ratings data via the People Meter. It would also show demographics more

precisely. But in practice, GYC’s ratings will be too low to register.

In 2003, a producer of the Nippon TV Network (NTV) manipulated

television ratings for his show.294 The producer used money to find out what

specific households were being observed by the ratings agency Video Research

Ltd. and got those homes to watch certain shows by bribing the occupants through

various benefits.295 The chairman of Nippon Television Network (NTV)

Corporation was forced to resign.

                                                                                                                         293 http://en-us.nielsen.com/content/dam/nielsen/en_us/documents/pdf/Fact%20Sheets%20III/Nielsen%20BDS%20Radio.pdf 294 “Heads Roll in NTV Ratings Scandal.” The Japan Times Online. 19 November 2003. http://search.japantimes.co.jp/cgi-bin/nn20031119b6.html 295 “Heads Roll in NTV Ratings Scandal.” The Japan Times Online. 19 November 2003. Last accessed on 19 June 2007 at http://search.japantimes.co/jp/cgi-bin/nn20031119b6.html.

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Rating service company Nielsen is the main authority for TV ratings, while

Arbitron dominates radio. Nielsen/Net Ratings is the main service for web site

traffic, but competition is greater here.296

AC Nielsen was spun off in 1996 from the information service conglomerate

Dun & Bradstreet to become an independent public company. In 1998, Nielsen

Media Research (NMR) spun off from AC Nielsen, which focuses on consumers

product market research. Since 1999 and 2001, both AC Nielsen and NMR are

owned by VNU, a major Dutch publisher: VNU itself was acquired by a

consortium of private equity firms in 2006, and renamed as The Nielsen

Company). Nielsen measures “overnights” in twenty-five major cities and rates the

four major broadcast networks. Nielsen Products also measures “pocketpieces”

every other week, measuring 5,000 households nationally with the peoplemeter,

and is available online.

In terms of local measurement, Nielsen has 211 designated market areas

(DMAs) where participants fill out viewing diaries for one week. For local stations

Nielsen “sweeps” the months of November, February, May, and July. Advertising

rates are based on local stations’ ratings during these months. The stations

therefore demand that the networks display their most popular programs during

                                                                                                                         296 O’Leary, Mick, “Web Measures Wrestle with Methodologies, Each Other”, Online Magazine 23 (May 1999): 105-106.—source not found.

94

sweeps periods to attract the largest possible audiences such as specials, hit

movies, and presenting celebrity guests.

Audience measurement companies operate all over the world wherever

advertising supported TV is in place:297

• Audimetrie SA in Belgium

• Barb in Great Britain

• MMI in Norway

• AGB in the UK, Italy, Mexico, Australia, Hungary, Greece, Turkey,

the Czech Republic, and Poland

• Mediametrie and Telemetric in France

• GFK Group, Medien Daten Suedwest, and TNS Emnid in

Germany298

• TELETEST in Austria

• IHA in Switzerland

• Auditel in Italy

• Sofres AM

• Intomart BV in The Netherlands

• AC Nielsen in Ireland299

                                                                                                                         297 “Sites of the TV Audience Measurement Companies.” European Audiovisual Observatory, August 2001 298 Larson, Erik , “Watching Americans Watch TV,” The Atlantic Monthly 269, no. 3 (March 1992): 66-74

95

III.2 New-Generation People Meter: Digital Meters

Digital meters identify audio and TV content through active codes

embedded in the program itself and in the commercial messages. Search engines

identify the programs and the advertisements that are watched. This enables real

time reports on watching or listening. Such a system can meter broadcast, DBS,

PVR, digital cable, and radio use.

In the U.S., there is a battle between the Nielsen Local People Meter (LPM),

which is channel-based, versus the Arbitron Passive People Meter (PPM), which is

program-based. The Nielsen LPM Procedure consists of a meter resting on top of

every TV in a Nielsen household and each family member has an assigned

number.306 While old local station system diaries collected in “sweep” periods,

Nielsen initiates overnight Local People Meter data. This allows for larger local

samples (8,000 vs. 540 for diaries). The Nielsen Local People Meter costs $30

million in development and permits the collection of audience response in near real

time. It provides continuous measurements of major local markets, rather than just

having four sweep periods. It includes demographics. Introduced in 2002, it

became a full-scale U.S. operation in 2006. It includes a low resolution optical                                                                                                                                                                                                                                                                                                                                                                                                        299 Source: IP—source not found. 306 Maynard, John , “Local People Meters May Mean Sweeping changes on TV,” The Washington Post, April 28, 2005, A01

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meter that monitors how many people are in the room, and identifies the members

of households. It can also determine fast-forwarding though ads. It expanded the

national sample from 5,000 to 10,000.

In contrast, the Arbitron Portable People Meter (PPM) is worn by the

consumer, and detects and records programming wherever the consumer is located,

and whatever the program source is.307 The PPM was tested in Houston from 2005

to 2006. The PPM reads an encoded audio message that is embedded into the audio

track of every piece of media (including, for example, TV, radio, and the Internet)

that has sound.308

309

The Arbitron PPM, because worn by users, is better able to keep up with multiple

TV sets in household and out-of-home viewing. However, it requires users to wear

                                                                                                                         307 http://www.arbitron.com/portable_people_meters/thesystem_ppm.htm 308 Besser, Charles N., “PPM is the next big score for sports TV”. Advertising Age 76, no. 26 (6/27/2005): 22 309 http://www.arbitron.com/portable_people_meters/images/us_ppmaction450px.jpg

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the device or have it nearby. It is also more expensive but it can be used for radio,

TV, cable, and others.310

III.3 Metering Alternatives: Cable Box And TiVo Box

An alternative is to use the digital set-topbox (STB) of cable or satellite TV.

This would increase the sample size to hundreds of thousands per market. The

concept and technology has been around since the 1980s.The cable TV industry in

the US decided not to collect STB data, individually or even in the aggregate, to

avoid giving customers a feeling that they are being watched and monitored, and to

avoid regulatory privacy protection laws that were certain to be enabled otherwise.

A second round of using set-top boxes in multi-channel real-time metering started

in the late ‘90s. Media research agencies could buy aggregated set top box data

from cable operators to provide a second-by-second analysis of viewing habits.313

Among other things, this enables a shift from program ratings to advertising

ratings. Advertising ratings are the message viewers tuned in when the commercial

message was actually running.314

                                                                                                                         310 Broadcasting & Cable, 2/2002—source not found. 313 “MTV Networks Leverages Charter Data from TNS Media Research,” Wireless News, August 10, 2007 314 Shabbab, George , “Not A Second to Lose,” MediaWeek 17, no. 28 (July 23-July 30, 2007), New York:10

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The TiVo Box, a type of DVR subscription service, enables real-time

monitoring of viewing and also keeps data for a while. It permits the analysis of

time shifting and zapping of commercial ads.315

Cellphones, too, can be used in media measurement. Researchers can use

specially adapted mobile phones to measure what TV programs consumers listen to

and see. One provider is Integrated Media Measurement Inc.316

Real Time Viewing Measurement for TV Programs

Nielsen is introducing a media consumption tracking system that follows

consumers’ activities on the web, TV, mobile and per GPS when shopping.

However, there are also privacy limits to media consumption tracking.

Measurement technology affects results. Therefore, it is always a battlefield.

The purpose of metering is not curiosity about the popularity of a program, but

rather to be a major tool for changing advertisers.. Any change in metering

procedure has economic effects. Measurement technology affects results.

Therefore, there is a constant battle between broadcasters and cable satellite

channels on the one hand, and advertisers, with the rating agencies caught in the

middle. For example, the effect of the adoption of the People Meter over paper

                                                                                                                         315 http://www.nytimes.com/images/blogs/tvdecoder/posts/1107/tivo-box.jpg 316 Clark, Don, “Ad Measurement is Going High Tech.” Wall Street Journal, Section B: Page 2, Column 3, April 6, 2006, Thursday.

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diaries made significant difference. And the shift to LPM does the same. People

Meters measurements were permanently lower than combined diary TV ratings in

1990 by an average of about 4.5 points. CBS lost 2.0 points and 1.5 points. In

contrast, cable had a substantial ratings gain. There were also effects on different

programming categories. Participation shows were boosted 5 points in rating,

sitcoms 1.5 and news 0.2. All other categories dropped, with medical shows having

the highest drop of minus 4.1 points.

All this translates into revenue impacts. In 1990, each ratings point was worth

approximately $140 million per year. The network decrease in ratings by 4.5 points

could therefore have cost the major networks about $600 million a year.

Similarly, local people metering had major impacts on numbers. In New

York City, the introduction of local people meters caused Fox, UPN, and the WB

network affiliates to show big drops in audiences. The Washington D.C.2005

tryout showed that instead of 650,000 households watching local TV from 5p.m. to

7p.m. as had been measured before, only 526,000 households were watching using

the new measurement technology. Cable lost another 114,000 households. Some

population groups especially showed audience losses with LPM. For Washington

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D.C., the claimed undercount rates were 25% for Hispanic homes and 20% for

African American homes.324

Fox TV network , a loser under the new technology, therefore complained

that LPM undercounted its minority viewers and that advertising rates were

therefore too low. “Don’t Count Us Out,” a group funded by News Corp.,

generated political pressures in Washington and New York City on Nielsen to

include these underrepresented audience groups.325 Thus one can see that ratings

technology and ratings methodology affect Dollars, Euros, and Yens. It is therefore

important that the ratings agencies are trusted by all sides. Minimum standards for

broadcast audience analysis research have been established by the Electronic

Media Ratings Council in New York, which audits and accredits rating services.

Members of the Council are the trade and advertising associations of the

broadcasting, cable TV, and radio.

We’ve looked at how to measure audiences. The next question is, how to

interpret and use the data.

III.4 Audience Metrics

There are many audience metrics. We will discuss ten:

                                                                                                                         324 Maynard, John , “Nielsen Delays Release of Local People Meters,” Washington Post, Thursday, June 2, 2005, C07. 325 Maynard, John “Local People Meters May Mean Sweeping changes on TV,” The Washington Post, April 28, 2005, A01.

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Households rather than individuals are usually the base unit for measuring

audiences. Audience measures are usually done in “parts” of days (dayparts).

Audience Metric #1: HUT (Households using TV)

HUT measures the households actually watching at that time.

Audience Metric #2: Rating

The rating of a program is its share in total TV audience. Rating = viewers

of a program (x 100) ÷ TV households. In the United States there are around 105

million TV households. For example, if 20 million households watch the show

E.R.,

Rating = 10510020× = 19.0

Audience Metric #3: Share

Share is the percentage of TV sets in use (or persons viewing) tuned to a

program.

Share = HUT

Viewers 100×

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For example, if 60 million households watch any TV during the E.R. time

slot (=HUT). Then the share = 20 million HH × 100/60 million HH (HUT) = 33.3.

The share is greater than the rating since it is the percentage of actual

watching households not of all potential ones. However that also means that a

program may have a tiny audience, yet a high share at 4am on a Sunday measuring

when hardly anybody is watching TV. (HUT is less than TV HH)

The Highest Ranked Regular Program Series, U.S.

Year Program Share Rating

1950-51 Texaco Star Theatre 61.6 81

1951-52 Arthur Godfrey’s Talent Scouts 53.8 78

1952-53 I Love Lucy 67.3 68

1953-54 I Love Lucy 58.8 67

1991-92 60 Minutes 21.7 36

1992-93 60 Minutes 21.6 35

1993-94 Home Improvement 21.9 33

1994-95 Seinfeld 20.4 31

1995-96 E.R. 22.0 36

1996-97 E.R. 21.2 35

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Audience Metric #4: Gross Ratings Points

Rating point = 1 percent of the potential audience.

Gross Ratings Points (GRP) is the sum of ratings over a time period. If an

advertiser uses four different programs with respective ratings of 15, 22, 19, and

27, the weekly GRP becomes the sum, or 83 GRP.

Audience Metric #5: CUME

The CUME or “reach” measures the number of viewers or listeners who

tune in at least once per week for a channel or station.. This is useful for cable

channels or radio stations.

Audience Metric #6: Average Quarter Hour Audience (AQH)

AQH is the average number of persons who listen to a station at least five

minutes during a fifteen minute period, for major periods of the day. It shows how

many people are reached over the week.

Example for CUME/AQH

Radio Station #1 has a CUME of 20,000, which is high, and an audience at

an AQH of 150, which is low. This means the station attracts large number of

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people over the course of in a week but does not keep them, and has therefore few

listeners at any given time. Thus, the station seems to promote itself well, but does

not have good programming to keep listeners.

Radio Station #2, in contrast, has a CUME of 10,000, which is low, and an

AQH of 2,500, which is high. This means the station has a small but loyal audience

and 25% of overall listeners are listening at any moment. Ultimately, this increases

the chance that advertisements will be heard by this audience, because it stays

tuned in.

Audience Metric #7: Average Frequency (AF) of Exposure

The AF is used to calculate how many times an advertising must be played

so the average listener will hear it, for example, 3 times.

AF = AQH × number of spots per week/CUME

Number of spots per week = {(AF × CUME)/AQH}

For example, assume Radio Station #1 has an AQH = 150 and a CUME =

20,000. To obtain an Average Frequency of 3, we estimate the number of spots

per week, using the above equation: {(3 × 20,000)/150} = {(60,000/150)} = 400.

The result means that we need 400 ad spots per week to reach the average listener

3 times. On the other hand, assume that Radio Station #2 has an AQH = 2,500 and

a CUME = 10,000. To obtain an AF of 3 (AF), the number of spots per week is

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calculated in this way: (3 × 10,000)/2,500 = 30,000/2,500 = 12. This indicates that

on Radio Station #2 one only needs 12 ad spots per week to reach its average

listener 3 times. This will be much cheaper because it is more targeted. However,

Radio Station #1 will reach more people because it has a higher CUME.

Audience Metric # 8: Cost Per Thousand (CPM)

CPM is the expenditure needed to reach 1,000 households or persons with an

ad. It is not strictly a value that gets measured but rather set by supply and demand.

It can be derived from the price of an advertising spot, divided by its audience.

CPM = {(cost of advertising) × 1,000}/average audience.

Inter-Media CPM Comparisons [year?]

Medium CPM

Daily Newspapers $19

Prime Time Broadcast TV $16

Day Time Broadcast TV $5

Radio $6

Magazines – General $5 - 190

Magazines – Specific:

Sports Weekly $8.75-28.38

• ESPN Magazine $19.59-

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54.95

• Sports Illustrated $19.59-75.17

• Sporting News $18.71-73.62

• TIME Business Edition $24.47

• Business Week, Fortune, Forbes $41.21

• Internet

• Web Banner List Price $29

• Web Banner Avg. Price $4

• Day Time TV $5

• Direct Email $20

• Solo Direct Email $934

• Shared Direct Email $40

Most newspapers calculate their CPM as the single column inch advertising

rate divided by their circulation. Magazines determine their CPM by dividing the

cost of a full page ad by their circulation.

Why Are CPM Prices Different for Different Media? First, different supply and

demand conditions are present in different media. Local newspapers usually have

very little local newspaper competition, and thereore have market power for

several categories of local ads. In New York City, CPM is enormously high for the

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New York Times theater box ads, which are the most effective way to reach

potential audiences. In contrast, local radio is more competitive because there are

several local stations and the advertising prices reflect no market power.

Second, media vary in their effectiveness, based upon length and quality of

exposure, sensory involvement, interactivity and ease of response. Higher

effectiveness raises advertisers’ willingness to pay for a higher CPM333.

The “3-D cube” of advertising value depicted below is a way to show average

CPMs for different media based on three dimensions: targetability; sensory

intensity and interactivity334.

335

                                                                                                                         333 Harvey, Bill. “Cable Advertising Revenue and Addressable Commercials”, CTAM Quarterly Journal (Spring 1997): 14-23 334 Harvey, Bill. “Cable Advertising Revenue and Addressable Commercials”, CTAM Quarterly Journal (Spring 1997): 14-23 335 Harvey, Bill. “Cable Advertising Revenue and Addressable Commercials”, CTAM Quarterly Journal (Spring 1997): 20

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Third, media have different incremental cost. For instance, print media must

add paper, printing and transportation to their total cost, whereas TV broadcasting

has no incremental cost per viewer.

For the major TV networks in the U.S. the CPMs are increasing, because

they can reach national audiences which other media cannot do. For cable

channels, there was a decline in CPM pricesfor broad based networks (such as the

USA Network) and an increase for specialty networks (Animal Planet). These

trends indicate that advertisers are looking for either niche demographic markets,

or for a national reach.

Media issue some form of a “rate card” which state the prices of advertising

time as space. It includes package plans, discounts and policies. These lists are

often last starting points for negotiations.

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                                                                                                                         336 Johnson, Bradley. “Low CPM Can Spell Bargin for Buyers”, Advertising Age, May 19, 2003 http://adage.com/article?article_id=49494

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Media Metric #9: Quads

Nielsen-type ratings measure only the number of viewers. This is a

quantitative measure and does not measure the quality of viewing. It does not

determine the intensity of preference of the audience. To measure qualitatively, not

just quantitatively, requires “attitude measurement” techniques such as focus

groups and in-depth interviews.

Audience measurement “quads” is a tool used by TV networks to measure

viewing behavior and attitude. Two factors are taken into account. The first is the

tuning length of the episode. i.e., the program’s “holding power.” Second, the

frequency of viewing or “loyalty” to the program is considered.

Holding power indicates program liking, involvement, and advertising. The

viewer is likely not to switch channels during commercial breaks.

Quads analysis distinguish four viewer types. Committed audiences (“Gold

cards”) watch over 75% of an episode and watch over 55% of episodes shown in

an analysis period. The “Occasionally Committed” watches 75% of a program, and

less than 50% of episodes. “Silver Sliders” watch less than 75% of a program, but

regularly. Finally, “Viewers Lite” watch less than 50% of a program, and rarely.

Measurements seem to show that cable networks have a more fickle audience than

broadcasts. Of course, since they ofer more viewing options, this is not surprising.

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337

Audience Metric #10: “Q”

A performer can be rated on both familiarity and how well she or he is

liked.338 A Performer “Q Score” is a measure of how much an audience “likes” a

show or performer. Evaluations/TVQ Inc., a NY-based research firm, developed

the methodology in 1964339. The “Q” metric is a derivative of ratings and overall

recognizability of the star, to qualitatively assess actors.340 Q is a ratio of the

“Favorite” score to the “Familiar” score. “Familiarity” measures the proportion of

respondents who recognized the performer. Respondents also indicate which stars

are their “favorites.”341 This means the Q rating can be high if a performer is

extremely well-liked by a core group.342 For example, James Gandolfini from “The

Sopranos” had a Q score of 36, which is above the prime time male average of

19.343

                                                                                                                         337 source could not be located. 338 Phipps, Jennie L. . “Favorites are Good Buys”, Television Week 22, no. 16 (Apr 21, 2003): 41-42 339 Phipps, Jennie L. . “Favorites are Good Buys”, Television Week 22, no. 16 (Apr 21, 2003): 41-42 340 Phipps, Jennie L. . “Favorites are Good Buys”, Television Week 22, no. 16 (Apr 21, 2003): 41-42 341 Phipps, Jennie L. . “Favorites are Good Buys”, Television Week 22, no. 16 (Apr 21, 2003): 41-42 342 Lowry, Brian , “Q Marks Spot in the Hunt for What Sells.” Los Angeles Times, Sep 12, 2001. pg. F.1 343 Phipps, Jennie L. . “Favorites are Good Buys”, Television Week 22, no. 16 (Apr 21, 2003): 41-42

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High performer Q and high program Q are related. Personality appeal raises

a show’s overall appeal. A high Q score for a show often means that viewers watch

more of the commercials.344

IV. Demand Experiments There are several types of demand experiments including:

1. Test Marketing

2. Uncontrolled Studies

3. Controlled Studies

4. Laboratory Experiments

Test marketing means launching the media product, e.g. a TV show or a

film, with full marketing and advertising efforts in several test cities The consumer

response is then tracked.

Test marketing is done for films, with initial limited roll-out and includes

exit interviews. It enables decisions about further development, adaptations or fine-

tuning, and discontinuation.362 An instance of this procedure is testing a TV show

                                                                                                                         344 Phipps, Jennie L. . “Favorites are Good Buys”, Television Week 22, no. 16 (Apr 21, 2003): 41-42 362 Aris, Annet, “Value-Creating Management of Media Companies: Chapter 5,” McKinsey & Company, Inc., 2003—source not found

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in a small country. The Dutch media producer Endemol uses the entire Dutch

market to test shows for an international rollout.363

Problems of test marketing are the premature exposure of the product to

competitors.

In controlled studies, researchers manipulate the important variables to

observe their effect; can be fairly accurate but also costly and time-consuming.364

In contrast, in an uncontrolled study, researchers are the only observers.

Uncontrolled research often uses past sales data such as:

• Aggregate sales data of a single company

• Sales data for an individual retail outlet

• Panel data – individual purchase reports from members of a selected

consumer panel.

Marketing research companies collect individual purchase data from panels of

several thousand households. Each household keeps a daily diary of items

purchased and their prices.367 Purchases by panel members can now be recorded

                                                                                                                         363 Aris, Annet, “Value-Creating Management of Media Companies: Chapter 5,” McKinsey & Company, Inc., 2003 364 Nagle, Thomas. T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 367 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

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automatically by in-store POS scanners. Customers could reveal their

demographics in return for some store credits or coupons.369 Examples include

book or music stores.

In experimentally controlled studies of actual purchases, the researchers

generate price variations while holding constant other variables, such as

advertising.372 This can also be done for mail-order or internet, by special offers to

a subset.373

Magazine test marketing serves as a good example of controlled purchase

experiment. A magazine can be tested through direct mail. Magazine firms utilize a

“dry test,” where the product is tested without being published. Solicitation letters

are sent out to potential readers, though the first issue may be years away.374 This

also allows the magazine company to determine which combination of design,

prices, offers, advertising copy, target demographics and mailing lists work the

best.375

On the internet, experiments become much easier. If Amazon.com wants to

find out whether a new design of a webpage increases sales, it will run a controlled

                                                                                                                         369 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 372 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 373 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 374 Kobak, James., “Testing a New Magazine Through Direct Mail,” How to Start a Magazine—source not found. 375 Kobak, James, “Testing a New Magazine Through Direct Mail,” How to Start a Magazine

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experiment376. It will show the different page design to, say, every hundredth

visitor. Determination of whether the new design increases sales can be made

within a few days.377

In-store purchase experiments can be quite costly and run into millions of

dollars. The cost is high because each additional factor studied requires more

stores. For example, when Quaker Oats conducted an in-store experiment that

focused on the effect of price alone, the study required 120 stores and ran for three

months.379 Also, charging lower prices can become expensive for large-ticket items

such as TV sets or computers. This leads to the use of laboratory experiments.

A laboratory research facility at a shopping mall resembles a small convenience

store.382 Such experiments try to provide the realism of in-store trials without its

high cost and exposure to competitors.383 Participants and prices are controlled.384

Consumers get rewarded a substantial discount. The overall cost is much smaller

                                                                                                                         376 Varian, Hal R. “Kaizen, That Continuous Improvement Strategy, Finds Its Ideal Environment.” The New York Times, February 8, 2007. 377 Varian, Hal R. “Kaizen, That Continuous Improvement Strategy, Finds Its Ideal Environment.” The New York Times, February 8, 2007. 379 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 382 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 383 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995 384 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

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than for in-store testing, and it is therefore popular with big-ticket electronics

products.385

V. Measuring Actual Sales

V.1 Books:

Book bestseller lists are tabulated from actual sales by newpapers,

magazines, or other organizations. It is compiled from hundreds of book stores,

but the identity and weight given to each store is not disclosed. The system is

basically an extensive sampling of retailers. The problem with such lists is that

they are self-fulfilling. They determine book location inside a bookstore, which has

a substantial effect on book sales. The list also determines whether or not the book

will be discounted.

Best-seller lists have been manipulated. Publishers often “pad” the list,

meaning that they buy their own books in bulk from stores around the United

States to get their sales up for the lists. The authors of the book The Discipline of

Market Leaders, business consultants Michael Treacy and Fred Wiersema,

reportedly spent $250,000 to buy 10,000 copies of their own book, propeling it into

the bestseller list and to sales of over 250,000 copies. (The New York Times now

                                                                                                                         385 Nagle, Thomas T. & Holden, Reed K. ,The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

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places a dagger next to any titles when substantial bulk sales are being reported at

individual stores.)

V.2 Music Sales - POS

In old days of music sales system, Billboard Magazine (or its equivalent in

other countries) contacted a sample of selected retailers. Reporting was often

inaccurate, merely rank-ordered, rather than with full numbers and susceptible to

manipulation. A vast improvement came about through the Point-of-Sale (“POS”)

SoundScan System. Developed by Sound Data in 1987 and used by Billboard for

its Top Album list, it is a computerized data collection system with bar-code

scanning by retailers. SoundScan claims to measure 85% of all music sales in the

US. Point-of-sale purchases are tabulated from over 14,000 retail as well as mass

merchant and non-traditional outlets such as on-line stores and venues 392 It is also

used by performing right organizations such as ASCAP and BMI to track royalties.

 Nielsen acquired SoundScan, and also offers BookScan and VideoScan.

DVD sales, however, remain a more closely quarded number. Distributors usually

hype a film’s initial DVD sales, but do not release periodic sales information

                                                                                                                         392 http://www.events-in-music.com/what-is-soundscan.html

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thereafter.395 Yet such DVD sales information is important to actors, directors, and

writers for royalties and profit information. In consequence, talent agencies and

management firms have created research teams to check on DVD revenue and

costs. There are also specialized companies that work on DVD sales, such as

Adams Media Research (AMR).396

V.3 Film Audiences

The company Exhibitor Relations Co. records film ticket data by collecting box

office attendance from studios and reports the rentals to the media every week.397

The main criticism of this movie audience reporting methodology is that it is

inaccurate. Anne Thompson, editor of Premiere Magazine, discussed the numbers

as “made up - fabricated every week”.399 The studios extrapolate the Sunday

figures from the Friday-Saturday figures, based on experience.400 To make sure

theaters are not misreporting the number of tickets sold, distributors employ

undercover checkers, who buy numbered tickets at the first and last shows at

randomly selected theaters.401 Film studios also receive direct information from

                                                                                                                         395 Horn, John , “DVD sales figures turn every film into a mystery,” Los Angeles Times, April 17, 2005. http://articles.latimes.com/2005/apr/17/entertainment/et-dvdmoney17 396 Horn, John , “DVD sales figures turn every film into a mystery,” Los Angeles Times, April 17, 2005. http://articles.latimes.com/2005/apr/17/entertainment/et-dvdmoney17 397 Shaw, David, “Tinseltown Spins Yarns, Media Take Bait,” Los Angeles Times, Feb 12, 2001 399 Shaw, David, “Tinseltown Spins Yarns, Media Take Bait,” Los Angeles Times, Feb 12, 2001 400 Shaw, David, “Tinseltown Spins Yarns, Media Take Bait,” Los Angeles Times, Feb 12, 2001 401 Epstein, Edward Jay, “The Big Picture, the New Logic of Money and Power in Hollywood,” New York: E.J.E. Publications, Ltd., Inc., 2005.

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national and regional multiplex chains in the United States and Canada.402 In

addition, studios conduct exist polls, to determine the demographics of

audiences.403 For focus group audience research, Nielsen National Research Group

(NRG) are providers.404

V.4 RFID Tracking

Radio Frequency Identification chips (RFIDs) are a technologically

advanced method of tracking. An RFID is a passive radio transponder with view-

ware that reflects a radio signal received. The tag of an RFID is a small integrated-

circuit chip with a radio and identification code embedded into it, which can be

scanned from a distance. It is likely to replace barcodes. As passive (unpowered)

RFID prices comes down to pennies, it is on the verge of becoming a major

tracking and measurement tool.

In 2005, Walmart required its top 100 suppliers to apply RFID labels to all

shipments, so as to improve supply chain management. The next step is tracking

                                                                                                                         402 Epstein, Edward Jay, “The Big Picture, the New Logic of Money and Power in Hollywood,” New York: E.J.E. Publications, Ltd., Inc., 2005. 403 Epstein, Edward Jay, “The Big Picture, The New Logic of Money and Power in Hollywood,” New York: E.J.E. Publications, Ltd., Inc., 2005. 404 Dutka, Elaine. “Audience Tests: Plot Thickens.”, Los Angeles Times, August 31 2003. . http://articles.latimes.com/2003/aug/31/entertainment/ca-dutka31

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the POS (Point of Sales) with potential ID and profiling use by potential

consumer’s home. It is a research tool for real time audience analysis.405

Samsung developed an RFID fridge, which suggests recipes based on what

one has stocked in the refrigerator, and compiles shopping lists of what is still

needed. The same idea can be applied to music CDs; it can suggest play list for the

evening. This can potentially be linked to media companies for audience analysis.

VII. SELF-REPORTING

VII.1 Measuring Circulation

A. Producer Self-Reporting

Producer self-reporting is mainly used by newspapers and magazines. Each

media company sends reports on circulation, ad sales, and other relevant

information to a central unit. The central unit compiles the information and

prepares different reports. The central unit is also responsible for auditing.

The Audit Bureau of Circulation (ABCs) began in exist in many countries to

audit and verify newspaper and magazine circulation. Before ABC, publishers

exaggerated sales to advertisers. This led to overprinting and dumping, just to

                                                                                                                         405 Weinstein, Ron. “RFID: A Technical Overview and Its Application to the Enterprise.” IEEE Computer Society (May/June 2005). http://electricalandelectronics.org/wp-content/uploads/2008/11/01490473.pdf

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claim the high circulation. Advertisers and ad agencies then created an institution

to sort out the mess.

The ABC board typically consists of advertisers and ad-agency

representatives as well as newspaper and magazine representatives.

Half yearly, newspaper members supply publisher’s statements that detail

how and where each copy is sold. Once a year, ABC audits sales. Twice a year,

ABC requires each magazine and newspaper member to submit a statement of their

circulation, which is known as a Publisher’s Statement.

Newspapers also conduct telephone surveys through sampling. For example,

Simmons, a large consumer research firm, conducts newspaper reader research.

There are several problems associated with measuring newspaper readership. One

issue is that information about section or even story readership is difficult to

obtain. Also, demographic information is not part of self-reporting.

Even with a circulation audit there have been many instances of mis-

reporting of circulation numbers. In 2004, Belo Corp., owner of the Dallas

Morning News, and other newspapers, and 19 TV stations, were investigated. It

was found that the company had falsely reported numbers and counted unsold

papers. It overestimated circulation by 5.1% and for Sunday’s paper by 11.9%.

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This resulted in $23 million in refunds and caused their advertisers to lose

confidence.

Other mis-reporting newspapers include Hollinger’s Chicago Sun-Times,

and Tribune Co.’s Newsday, Hoy, etc. The Tribune counted unsold copies that

were not returned. It overstated 40,000 copies of circulation and 60,000 copies for

Sunday’s paper.

But how to define circulation? Circulation encompasses paid subscriptions and

newsstand sales. The question is how do we count bulk copies provided in hotels,

businesses, and hospitals? How steep can discounts be? The ABC specifies that

circulation that boost a paper must be sold for at least 50% of its normal price to be

counted as paid circulation.415

Alternatives to ABC in the U.S. are BPA International, which also provides

a business magazine in twenty countries, and Mediamark Research, which

specializes in consumer magazines, Readership.com and Scarborough. In addition,

there are other magazine circulation reports include Folio 400, which tracks

newsstand and subscription sales of top 400 magazines, and Magazine Publishers

of America, which tracks circulation for its 200 member magazines and

periodicals.

                                                                                                                         415 Steinberg, Jaques & Torok, Tom, “Your Daily Paper, Courtesy of a Sponsor,” The New York Times, January 10, 2005, C6

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VI. Measuring Traffic

VI.1 3 Approaches To Measuring Internet Audiences

There is a battle to become the Nielsen of the internet.416 The two main

companies in the US are Nielsen/Net Ratings and Media Metrix. Each has about

50,000 people being tracked.417 Other companies are PC Data, NetValue, and

advertising and marketing networks such as DoubleClick and MatchLogic.418

Privacy concerns have been expressed against companies like DoubleClick that

operate without people’s knowledge. .419[UPDATE]

There are three approaches to measuring Internet audience: Site-Level, Ad-

Level, and User-Level. Site-Level counts audience’ website visits, which is similar

to actual sales approach. Ad-Level measures clicks on ads when user is transferred

to advertisers. It is also similar to actual sales approach. User-Level is built by the

third parties from panel/meter data, and similar to TV ratings approach.

A. Site-Level Measurement

                                                                                                                         416 Roberts, Johnnie. “How to Count Eyeballs on the Web.” MSNBC.com. 27 November 2006. Newsweek. 417 Appelman, Hilary. “Ratings That Know What You’re Looking At, and When.” NYTimes.com. 7 June 2000. Last accessed on 18 June 2007 at http://partners.nytimes.com/library/tech/00/06/biztech/technology/07appe.html. 418 Appelman, Hilary. “Ratings That Know What You’re Looking At, and When.” NYTimes.com. 7 June 2000. Last accessed on 18 June 2007 at http://partners.nytimes.com/library/tech/00/06/biztech/technology/07appe.html. 419 Appelman, Hilary. “Ratings That Know What You’re Looking At, and When.” NYTimes.com. 7 June 2000. Last accessed on 18 June 2007 at http://partners.nytimes.com/library/tech/00/06/biztech/technology/07appe.html.

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    Site-Level measurement is basically a self reporting system by the

website or visitor that can potential identify users, user types, countries, etc.

Tabulations are based on page requests. This is the tactic that is most commonly

used by websites. Internet ratings are useful because total website hits can be used

as the basis for determining unique users, given a relationship between the two.

The modified exponential function is the best fit.

The following graph shows the top websites of US internet users for May, 2010.

Rank Property Unique Visitors (000)

Total Internet : Total Audience 1 Google Sites 179,188 2 Yahoo! Sites 167,220 3 Microsoft Sites 160,061 4 Facebook.com 130,308 5 AOL LLC 112,364 6 Ask Network 91,939 7 Fox Interactive

Media 88,072

8 Turner Network 86,086 9 Glam Media 80,748 10 CBS Interactive 78,432 422

In 2003, Nielsen developed Net ratings software entitled SiteCensus. It is a

browser-based measurement tool, which makes a variety of data available to site

operators. They are able to see the path that users follow, the content that users

view, and the users’ location of access. This includes requests from work, school,

and wireless.

                                                                                                                         422 http://comscore.com/Press_Events/Press_Releases/2010/6/comScore_Media_Metrix_Ranks_Top_50_U.S._Web_Properties_for_May_2010

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Site level collection is often referred to as “packet sniffing.” It monitors

network traffic coming to a website and directly extracts usage data from TCP/IP

packets.423

Site-Level has systematic measurement biases. It over-counts because it

repeats visitors, and it counts not just people but also bots and spiders. It also

undercounts cached pages and can not distinguish multiple users on the same

computer.

How could one individualize information about a web-site’s audience?

Registration requirements do not work well, because it requires an effort by users;

there is also a privacy concern and fear of spam. Using “cookies” is a second major

tool. In this situation, it combines the control advantages of a site-centric approach

with the individualization of the user-centric approach. It is a standard

programming device that produces electronic files to tag individual customers with

a unique identification. Essentially, it allows a website to recognize an

individual.424

B. Ad-Level Measurement

                                                                                                                         423 Srivastava, Jaipdeep & Cooley, Robert & Deshpande, Mukund & Tan, Pang-Ning , “Web Usage Mining: Discovery and Applications of Usage Patterns from Wed Data”, SIGKIDD Explorations 18, no. 2 (January 2002):13 424 Deck, Cary A. & Wilson, Bart., “Tracking Customer Search to Price Discriminate.” Electronic Inquiry 44, no. 2 (April 1, 2006)

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Click-Through (CTR) Software measures whether a user clicked on an ad

and links it to the advertiser. It is valuable to advertisers because it measures the

actual effect of a web advertisement and is unique to the Internet. Some per-click

payments are quite high, reaching $20. However, usually they are less than $1.

Inflated click rates became a big problem. Robot hits and people create fake

clicks. In fact, fake clicks by people have become a cottage industry in India. There

are major abuses of pay-per-click. For one, “click fraud” is not illegal. Portals like

Yahoo have disincentive to crack down, there is more incentive to keep click fraud

as it shares revenue from PPC (Pay Per Click) that are charges from advertisers.

Attempts for techno-fixes have failed.

C. User-Level Measurement

User-level measurement is a sampling technique and is ultimately drawn

from a TV audience sampling model. It is composed of a large model of randomly

selected users. The software meter on a user’s PC measures behavior. The meter

reads the URL in the browser, then counts and forwards data to a web-rating

company. Data is then matched to “dictionaries of the Internet,” which categorizes

the millions of recorded URL’s.425

                                                                                                                         425 Coffey, Steve , “Internet Audience Measurement: A Practitioner’s View,” Journal of Interactive Advertising 1, no. 2 (Spring 2001): 13.

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There are several advantages of the user-level approach. Uniform

measurement provides comparability. It also provides demographics, counts the

pages actually received, and measures actual behavior, which is usually not self-

reported. Finally, there is no conflict of interest. However, it does require user

cooperation. Hence, incentives are offered to users who are willing to use the

browser.426

Nielsen is a near monopolist in TV ratings in the US but not in web ratings.

In 1995, Media Metrix installed the first meter of internet uses, the “PC Meter,”

into a consumer sample.429 There are over 100 web ratings companies such as

comScore and Hitwise.430 The methodology followed by rating companies includes

randomly collecting a sample of 50,000 recruited by phone and mail.

There are two main problems with user-centric measurement. First, there are

disadvantages to small sites which may get only a few hits and may be ignored or

undercounted. Second, it provides poor site diagnostics due to the fact that there is

no good information on sites and what the user does there.

VI.2 Data Mining

                                                                                                                         426 Srivastava, Jaipdeep & Cooley, Robert & Deshpande, Mukund & Tan, Pang-Ning, “Web Usage Mining: Discovery and Applications of Usage Patterns from Wed Data”, SIGKIDD Explorations 18, no. 2 (January 2002):13 429 Coffey, Steve, “Internet Audience Measurement: A Practitioner’s View,” Journal of Interactive Advertising 1, no. 2 (Spring 2001): 11. 430 Johnnie L. Roberts, Newsweek, Nov. 27, 2006—source not found.

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The Internet also provides a powerful tool for additional analysis and has the

capacity to track users’ browsing behavior. Mouse activity is measured through

number of clicks, the time spent moving the mouse in milliseconds, and the time

spent scrolling.431 The total time spent on a web page and the total time spent

scrolling the mouse is a reliable indicator of interest. On the other hand, the

number of mouse clicks is not a good indicator of interest432.

Demand of internet sites can be measured using web usage mining. This process is

a data mining technique used to find the usage data of web sites so web

applications can be used better.433 Pattern discovery is the usage of algorithms to

find usage patterns.434

VI.3 Case Discussion: How to Measure the Usage of the “Golden

Years” Internet Portal?

How do we know how many internet users “The Golden Years” attracts?

How many users read ads displayed on it? Measuring ad-clicks/hits from GY’s

                                                                                                                         431 Claypool, Mark & Brown, David & Le, Phong & Waseda, Makoto, “Inferring User Interest,” IEEE Internet Computing 5, no. 6 (November 2001): 35. 432 Claypool, Mark & Brown, David & Le, Phong & Waseda, Makoto, “Inferring User Interest,” IEEE Internet Computing 5, no. 6 (November 2001): 37 433 Srivastava, Jaipdeep & Cooley, Robert & Deshpande, Mukund & Tan, Pang-Ning. “Web Usage Mining: Discovery and Applications of Usage Patterns from Wed Data.” SIGKIDD Explorations. 1, no. 2 (January 2002): 12-22. 434 Srivastava, Jaipdeep & Cooley, Robert & Deshpande, Mukund, & Tan, Pang-Ning. “Web Usage Mining: Discovery and Applications of Usage Patterns from Wed Data.” SIGKIDD Explorations. 1, no. 2 (January 2002): 12-22.

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website to advertising sites helps Golden Years Media in two ways: is a base for

collective advertising revenues, and it provides information on what interests the

visitor. A website “hit” counter can collect data on the number of hits/clicks to the

GY Portal to measure the demand for the website. Together with cookies, this

would provide information about GY’s online audience.

Case Discussion: Viacom

We asked the questions, how Viacom can determine:

• The demand and related information for still non-existent products

• The characteristics of viewers/readers

• Their willingness to pay

• The characteristics of non-buyers

• What the audience likes/dislikes about the product

We now understand better the potential actions and their effectiveness. To

predict the audience for the GY cable channel, early business planning could use

focus groups, conjoint analysis, Delphi surveys, and diffusion studies. At the

production planning stage, the firm can conduct focus groups, test screening, and

psycho-physiological tests. Once the channel is running, Viacom could perform

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phone surveys, use Nielsen’s people meters (if the audience is large enough to be

measurable with some confidence), or try to use a cable set top box. The data are

used in econometric studies to find drivers, factors, and elasticities. Viacom could

also be a partner in controlled marketing research for the impact of ads for

products advertised on its channels.

The Golden Years Magazine audience could be researched the same way as

the GY channel (without the Nielsen and in much of STP method), thus achieving

synergies. In addition, three methods are added: differentiated direct solicitations,

actual rates data, and surveys of actual subscribers.

For the “The GY Portal” website, Viacom can also use some of the same

information, plus cookies (on user PCs), click data (on ads), and data on visitors

(website).

VIII. Conclusions

There are many approaches to collect and analyze data. In the near future,

the tools of online tracking will permit a real-time observation of audience, global

aggregations, large samples, customers and the tracking of their consumer

behavior. This will add power to data collection. But how is that data used? The

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remaining weakness is the quality of research follow-up. Current methodologies

are limited.

• Econometrics: must project past behavior into the future;

• Epidemic model projections: based on imperfect comparisons to other

products;

• Trade-off (conjoint): requires this to balance specific features of a

product they may be unfamiliar with.

There is no strong operational link of peak demand estimation to behavioral

models of psychological or sociology.The challenge is not just collecting more

data, but implementing more advanced “data mining.”

But even with these better tools, it is much harder to do demand

research today. It is harder to estimate demand for new products and services in a

rapidly changing environment, with fragmented audiences, much greater choice,

and shorter attention spans. Media firms will increasingly get rapid audience data

and act most rapidly on them, in the design of their products, in marketing, and in

pricing. As sophisticated as the tools are, they are probably just the beginning of

development of next generation tools utilizing much more advanced:

- Behavioral research

- Audience instant feedback

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- Trendsetters

- Cross cultural sampling

- Statistical tools

- Online technology

Demand analysis becomes more important when:

- The greater the uncertainty

- The greater the upfront investment

- The greater the economies of scale and network effects

- The more competitive alternatives

- The shorter the product cycle

Reliance on the “gut feeling intuition” of single-minded entrepreneurs and of

internal advocates can be the most expensive way to learn. If a file has a cost of

$50 million with a probability of 20% to gross $250 million, then improving the

odds to 22% by smarter demand analysis raises expected profits by $5 million, or

10%.

We have covered a lot of ground. But a last and important question remains raised

at the beginning of the chapter beyond techniques, and technologies, technocratic

management: whether such approaches are really what media firms need. Should

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media companies use demand estimation techniques, like a car manufacturer or an

airline?

Many are inclined not to forecast at all before launching a media product

because forecasts are so inaccurate.435 Leo Bogart, Executive VP of the Newspaper

Advertising Bureau, wrote that “It is a fantasy to believe that a newspaper can be

designed and packaged like a bar of soap or a can of dog food.”436

Disney CEO Michael Eisner argued that while audience research was useful to

understand past or even the present, it is not useful to look into the future. The

audience wants originality, up to a point.438

Second, do media owe their audience a special responsibility to go beyond what

this audience wants? Should it have an obligation to cover unpopular news and

difficult but important topics in its entertainment?

Time, Inc., former Editor-in-Chief Norman Pearlstine believes there should

be a balance between seeing what readers want and what media professionals think

they need. “There’s always been a balance between educating your reader and

                                                                                                                         435 Carey, John & Elton, Marin. “Forecasting demand for new consumer services: challenges and alternatives.” New Infotainment Technologies in the Home: Demand-Side Perspectives, New Jersey: Lawrence Erlbaum Associates, pp. 35-57. 436 Underwood, Doug. When MBAs Rule the Newsroom: How the Marketers and Managers Are Reshaping Today’s Media. New York: Columbia University Press, 1993, pp. 3-13. 438 http://newsimg.bbc.co.uk/media/images/40056000/jpg/_40056808_eisner_ap203b.jpg

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serving your reader…you obviously balance telling them what you think they

ought to read with giving them what they want to read…”439

Recall the earlier question: Does the audience’s demand shape the content

supply? Or does the supply – by large media firms – shape viewer preferences and

demand? Are media demand-driven as much as the audience research techniques

imply? Or are they supply-driven as marketing activities imply? As it is often the

case, both sides are partly right. Advertising, PR, and media content itself shape

the public. But audiences also reward originality, and many do not want to be

pandered. Thus, creativity is required not only in the media product itself, but also

in understanding the audience’s needs, tastes, preferences, desires, and fears. These

demand factors are often subconscious, unarticulated by audience.

Is demand analysis therefore a “bean-counting” by uncreative minds, or a

tool for pandering to audiences rather than leading them? A manager should not

make the choice between judgment and empirical estimation. If used effectively,

they are complementary.441 The avant-garde media manager may be three steps

ahead of the audience. The conventional media manager follows the audience by

one step, letting audience research make the decisions. The moderately successful

media manager is probably one step ahead, using audience research. And the

                                                                                                                         439 Hickey, Neil. “Money Lust”, Columbia Journalism Review 37, no. 2 (July/August 1998): 32 441 Nagle, Thomas T. & Holden, Reed K., The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making, Second Edition 1995

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successful innovator can be two steps ahead, usually has creative understanding of

the audience, market, and society, plus research to lower the risk.

And therefore, we should disagree with Goldman’s Hollywood adage that “nobody

knows anything.” One can improve the odds slightly, but that is enough for a

competitive advantage. In other words, “somebody knows a little better.”

Understanding one’s audience may be the cheapest investment with the highest

return. And demand analysis – understanding the audience, customers, and market

– is the key to improving the odds. We are just at the beginning.

END OF CHAPTER

VIII.1 Tools Covered

In this chapter, we covered the following tools for demand estimation:

• Statistical inference and sampling, confidence intervals

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• Delphi and Comb analysis

• Audience model-building

• Econometric demand estimation

• Network effects and the construction of upward-sloping demand schedule

• Conjoint Analysis

• Epidemic models of diffusion

• AQH, AF, and Qumes audience metrics

• Relation of ad revenues to macroeconomy

• Controlled experiments

• Panel data use

• Internet surveys

• Pyscho-physiological techniques

• Nielsen and Arbitron methodologies

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VIII.2 Issues Covered

The following issues were covered:

• People meters and PPV

• POS measurement

• Self-reporting methodology

• Click-counting

• Statistical estimation of demand

• Forecasting methodologies

• Internet methodologies

• Measuring Internet Traffic: site-level measurement, user-level measurement,

and user-centric measurement

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 Outtakes  begin:  

 

In 1939, when TV broadcast had actually been initiated the New York Times, noted that television could never compete with radio since it required families to stare into a screen.

During the “classical period” of the 18th and early 19th century, economics and psychology were closely linked. Adam Smith combined the two in The Theory of Moral Sentiments.443 But when neo-classical economics engaged, the two approaches separated more recently. They recombined in “Behavioral Economics.” Behavioral economics has the potential to explain the dynamics of consumer demand.444 It is a reaction to the deficiencies of conventional economics, and thus aims to incorporate more realistic notions of human nature into economics.445

For example, if the attributes “price” and “TV size” have average

importance scores of 5.1 and 2.5 respectively, it does not mean that “price” is more

                                                                                                                         443 “Behavioral Finance.” Wikipedia. Last accessed on June 30 2010 at http://en.wikipedia.org/wiki/Behavioral_economics 444 Snyder, Eleanor M., “The Mass Consumption Society”, Journal of Marketing Research 3 (May 1966): 208-209. 445 Rabin, Matthew, “A Perspective on Psychology and Economics,” European Economic Review 46 (2002): 657-685

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important that “TV size.”446 Conjoint analysis reveals that on average, participants

perceived that the difference between a price of “10% more than you would expect

to pay” and a price of “10% less than you would expect to pay” as more important

than the difference between TV sizes and screen styles.447 The absolute values of

the utilities have no inherent meaning. The importance of this research technique is

in the calculation of the range between the lowest and highest levels of utility value

within each attribute.448

Week of June 7, 2010449

RANK PROGRAM NETWORK VIEWERS

(000)

RATING

1 NBA FINALS -GM 5(S) ABC 18,654 10.8

4 NCIS CBS 11,257 7.5

5 AMERICA’S GOT TALENT-TUE NBC 13,088 7.4

6 GLEE FOX 11,075 6.3

6 NCIS: LOS ANGELES CBS 9,568 6.3

                                                                                                                         446 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 447 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 448 P & B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php 449 http://en-us.nielsen.com/rankings/insights/rankings/television

139

8 TWO AND A HALF MEN CBS 9,839 6.2

9 MENTALIST, THE CBS 8,909 6.1

10 60 MINUTES CBS 9,039 5.8

10 BIG BANG THEORY, THE CBS 9,119 5.8

The Highest Rated Individual Broadcasts, 1960 – 1990

Number Program Rating Share

1 MASH Special 60.2 77

2 Dallas 53.3 76

3 Roots, Pt. VIII 51.1 71

4 Super Bowl XVI 49.1 73

5 Super Bowl XVI 48.6 69

6 XIII Winter Olympics 48.5 64

7 Super Bowl XX 48.3 70

8 Gone With The Wind, Pt. 1 47.7 65

9 Gone With The Wind, Pt. 2 47.4 64

10 Super Bowl XII 47.2 67

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The tools for determining and analyzing demand for media are increasing in their

technological sophistication:

- Internet connectivity for media consumption

- Local People Meters

- Measurement software

- Cookies

- RFID

- Watermarks and IDs

These tools provide enormously powerful methods of instant feedback. Thus,

demand measurement of media use will be increasingly:

- Real – time

- Global

- Large Samples

- Customized

 

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