Marketing Mix

DOI: 10.2501/JAR-52-4-433-449 December 2012 JOURNAL OF ADVERTISING RESEARCH 433

INTRoDuCTIoNSocial media are a game-changing technology with

a major impact on business.

Early adopters who integrated social media

into their strategy and operations are well placed

to capture new opportunities. Based on a sur-

vey of more than 3,000 senior executives across

industries, geographies, and functions, a recent

McKinsey report indicated that companies quali-

fied as “networked” (those that used collabora-

tive Web 2.0 technologies intensively to connect

the internal efforts of employees and extend the

organization’s reach to customers, partners, and

suppliers) outperformed other companies in terms

of market share, profitability, and market leader-

ship (Bughin and Chui, 2010; Shearman, 2011).

The impact of social media has been widely

reported in the lay press, trade journals, and aca-

demic research. In fact, the growth of social-media

advertising spending in the United States will out-

pace search advertising by 34 percent to 15 percent

on a compound annual basis over the next 5 years

(Forrester, 2010). This “paid-for” use of social media

accounts for only a fraction of its real impact on

brand marketing. Potentially far more powerful—

though less well understood—is the accelerated

trend in unpaid-for so-called earned, unsolicited

communications originating with consumers that

are beginning to shape (and reshape) the successes

and failures of brands and companies.

Companies need to do more than just listen.

They need to engage in brand conversations. The

traditional notion of a “target customer” in the

brand-communication paradigm must be enriched

to take account of the fact that the consumer now

has a voice and wants to be heard. In this study, the

authors propose a methodology to measure and

quantify the impact of this “people power” on the

sales and profits of brands and companies.

Wikipedia describes “social media” as “Media

for social interaction, using highly accessible and

scalable publishing techniques. Social media use

Web-based technologies to turn communication

into interactive dialogues.”

Although this definition is informative, it

offers no practical help to companies wanting to

The Power of EvilThe Damage of Negative Social Media

Strongly outweigh Positive Contributions

MARCEL CoRSTJENSInsEADmarcel.corstjens@

insead.edu

ANDRIS uMBLIJSMckinseyandris_umblijs@

mckinsey.com

Media activities generated by consumers or communities that are neither paid for

nor induced by brand owners are claimed to have a potentially game-changing impact

on communication and brand building. In this study, the authors propose a rigorous

methodology to assess the impact of this type of social media activities on the actual

performance of brands in the market. The article begins by developing a four-step

process to condense the complex reality of micro-social-media events for a brand into a

manageable set of social media indicators (sMI). These sMI subsequently are used as a

subset of the drivers, together with more traditional marketing-mix elements—in a general

market-response model—to estimate their relative impact on brand performance in the

market. This methodology is illustrated with two real-world examples—one in the market

for flat-screen-television market and the other in the set of Internet broadband-service

providers.

434 JOURNAL OF ADVERTISING RESEARCH December 2012

THE POWER OF EvIL

capitalize in a meaningful and system-

atic way on the opportunities that social

media provide. Companies need to make

a distinction between three types of social

media activities:

• type 1: induced and encouraged—but

not paid for—by brand owners;

• type 2: produced by consumers for

brand owner at the brand owner’s

request; and

• type 3: generated by consumers or com-

munities that are neither paid for nor

induced by brand owners.

As example of the first type of social-

media activity, the “evolution” video is

used by Unilever to advertise the Dove

brand on YouTube. Unilever did not pay

for ad space and time; it initiated a dia-

logue between consumers about Dove and

what it stood for.

The second category often consists of

social media grounded in “user-generated

content” (UGC). Again, at Unliever, Dove

uses the UGC approach to generate a num-

ber of comments that are subsequently

shared in a variety of media, traditional,

and/or social. The production process is

different from traditional communication

approaches, but the advertisements that

result from UGC are shown in paid-for

media.

Type 3 social-media activities are unique

in that they are initiated, developed, and

produced by consumers rather than brand

owners. Examples stretch from consumer

feedback on a restaurant experience

through Twitter to comments on a publica-

tion on the Amazon Web site all the way to

a consumer-generated video on YouTube

accusing Unilever of destroying palm

forests in Indonesia to produce the palm

oil used in the production of its soap

brands.

This article focuses on this third group

of social-media activities and their impact

on brand performance. Types 1 and 2

already have been featured in the litera-

ture (Marchanda, Dube, Goh, and Chin-

tagunta, 2006; Robinson, Wysocka, and

Hand, 2007; Baye and Gatti, 2009; Shang

and Ghose, 2010; Pfeiffer and Zinnbauer,

2010; Campbell, Pitt, and Parent, 2011),

whereas type 3 can benefit from further

analyses and new approaches.

THE SoCIAL-MEDIA CoNTEXTNew and traditional media seem to be

worlds apart. When discussed together,

they tend to fuel controversy about their

respective roles and their potential syn-

ergies—a conflict illustrated by a clash

between Martin Sorrell, WPP ceo, and

Keith Weed, Unilever cmo (Marketing

Week, June 25, 2010).

Sorrell stated that research showed that

traditional media were still best for brand

building; Weed countered by insisting,

“Brands are being built when consumers

engage online.” Affirming that Unilever’s

objective was to double its growth while

reducing its environmental footprint, he

added:

Digitisation and globalisation feed off each

other creating exponential impact… The

more digital you are, the more global you

can become. The more global you are, the

more digital you can become. You have to

be ahead of your consumers in digital, you

cannot be at the same level as them.

A second issue surrounding the use of

social media in marketing is related to

measurement: How does one generate

meaningful measures that capture the full

qualitative and quantitative richness of

type 3 social-media activities for a particu-

lar brand and/or company?

The discussion begins with the rapid

rise of digital-communication channels

and the consequent split of marketing

analy tics into two camps:

“Traditionalists” analyze well-

established traditional marketing-mix

elements (shelf space, radio, print, tele-

vision advertising, price, promotions,

macroeconomic factors, and so on) using

well-developed methodologies, linking

these marketing activities to sales and

profitability.

“Digitalists,” whose roots are in technol-

ogy, focus on the measurement of many

very detailed parameters available in the

digital space in isolation of other market

mix elements.

Because of abundance of very detailed,

granular data, one trend in social-media

measurement (“listening”) has been to

provide a multitude of measures to cap-

ture activities in the social-media space

and provide estimates of their significance.

One can argue that television advertis-

ing also has a very complex structure and

can be measured at a very granular level.

Indeed, exposure frequency and reach of

different target audiences at individual

television advertising spot level regularly

is measured with good accuracy, and these

data are readably available. Actually, “tra-

ditionalists” use a more aggregated meas-

ure: Gross rating points (GRP) that capture

the market pressure of television advertis-

ing campaigns. The reason for using this

more aggregated measure is that more

granular levels of data do not always pro-

duce better analytical results (Kros, 2010).

The digital community currently oper-

ates almost independently from such

Early adopters who integrated social media

into their strategy and operations are well

placed to capture new opportunities.

December 2012 JOURNAL OF ADVERTISING RESEARCH 435

THE POWER OF EvIL

“traditionalists” and often considers itself

superior because its metrics can capture

the richness of social-media activities in

a more sophisticated, multi-dimensional

way not demonstrable with a simple GRP

television measure.

This multitude of measures, however,

makes it difficult to get an overall view of

the importance of social-media activities

over a specific period of time for a specific

brand. Many services track and measure a

wide array of such content indicators for a

range of social media (Figure 3).

Although each has its idiosyncrasies,

by and large these measurement systems

focus on

• the number of messages activated,

• the sentiment of each message, and

• the number of viewers weighted by

their respective importance.

The user of such data can drill down to a

microscopic level of a particular message,

in a particular medium, on a particular

day, aired by a particularly important per-

son (e.g., “Mrs Jones, an opinion leader”).

Although this level of detail is impressive,

marketers need a fuller perspective if such

information is to be actionable. The aggre-

gate view largely is missing in this set of

social-media metrics. It is like having an

elaborate dashboard with multiple gauges

but no mechanism to integrate all the bits

of information.

Yet another concern with existing

social-media research is the absence of

a methodology to link the intensity of

these activities with brand/company per-

formance. The viral YouTube Indonesian

palm-forest attack on Unilever could have

been considered a serious concern for

Unilever, but the basic question—“How

significant was its impact on Unilever and

the Dove brand?”—remained unanswered

absent a rigorous methodology for such

determination.

Given the recent emphasis on return

on marketing investments, establishing

a methodology to measure the return on

investment in social media—in particu-

lar for type 3 social-media activities—is

becoming a key managerial priority and is

one of the objectives of this article.

The current research makes a two-part

contribution:

• the development of a replicable meth-

odology enabling marketers to gener-

ate a manageable set of “Social-Media

Rating” (SMR) parameters that capture

the full richness of Type 3 social-media

activities, and

• the authors’ proposal that the inte-

gration of these parameters into a

marketing-response model and the

explicit estimation of the relative impact

of social-media activities on a brand/

company’s economic performance.

This is valuable not only because it allows

marketers to estimate social-media impact

on sales and profits but it improves the

validity of the estimation of the impact of

other (more traditional) elements of the

marketing mix on sales and profits.

Indeed, omitting SMR parameters from

marketing-mix models may distort the

results because a potentially major sales

driver has been overlooked.

In the next section, the authors explain

the proposed methodology to aggregate

a large variety of social-media measures

into a small set of parameters or SMR

indicators. The authors explain how these

indicators can be integrated into a rigor-

ous marketing-mix model and how their

impact can be estimated and compared to

the impact of such traditional marketing-

mix elements as price, promotions and

traditional media. The authors then illus-

trate the application of the methodology to

a major electronics brand.

METHoDoLoGYThe ultimate objective of the current study

was to determine the impact of social-

media activities on the variability of sales

over time (Figure 1).

The first stage was the construction of

a meaningful, manageable set of social-

media activity indicators (in the limit a

single indicator) that captured the large

quantity, variety, and richness of ongoing

social-media activities in the marketplace.

The value of these indicators over time

is then used in a marketing-mix response

model alongside other stimuli such as

television advertising, print, competitive

activities, coupons, promotions, macro-

economic factors, and the like to explain

the variability in sales of the focal brand/

company.

One can argue that television advertising

also has a very complex structure and can

be measured at a very granular level.

Yet another concern with existing social-media research

is the absence of a methodology to link the intensity

of these activities with brand/company performance.


THE POWER OF EvIL

The process of condensing the complex

world of micro-social-media events hap-

pens in four stages (Figure 2).

Stage I: DefineIn the “define” stage, management artic-

ulates the relevant categories for which

SMR ratings will be constructed.

There usually are four types of topics

covered by social media:

• Product Functionality and Quality

For example, the agency that managed

Samsung’s social-media strategy played

a critical role in creating a viral flow of

information about the so-called “grip”-

effect technical flaw in the first itera-

tion of the iPhone 4. (When the mobile

was handled in a certain way, the new

design antenna lost reception, and the

call was dropped.)

• Product and Ingredient Sourcing

Ethical sourcing grounds a variety of

social media discussions that range in

topic from child labor in poor countries,

to rainforest destruction, to impoverish-

ment of farmers, and so on.

• Service Quality

Consumer complaints about a variety

of customer-service issues such as long

waits on phone-response lines.

• Industry-Specific Concerns

Just as the arrival times and tarmac waits

are regular fodder for transportation-

related social media discussions, so do

any number of other industries foster

discussions specific to their practices.

The define stage is different for each social-

media project and driven by what man-

agement wants to understand about how

specific types of social-media messages

have an impact on sales. For example, to

explain what effect social media have on

sales of Brand A flat-screen television sets,

management identified four specific top-

ics of interest: pricing, technology, product

features, and brand image. Typically, these

topics need to be narrowed down fur-

ther by identifying a set of keywords and

phrases (topics) for each category:

• Price: high price, low price, overpriced,

value for money, promotion, rebate, and

so on;

• Technology: Plasma, LCD, LED, 3D,

and so on;

• Product Features: screen size, contrast

ratio, sound quality, ease of use, HDMI

ports, and so on; and

Time (weeks)

Social Media Rating (SMR) time series

Weighting/aggregation methodology

Market mix regression model

Weekly SMR (SM Negative)

SM Listening Results

Sales

Time (3 years)

Base

SM Positive

SM Negative

POS Advertising

TV Advertising

Competitive advertising

Promotional Price

MarketingStimuli

Regression

Figure 1 Illustrative Representation of Inclusion of social Media into a Marketing-Mix Regression Model Explaining sales by Market stimulus, Including social-Media Pressure

In the “define” stage,

management articulates

the relevant categories

for which SMR ratings

will be constructed.


THE POWER OF EvIL

• Brand Image: Brand A, Brand B (com-

petition), trust, consistent quality, reli-

able service, and so on.

Stage II: ListeningIn the “listening” stage of the methodol-

ogy, data are collected on the quantity and

the quality of social-media events per-

taining to the specific topics identified in

Stage I. There are many social-media lis-

tening service providers in the market-

place (See Figure 3; Rappaport, 2010).

Listening services provide rich infor-

mation regarding each individual social-

media event—Tweet, Facebook entry,

product-rating entry, and the like. For the

construction of SMR time series indicators,

the following information provided by

social-media listening service providers is

particularly relevant:

• filtering all individual social-media

events (Tweets, Facebook entries, and so

on) containing selected topics;

• estimating the sentiment of each of the

filtered events into one of the following

three groups: positive, negative, and

neutral;

• providing information about the origi-

nator of each social-media event:

– reach of each social-media event orig-

inator—number of friends on Face-

book, number of followers on Twitter,

and the like; and

– information regarding status of

the author in the social-media

Sub-Category 2

Category

Sub-Category n

Sub-Category 1

Topic 1

(+)Sentiment

(1)Sentiment

(–)SentimentTopic …

Topic n

Keywords/Phrase

Keywords/Phrase

Keywords/Phrase

Topic’s unique

followers

Topicrelevance

KOL

Followers

Keywords/Phrase

Keywords/Phrase

Define Listen Weight Aggregate

Figure 2 Methodology for the conversion of Large numbers of Individual social-Media Events into social-Media Rating (sMR) Indicators

Figure 3 some of the companies Providing social-Media Listening services


THE POWER OF EvIL

community—key opinion leaders

(KOL), and so on.

• providing information on the size of

the chain reaction of each social-media

event (topic), with analysis including:

– number of topic’s unique followers;

– total number of secondary, third,

fourth, and so on degree events; and

– further chain-reaction communica-

tions on the particular topic initiated

by the original author.

• providing information on the relevance

of the topics. To that end, most social-

media listening-service providers

deploy linguistic-analysis algorithms

to determine the relevance of each com-

munication to the original topic of the

communication.

Stage III: EvaluationIn the third phase of the current program,

the “listening” points are integrated for

each social-media event by weighing the

event according to the level of the compo-

nents described above.

The communication pressure of each

event in the marketplace is determined by

the size of audience reached—weighted by

• the “importance” (or KOL status) of

people originating the event;

• the size of the chain reaction the event

creates; and

• the relevance of the specific communica-

tion topic.

More specifically, the weighting procedure

is performed as follows (each original

communication has a starting value of 1):

• apply weight to this starting value

depending on the KOL status of the com-

munication originator;

TABLE 1Illustrative Example of the Weighting Procedure (Messages in the Table are for Demonstration Purposes)

Message Category Sentiment

Stage of weighting

Total score

A B C D E

Message count

KoL weight Reach

Chain reaction

Topic relevance

Brand A is the leader in LED technology and their LED Tvs are best on the market. I bought a 42 inch one and it has the best picture I have seen.

Technology Positive 1 2 3 2 3 36

I bought Brand A LED Tv. The black was really black on the screen—the contrast ratio is excellent. The Brand A are producing really good LED screens! And the Tv is so thin—excellent product!

Technology Positive 1 3 3 1 3 27

I spent hours to set up my new Tv. The Brand B really is not good in this sense and does not think about convenience of usage of their products—you need an engineering degree to set up their Tv!

Technology negative 1 1 1 2 2 4

My new Brand A Tv died 2 hours after plugged in! I will not buy Brand B Tvs any more. I sent it back and asked to exchange it for a different Tv.

Technology negative 1 1 3 1 3 9

Brand A Tvs are overpriced. Price negative 1 1 1 1 3 3

Brand A Tvs are worth every penny I paid for—good price for this quality product!

Price Positive 1 2 3 1 3 18

Brand c products are cheap, but you get what you pay for—I would not take one for free!

Price negative 1 3 3 3 3 81

With the manufacturer rebate Brand A Tvs are good value for money! The rebate offer lasts only one more month! Hurry!

Price Positive 1 2 2 1 3 12


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• apply additional weight depend-

ing on the reach of the originator of the

communication;

• apply additional weight depending on

the size of the chain reaction following the

original communication; and

• apply additional weight depending on

the relevance of the rated communication

topic to the original topic.

To keep the transformation process man-

ageable, the authors started the weighting

process by scoring each item at each step

in the process on a three-point scale (small,

medium, large). After each social-media

event was weighted according to the pro-

cedure articulated previously, it would

register a score between a minimum value

of 1 (if factors 2–5 in the preceding list had

the “small” weight of 1) and a maximum

value for a single event (Tweet, Facebook

entry, etc.) of 81 (the maximum weight of 3

to the power of 4—having four weighting

factors in the system).

Step IV: AggregationIn the final stage, the information gath-

ered in the first three phases is aggregated.

Weighted scores of all events are aggre-

gated into SMR indicators for

• each analysis topic (in our example,

price, technology, product features, and

brand image), and

• each sentiment (positive, neutral, and

negative).

The authors proposed 1 week as the unit

of time in the proposed procedure.

The preceding example—with four cat-

egories (topics) and three sentiments—

would produce 12 individual weekly SMR

time-series variables. Depending on the

purpose of the analysis and the number of

weekly data observations (i.e., degrees of

freedom), the number of category and sen-

timent variables can be further reduced. In

the most extreme case, phase 4 may result

in a single SMR indicator using a priori

weights for each of the categories and for

each of the sentiment dimensions, where

these weights can be determined together

with management.

Preparation of SMA-data time-series

using this methodology involved a

relatively large data processing effort.

First, historic data (2 to 3 years) on social-

media activities were required. Leading

social-media listening enterprises have

access to individual event-level social-

media databases covering at least a 2-year

history.

All this historic data should be pro-

cessed at the level of the individual event

(Tweet, Facebook entry, etc.) to assign

weights following the proposed method-

ology. This process can be automated, but

requires a significant set-up effort.

Subsequently, these variables are

entered into a market mix response model

to estimate the impact of these SMR on

sales.

Based on observed SMR time-series

trends and the sales responsiveness to the

specific SMR, companies have to decide

how to act upon the social-media pres-

sure. Practically and economically, it is not

possible to act indiscriminately upon

all raised SMR levels in all monitored

channels.

At this point, the ability to measure the

sales impact of SMR proposed in this arti-

cle comes into play. Depending on both

the SMR pressure level and the resulting

sales effect, a company may channel the

response into a variety of directions:

• Ignore: If the SMR pressure level and/

or the sales effects are small.

• Invigorate: If the SMR is positive and

has a positive sales effect. Invigoration

is performed by viral marketing—seed-

ing a company’s generated content into

networks and chat rooms or by commu-

nicating to opinion leaders on a given

positive topic, which conversations, in

turn, had caught imagination of people

on social-media networks.

• Re-invigorate: If a positive social-

media communication wave is losing its

momentum but still is very beneficial to

the company in terms of sales impact.

• Problem-Solve: If the negative senti-

ment SMR pressure is relatively large or

if it is rapidly growing even if it started

at a relatively small point. In this case,

the ability to measure the sales effect

(using the method proposed in this

study) is very important.

Reaction to the negative SMR could be

chosen with very different implications to

the cost and effort this action requires.

For example, if the negative social-media

pressure regarding Unilever personal-care

brands that use palm oil as an ingredient,

and the social-media campaign is about

rainforest destruction by large corpora-

tions, the reaction would depend on the

sales impact of this particular SMR.

If the sales impact is not large, the com-

pany could choose to react by communi-

cation and explanation of the situation

through proactive direct or viral social-

media communications or by dedicated

advertising activities. If the sales impact is

large, however, the company could choose

to change the entire ingredient-sourcing

strategy, which has much larger cost and

effort implications. The ability to meas-

ure sales impact of user generated social-

medial, therefore, is of vital importance to

companies.

APPLICATIoN 1: FLAT SCREEN TELEVISIoNSTo illustrate the methodology used to esti-

mate the impact of type 3 social-media

activities on brand sales, the authors report

on the products of a global manufacturer

of electronic consumer goods—flat-screen

televisions—with prominent market


THE POWER OF EvIL

exposure in a major European country, to

illustrate the methodology used to esti-

mate the impact of type 3 social-media

activities on brand sales.

The criterion variable in the model is

unit retail sales of the brand of flat-screen

televisions. Explanatory variables cover

the full spectrum of the manufacturer’s

marketing investments, product pricing,

and factors capturing the macroeconomic

situation at the time and seasonality. To

these traditional explanatory variables, the

authors add type 3 SMR variables derived

according to the methodology described

earlier.

Manufacturer’s marketing investments

include the following major channels:

• retail investment,

• television,

• Internet,

• print,

• radio, and

• outdoor.

The consumer confidence index is the key

macroeconomic variable that has a signifi-

cant impact on brand unit sales.

Within the category, the largest effect

captured in the seasonality variable is the

sales increase from late November to the

first week of January, capturing the typical

increase in television sales over the Christ-

mas/New Year holiday season.

The type 3 social-media impact is rep-

resented by three separate independent

SMR variables:

• SMR with positive sentiment,

• SMR with neutral sentiment, and

• SMR with negative sentiment.

A multivariate time-series analysis was

performed to estimate the relationship

between the unit brand sales and explana-

tory variables (as described earlier): mar-

keting activities, social-media activities,

macroeconomic factors, and seasonality

(Table 2).

The marketing investments variables

are measured as follows (See Table 2):

• Retail: Weekly euro investment in retail

advertising, mostly circulars (Source:

company internal financial records);

• Internet: Weekly euro investment in

Internet advertising—more precisely,

Internet display advertising plus invest-

ment in paid search (Source: media

agency);

• Print: Weekly euro investment in print

advertising, specifically magazines and

newspapers (Source: media agency);

• Television: Weekly euro investment in

television advertising (Source: media

agency);

• Radio: Weekly euro investment in radio

advertising (Source: media agency);

• Outdoor: Weekly euro investment in

outdoor advertising, including bill-

boards, posters, etc. (Source: media

agency); and

• Price: Average product (flat-screen tel-

evision set) unit price paid by the con-

sumer in Euros. (Source: retail panel

data—information provided by third-

party supplier that produces these data

based on in-store sales scanning data,

including online sales.)

Generally, rising consumer confidence is

a precursor to higher consumer spending,

and consumer surveys produced a macro-

economic consumer confidence index

variable. For euro-zone countries, GfK

provided a consumer confidence index

that measured the level of confidence

among households in economic perfor-

mance in each country. The GfK consumer

confidence barometer is closely watched

by economists and politicians. The index

is produced monthly based on 2,000 con-

sumer surveys in each euro-zone country.

The model in the current study is

weekly, and the authors used the same

monthly consumer confidence index in

each week during each month.

TABLE 2Descriptive statistics of Model variables

Mean unitsMinimum units

Maximum units

Standard Deviation

sales Units 9,316 3,672 45,832 6,937

Retail 486,748 31,235 1,671,086 369,207

Internet 445,222 3,374 898,531 292,601

Print 157,902 0 1,397,164 350,366

Tv 411,208 0 2,933,487 662,219

Radio 41,941 0 455,241 115,273

Outdoor 12,724 0 450,266 66,113

Disposable Income 8,822 8,666 9,135 117

ccI 45 26 61 11

Price 739 600 854 52

Positive social Media 301 0 1,800 401

neutral social Media 91 0 984 146

negative social Media –68 –589 0 316


THE POWER OF EvIL

Social-media variables (positive, neutral,

and negative) are type 3 SMR-calculated,

according to the methodology presented

in the previous section (See Table 2).

In all likelihood, the relatively high

correlations between positive and neu-

tral social media were caused by the fact

that social-media “chatter” often clusters

around the same events (new product

launch, big advertising campaign, etc.).

Some consumers give positive comments,

some neutral. The cause events of this

social-media chatter are frequently the

same and, therefore, result in higher cor-

relation between these variables.

Negative social-media comments usu-

ally trigger additional discussions in social

networks. This increases general chatter

on the subject. This phenomenon could

be one explanation for the high correla-

tion between negative and neutral social

media.

The analysis period in the current study

included the economic downturn from

late 2008 to 2009, during which consumer

confidence fell dramatically and retailers

tried to maintain sales levels with signifi-

cant price reductions—considerations that

help explain the observed high correlation

between the consumer confidence index

and price.

The authors are less certain as to why,

in this example, positive social-media had

a relatively high correlation with radio.

It could be just a coincidence—a com-

pany’s radio advertising campaign aired

during a period when increased positive

social-media activity took place. Such

correlations sometimes are observed dur-

ing advertising-supported new product

launches (Table 3).

The variables correlation matrix indi-

cated no major multicollinearity problems

for the estimation of the parameters of the

response function. A multivariate time-

series regression was used to estimate the

relationship between sales and independ-

ent variables (Equation 1; Corstjens et al.,

2011):

Sales market

marketing

t ti

i i

jj j

j j

= +

+

∑∑

β β

β λ ω

0

( ) (1)

where

• market: macroeconomic market vari-

able—consumer confidence index,

• marketing: product-marketing activity

and social-media rating (positive, nega-

tive, and neutral):

marketing marketingji

ii

j t ijλ λ= ∑ −( )

The macroeconomic market variable is

modeled as having a linear effect on sales

within the relevant range of the data obser-

vations. Usually, there is no major varia-

tion in values of macroeconomic variable

over the relevant time period (See Table 2).

The non-linear impact of marketing vari-

ables is captured by two distinct effects:

• dynamic effects (lambda), and

• diminishing returns to scale (omega).

Dynamic effects refer to the fact that many

marketing activities still influence sales

after the activity itself has been completed;

consumers remember the marketing activ-

ity and act upon it in their future purchase

decisions. This effect (lambda) is taken into

TABLE 3Model Independent variable correlation Matrix

CCI PriceNegative SM

Positive SM

Neutral SM Radio Print Retail outdoor Internet TV

ccI 1.00 0.77 –0.32 –0.44 –0.44 –0.32 –0.19 –0.19 0.21 0.56 0.24

Price 1.00 –0.17 –0.28 –0.29 –0.22 –0.22 0.03 0.11 0.31 0.13

negative_social_Media 1.00 0.49 0.89 0.38 0.00 0.03 –0.04 –0.27 –0.12

Positive_social_Media 1.00 0.51 0.57 0.19 0.10 –0.10 –0.16 –0.13

neutral_social_Media 1.00 0.31 –0.08 0.11 –0.02 –0.34 –0.23

Radio 1.00 0.35 0.22 –0.06 0.08 0.07

Print 1.00 0.51 –0.06 0.41 0.06

Retail 1.00 0.06 0.28 –0.19

Outdoor 1.00 0.09 –0.13

Internet 1.00 0.20

Tv 0.65

Note: The variables correlation matrix indicated no major multicollinearity problems for the estimation of the parameters of the response function.


THE POWER OF EvIL

account by the traditional Koyck transfor-

mation approach.

Sales typically respond non-linearly

to the intensity of marketing activity: too

much investment pushes sales into dimin-

ishing returns (omega).

The authors used a stochastic intercept

βt, which is estimated over time using the

State Space or the Dynamic Linear Model-

ling approach (Kalman, 1969; Sriram and

Kalwani, 2007). This estimation process of

the stochastic intercept βt extracts most out

of the available data by accounting for the

parameters that influenced sales that were

not explicitly included in the parameteri-

zation of the market response function.

The authors also tested for “halo” and

“cannibalisation” effects: whether adver-

tising of other products under the same

brand name influenced sales of the focal

product. In the case of the current study,

neither of these effects was statistically sig-

nificant in the model.

The estimation of Equation 1 requires

simultaneous estimation of multiple

parameters:

• parameters for dynamic effects,

• parameters for diminishing returns, and

• estimates for the stochastic intercept.

Given the limited number of observa-

tions, the authors used a combination of

simulation and estimation to calibrate the

response function. The estimation pro-

cedure systematically tested the entire

multi-dimensional surface of potential

transformation parameters with small

regular increments. Simultaneously, the

procedure then estimated the linear coeffi-

cient associated with each marketing vari-

able (beta) and the stochastic intercept βt

using the State Space technique. This com-

plex tailor-made estimation methodology

requires high computer processing power

and usually takes multiple hours.

The methodology in the current study

automatically and systematically elimi-

nated all potential solutions that did not

meet face validity: for example, marketing

drivers should have a positive relationship

with sales; price should have a negative

relationship with sales; and sales should

be non-zero when all marketing activities

are zero.

For those solutions that passed the face-

validity test, a battery of statistical tests

were used to select the best solutions.

These included tests for autocorrelation,

statistical significance of the estimated

parameters, and the overall goodness of fit

(Table 4) provides key statistical results of

the selected estimation of the model. (For

a more detailed explanation of the estima-

tion procedure, see Corstjens et al., 2011.)

The model results were satisfactory in

terms of lack of autocorrelation and heter-

oskadasticity, and all of the coefficients for

the explanatory variables had statistical

significance at reasonable significance

level.

Results of the model in terms of the per-

centage contribution of each explanatory

variable to the modeled unit product sales,

together with p-values of each explanatory

variable β coefficient estimate are shown

in Table 5.

Of 12 explanatory variables, 11 proved

to be statistically significant in the model.

TABLE 4key statistical Results of the selected Estimation of the current ModelR-square 93%

Adjusted R-square 90%

DW 1.85

normality p-value 0.06

Q-stat p-value 0.05

TABLE 5Model Results and statistics for Each variable

Lambda (%) omega

Contri-bution (000)

Contri-bution (%) p-value VIF Lag

Base 1,383 51.9 0.002 1.2

seasonality 84 3.2 <0.0001 1.4

Consumer Confidence Index 19 0.7 0.002 3.5

Price –49 –1.8 0.004 4.0

negative social Media 20 0.2 –184 –6.9 0.050 2.1 1

Positive social Media 20 0.8 186 7.0 0.018 1.4 0

neutral social Media 10 0.8 14 0.5 <0.0001 1.9 1

Radio 50 0.5 153 5.7 0.049 2.2 3

Print 0 0.8 184 6.9 0.001 2.7 1

Retail 0 0.8 307 11.5 0.045 2.1 0

Outdoor 0 0.2 71 2.7 0.016 1.1 0

Internet 20 0.8 311 11.6 0.050 2.0 1

Tv 60 0.8 188 7.0 0.016 2.3 1

Note: Results of the model in terms of the percentage contribution of each explanatory variable to the modeled unit product sales, together with p-values of each explanatory variable β coefficient estimate.


THE POWER OF EvIL

(Disposable Income was not statistically

significant.) Analysis was a relatively

slow-moving variable and usually lagged

the weekly “action” in the marketplace.

During the economic downturn of

2008–2009, consumers became cautious

about making relatively large purchases

(such as flat-screen televisions) much

faster than the decline of their real dispos-

able incomes that showed up in statistics

would have suggested. The consumer

confidence index, therefore, picked up this

aspect of consumer behavior better than

the disposable income variable.

For established products, the base typi-

cally accounts for 80 percent to 90 per-

cent (or even more) of the variation in

volume. The situation usually is differ-

ent for new innovative products (in the

current study case, flat-screen televisions

with light emitting diodes technology that

were introduced just before the modeling

period). The new technology generated a

much better television picture, consumes

significantly less power, and is much flat-

ter and lighter than previous televisions.

In this study, the brand owner’s com-

munication of these features was a critical

part of the appeal of the new technology

by consumers. When consumers still did

not know anything about the new product,

both the purchase volume and the base

were zero. Once the brand owner started

to communicate benefits of the new prod-

uct, the sales started picking up—in fact,

most of the increase could be attributed

to being incremental-communications-

driven volume.

Only after some time was the base

established at levels close to more mature

products. Therefore, new-products mar-

keting (including the push in retail chan-

nels, which is part of the incremental in

the current example) generated a portion

of sales larger than for mature products.

New products usually also generate more

social-media chatter—one reason, in fact,

why the authors chose this product cat-

egory for this illustration. Also, the impact

of social media (not only company's)

advertising was included in the incremen-

tal sales performance, making the increase

look relatively large.

Social media were modeled as separate

effects from positive, negative, and neutral

sentiments. As shown by p-values statis-

tics, all three types of social-media vari-

ables were statistically significant in the

model (See Table 5).

Positive SMR incrementally generated

7.0 percent sales of the product, and the

confidence level of this estimate is very

strong. Neutral SMR showed 0.5 percent

incremental sales generated, with very

strong p-value. Negative SMR is shown

to be causing a –6.9 percent sales decline,

with statistically significant presence in

the model.

It is important to note that the relative

SMR pressures for positive, neutral, and

negative sentiments over the analysis

period were very different. The authors

observed 14.8 k positive, 4.5 k neutral and

only 3.3 k negative SMR pressures over the

period of analysis (Table 6).

At the same time, the respective impact

on sales of these three sentiments was +7.0

percent, +0.5 percent, and –6.9 percent,

respectively. Each thousand negative-

sentiment SMR generated 4.42 times more

negative sales impact than a thousand

positive-sentiment SMR. Neutral SMR

generated even less impact from each

SMR—only one-fourth of the level gener-

ated by the positive sentiment per unit of

SMR (Figure 4).

The authors also estimated the model

without social-media variables (Table 7).

TABLE 6sMR Market Pressures and Incremental sales per Unit of sMR Relative to the Positive sMR Impact

Positive SM Neutral SM Negative SM

Total social Media Ratings (sMR) 14,762 4,457 3,320

sales uplift generated (% of total sales) 7.0% 0.5% –6.9%

sales uplift per thousand sMR 0.47% 0.12% –2.08%

Relative uplift per sMR point (Positive = 1) 1.00 0.25 –4.42

Social Media Rating (SMR)

Time

PositiveNeutralNegative

Figure 4 sMR of All Three sentiments over Time


THE POWER OF EvIL

As the results demonstrate, with-

out social-media variables, the model’s

explanatory power was reduced. Most

notably, the p-value of television advertis-

ing increased from 0.016 if social-media

variables were included, to the 0.061 when

social-media variables are excluded from

the set of explanatory variables.

Without social-media variables, the

robustness of the model was reduced: the

absence of the significant sales stimuli

data regarding social media made the

model incomplete.

APPLICATIoN 2: BRoADBAND SERVICESTo further test the efficacy and impact

of social media, the authors considered

the performance of a broadband-services

provider in a major European country. To

examine the robustness of their model, the

authors used the same approach in this

application as they did in the flat-screen-

television application. The analysis in the

broadband services industry was based on

60 weekly observations.

The criterion variable in the model is

customer acquisitions (i.e., the number of

broadband service customers who sub-

scribe or renew the broadband services

provided by this particular company).

Explanatory variables cover the full

spectrum of the manufacturer’s marketing

investments—both “traditional” televi-

sion advertising and print advertising, and

“digital”—online displays and affiliates.

Explanatory variables also cover product

pricing, promotions, and seasonality.

To these traditional explanatory varia-

bles, the authors add type 3 SMR variables

derived according to the methodology

described in Methodology section, using

three separate independent variables:

SMR with positive sentiment, SMR with

neutral sentiment, and SMR with negative

sentiment

A multivariate time-series analysis,

similar to the one used in the flat-screen

television application, is performed to esti-

mate the relationship between customer

acquisitions and explanatory variables,

as described earlier: marketing activities,

social-media activities, pricing, promo-

tions, and seasonality.

Table 2 summarizes the descriptive sta-

tistics of the variables used in the model.

The dependent variable (customer acqui-

sitions) is the weekly number of customers

subscribing to the company’s broadband

product. Acquisition numbers also include

subscription renewals of customers who

previously had used this service. The mar-

keting investments variables are measured

as follows:

• Television: Weekly GRP in television

advertising (Source: media agency);

• Print: Weekly impressions in print

advertising: general content maga-

zines and computer magazines (Source:

media agency);

• Online Display: Weekly online display

advertising impressions (Source: digital

agency);

• Online Affiliates: Weekly online affili-

ates impressions. Affiliates is a “rented”

advertising space (banners, etc.) on

other companies’ Web sites; the adver-

tiser pays the Web site owner for each

click on the advertisement (Source: digi-

tal agency);

• Price: Average subscription price paid

by the consumer in euros (Source: com-

pany’s internal records); and

• Promotions: Weekly promotions:

reduced price subscription to the ser-

vice. In the current sample, the authors

had three separate weeks of such weekly

promotional events (Source: company’s

internal records).

Social-media variables (positive, neutral,

and negative) are type 3 SMR calculated

according to the methodology presented

in the previous section (Table 8).

The relatively high negative correla-

tion of –0.63 between online display and

online affiliates is caused by the fact

that these two activities have been

TABLE 7Model Results with social-Media variables Removed

TypeLambda (%) omega Lag

Contri-bution (000)

Contri-bution (%) p-value VIF

Base 1,632 51.7 0.000 1.3

seasonality 83 2.6 <0.0001 1.4

Consumer Confidence Index 38 1.2 0.000 3.5

Price –45 –1.4 0.001 2.5

Radio 50 0.5 3 118 3.7 0.050 2.3

Print 0 0.8 1 168 5.3 0.005 2.3

Retail 0 0.8 0 274 8.7 0.030 1.9

Outdoor 0 0.2 0 65 2.0 0.050 1.1

Internet 20 0.8 1 403 12.8 0.020 1.7

Tv 70 0.6 1 422 13.4 0.023 3.5

Note: R-square 85%; Adjusted R-square 83%; DW 1.63; Normality p-value 0.01; Q-stat p-value 0.05


THE POWER OF EvIL

planned interchangeably with each other.

Display and affiliates by nature are very

similar online activities and, when one

type by the marketing planners is used,

the other is not, and vice versa. No other

major correlations surfaced between

the explanatory variables in the model

(Table 9).

The same type multivariate time-series

regression as in the flat-screen-television

application was used to estimate the rela-

tionship between sales and independent

variables (Equation 1), and provide key

statistical results of the selected estimation

of the model (Table 10).

The model results were satisfactory in

terms of lack of autocorrelation and hetero-

skadasticity, and all of the coefficients

for the explanatory variables had statis-

tical significance at a reasonable signifi-

cance level.

Results of the model in terms of the per-

centage contribution of each explanatory

TABLE 8Descriptive statistics of Model variables; Broadband Application

Mean Minimum MaximumStandard Deviation

Acquisitions 12,904 9,867 24,017 2,465

Tv 85 0 263 68

Print 3.88 0 18.01 3.88

Display 96.9 0 436.1 107.9

Affiliates 8.53 0 38.54 10.86

Price 18.44 15.99 23.41 1.97

Promotions 0.02 0 1.00 0.13

Positive social Media 299 120 520 107

neutral social Media 818 160 2,035 472

negative social Media –1,097 –280 –1,720 –287

Note: Social-media variables (positive, neutral, and negative) are Type 3 SMR calculated according to the methodology presented in the previous section.

TABLE 9The variables correlation Matrix, Indicating no Major Multicollinearity Problems for the Estimation of the Parameters of the Response Function

Acquisitions TV Print Display Affiliates Price Promotions

Positive Social Media

Neutral Social Media

Negative Social Media

Acquisitions 1.00 0.43 0.29 0.27 0.48 –0.18 0.19 –0.28 0.26 –0.14

Tv 1.00 –0.24 –0.20 0.36 –0.06 –0.10 –0.19 0.10 –0.04

Print 1.00 0.03 –0.06 0.10 0.05 0.19 0.11 –0.18

Display 1.00 –0.63 –0.32 0.05 0.10 0.33 –0.37

Affiliates 1.00 0.55 –0.08 –0.34 0.22 –0.11

Price 1.00 –0.02 –0.14 0.15 –0.11

Promotions 1.00 0.08 0.12 –0.15

Positive social Media 1.00 0.26 0.01

neutral social Media 1.00 –0.01

negative social Media 1.00

Note: The variables correlation matrix, indicating no major multicollinearity problems for the estimation of the parameters of the response function.

TABLE 10key Model statistics: Broadband ApplicationR-square 93%

Adjusted R-square 90%

DW 2.01

normality p-value 0.05

Q-stat p-value 0.05


THE POWER OF EvIL

variable to the modeled unit product sales,

together with p-values of each explanatory

variable β coefficient estimate, are shown

in Table 11.

In the broadband-services provider

example, the authors built a model based

on the number of new subscriptions and

service renewals, not on the total num-

ber of subscribers using the service every

week. The service was based on a single

duration, 24-month contract. The sup-

plier providing this service was an entrant

carving out market share in an existing

market with aggressive pricing and adver-

tising. This strategy may explain why

the base was at a much lower level com-

pared to typically observed values in other

industries/companies.

Moreover, broadband service is a com-

modity and does not command high cus-

tomer loyalty levels. The results of the

current study showed that the aggressive

pricing and advertising pressure had a

strong impact on securing new subscrip-

tions and renewals. Although price had

a negative impact on new subscriptions

and renewals, the contribution of the price

variable in the current model was positive

because, over the authors’ period of obser-

vation, prices came down, thereby having

a positive effect on the new subscriptions

and renewals.

Of the tested three types of social-media

messages, only the negative-sentiment

social media was significant in the model.

Broadband service—like electricity and

gas—is a low-involvement product and

causes customer reaction only if some-

thing is not working as expected. Using

the electricity analogy: customers do not

Tweet to their followers their excitement

if they turn on an electricity switch and a

light comes on. If the light does not come

on, however, the customer is frustrated

and will complain bitterly.

Similar effects are in play in our

broadband service application. When

something is not working properly, frus-

tration is communicated via social media

that results in negative impact on poten-

tial customer acquisitions among the peo-

ple reading these negative messages.

The authors also estimated the model

without social-media variables across the

broadband application (Table 12).

Without social-media variables, the

current model’s explanatory power was

reduced. Estimates of the contributions of

marketing vehicles also changed; especially

estimated contributions of pricing, televi-

sion, and base change compared to a model

wherein the social-media component is inte-

grated amongst the explanatory variables.

TABLE 11Model Results and statistics for Each variable; Broadband Application

Lambda (%) omega

Contri bution (000)

Contri-bution (%) p-value Lag

Base 235 29.1 <0.0001

seasonality –13 –1.6 0.0052

Price 230 28.5 <0.0001 0

Promotion 0 0.5 16 2.0 0.0017 0

negative social Media 35 0.3 –82 –10.2 0.0152 0

Tv 87 0.7 197 24.4 0.0004 1

Print 45 0.7 58 7.2 0.0511 2

Online Display 10 0.3 55 6.8 0.0004 0

Online Affiliates 3 0.4 111 13.8 <0.0001 0

Note: Results of the model in terms of the percentage contribution of each explanatory variable to the modelled unit product sales, together with p-values of each explanatory variable β coefficient estimate.

TABLE 12Model Results with social-Media variables Removed; Broadband Application

Lambda (%) omega

Contribution (000)

Contri-bution (%) p-value Lag

Base 220 27.3 <0.0001

seasonality –11 –1.4 0.0259

Price 185 22.9 <0.0001 0

Promotion 0 0.5 15 1.8 0.0022 0

Tv 88 0.7 186 23.1 0.0028 1

Print 45 0.7 60 7.5 0.0810 2

Online Display 12 0.3 49 6.1 0.0019 0

Online Affiliates 4 0.5 102 12.6 <0.0001 0

Note: R-square 91%; Adjusted R-square 87%; DW 1.79; Normality p-value 0.07; Q-stat p-value 0.06


THE POWER OF EvIL

From the results of the current study,

the authors observed that negative

social media seemed to reduce sub-

stantially (10 percent) the company’s

new-subscriptions and renewals rates.

Clearly, the services supplier needed

to address this issue. More specifically,

it needs to investigate what the nega-

tive social-media contributors complain

about. And, based on this assessment,

it needs to plan how to fix the problems

that generate this negative chatter and esti-

mate how much this adjustment will cost.

MANAGERIAL IMPLICATIoNSAs shown by two applications from

very different industries—flat-screen tel-

evisions and broadband services—type 3

social media have a distinct and measur-

able impact on brand sales.

Managing the negative social-media

sentiment is crucial for many industries.

Alternatively, positive social-media senti-

ments and threads—if boosted and rein-

vigorated—can have a significant positive

impact on sales and profits.

In summary, the current model enabled

the authors to distinguish three major

brand sales components:

• sales not directly driven by market

activities: these consist of base sales,

seasonality, consumer confidence, and

other exogenous factors depending on

the industry;

• paid-for media and marketing stimuli:

television, Internet, outdoor, retail,

radio, price, and other endogenous

factors depending on the industry;

and

• type 3 social media—positive, neutral,

and negative social media—have a sig-

nificant effect on brand sales.

The effect of social media on brand sales

depends on the type of product category and

the competitive landscape of the industry.

In the television application, the posi-

tive social-media sentiments contributed

incremental 7 percent to sales. At the

same time, negative social media caused

a –6.9-percent sales decrease. As expected,

neutral sentiment social media contrib-

uted a much smaller 0.5 percent to sales.

These three social-media types, how-

ever, had different levels of SMR pressure

in the marketplace. Per comparable unit

of SMR pressure, negative social media

caused 4.4 times more sales decline than

a unit of positive SMR. Neutral social

media, conversely, had only one-fourth of

the sales contribution (per unit of SMR)

delivered by a unit of positive SMR.

These results confirm previous findings

about the asymmetric impact of positive

and negative messages and the dominant

impact of negative messages (Reichelt,

2003; Ferguson, 2005; Park and Lee, 2009).

The relatively higher impact of negative-

sentiment social media also was observed

in the broadband-services application,

showing a major detrimental effect of 10

percent to new subscriptions and renew-

als. In this case, positive and neutral social

media did not have a detectable effect on

business outcomes.

In the authors’ examples, the type 3

social-media effects had lesser effect on

brand sales than the exogenous factors or

the paid-for marketing activities. With-

out a doubt, the impact of social media

will grow as more and more customers

spend more and more time in social-media

environments.

Neglecting the type 3 social-media

impact on brand sales is already a major

shortcoming, and the problem of such

neglect will grow in future.

Social media have given customers a

voice. These customers are no longer just

“targets”—a concept that still prevails

among marketing practitioners—for com-

panies to talk down to. Successful compa-

nies are changing their one-way targeting

approach to two-way communications.

In an era when an unhappy society can

use social media to topple a government,

brand owners should be ready for the

social media–induced paradigm shift in

marketing.

When building a social-media cap-

ability, the first step for a company is to

understand what impact social media

have had on product/brand sales so that

company managers know where out-

bound social-media activities should be

concentrated.

It is no easy task to build an outbound

social-media capability. In traditional

one-way customer communication, a

30-second advertisement is produced

in a multi-month cycle by teams of

creative people working on the con-

tent, with multiple approvals required at

different stages. With social media,

companies need large teams of skilled

people to get involved on a daily basis

in a vast volume of customer communi-

cations. There is no time for crafting and

refining the message or for multi-layer

approvals.

A company’s ability to measure the

impact of social media on sales, therefore,

will become vital for marshalling scarce

and expensive social-media management

The effect of social media on brand sales

depends on the type of product category and

the competitive landscape of the industry.


THE POWER OF EvIL

resources to the right products and con-

versation topics.

A word of caution: as with any new

approach, the early user has to be sensi-

tive to a number of potential limitations in

using the proposed methodology to iden-

tify the sales effect of social media. In the

current study:

• If the sales effect does not exist, it can-

not be measured. Intuitively, it would

seem that, for low-involvement prod-

uct category brands such as detergents,

household products, some commodity

food categories, and the like, there is not

enough consumer emotional involve-

ment to generate significant chatter in

social media to distinguish a detectable

effect on sales.

The authors’ examination of social

media’s impact on a broadband-services

application, however, showed that even

in some low-involvement product cat-

egories, the impact of social media—

especially negative messages—should

be analyzed carefully.

• Few existing social-media listening

engines have appropriate linguistic

algorithms to divide social-media mes-

sages correctly into positive, neutral,

and negative. The large number of mes-

sages that need to be classified means

that manual filtering can be time con-

suming and expensive.

Automatic message filtering by senti-

ment is a relatively complex area. Ran-

dom checks of social-media messages

filtered as negative by automated filtering

methods sometimes reveal that there

could be a word in the message with a

negative connotation, but this negativ-

ity is not directed toward the brand, and

the message is wrongly classified as a

negative sentiment. Therefore, state-of-

the-art linguistic algorithms should

be used to minimize such wrong

classifications.

Linguistic filtering problems could be

caused by names with identical spelling

but different meaning. For example, the

authors’ attempt to filter social-media

messages regarding the credit-card com-

pany Visa ran into difficulties because

“visa” also means a document to allow

entry into a foreign country.

• There is also a challenge to assess accu-

rately the reach of each message in

social media.

Social-media listening engines know how

many friends on Facebook or how many

followers on Twitter each originator of a

message has. Not all friends and follow-

ers, however, read all messages. This dif-

ficulty is overcome by simultaneously

assessing what percentage of friends or

followers actually reads the message.

This assessment is performed by using

online panel data in parallel with social-

media listening. One should check on the

online panel which percentage of friends

and/or followers is assessing the URL

where the message is published. This

percentage from a representative sample

of the entire population can be used to

estimate the actual reach of social-media

communications.

Leading companies are setting up dedi-

cated capabilities to monitor and actively

manage social-media conversations. To

be effective means understanding which

social-media activities to respond to from

the multi-million activities out there, and

developing triage capabilities to deter-

mine the relevant response: ignore; stop;

perform damage limitation; boost; reinvig-

orate; and so on.

The social-media impact-measurement

method described in this study is able to

account for the sales impact of different

social-media categories and topics such

as pricing, product quality, service levels,

ethical sourcing, etc. By better understand-

ing the sales impact of each social-media

category/topic, companies will be able to

direct social-media management resources

toward those with the greatest impact on

sales and profits.

This ability also is vital for the measure-

ment of the impact of a company’s out-

going social-media messages to enable

continuous improvement of its social-

media management capability.

maRceL coRstJens is the Unilever Professor of Marketing

at InsEAD. His research focuses on economics and

marketing science modeling approaches in the areas of

communication, distribution channels, and retailing.

andRis umbLiJs is senior expert at Mckinsey. Before that,

he was at Accenture, where he led development of

marketing analytics offerings and managed key client

projects in fact-based marketing-decision support across

multiple industries.

When building a social-media capability, the first

step for a company is to understand what impact

social media have had on product/brand sales so

that company managers know where outbound

social-media activities should be concentrated.


THE POWER OF EvIL

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