New Products–1

New Product Models

Pre-Test Market Models

Product Design using Conjoint Analysis

Forecasting with Diffusion Models

New Products–2

New Product Decision Models

Product design using conjoint analysis

Forecasting the pattern of new product adoptions (Bass Model)

Forecasting market share for new products in established categories (Assessor model)

New Products–3

The New Product Development Process

DesignIdentifying customer needs Sales forecasting

Product positioning EngineeringMarketing mix assessment Segmentation

Opportunity IdentificationMarket definitionIdea generation

TestingAdvertising & product testing

Pretest & prelaunch forecastingTest marketing

IntroductionLaunch planning

Tracking the launch

Life-Cycle ManagementMarket response analysis & fine tuning the

marketing mix; Competitor monitoring & defenseInnovation at maturity

Go No

Go No

Go No

Go No

RepositionHarvest

New Products–4

Value of Concept Testing:TV Series

2,000 Ideas

100 Scripts

20 Pilots

5 Scheduled for prime time

1 Success (?)

New Products–5

New Product Testing from a Customer’s Perspective

1) Concept testing

2) Alpha/Beta/In-house testing

3) Laboratory test market

4) Full-scale test market

New Products–6

Questions Answered in Concept Testing

Is the concept worth pursuing further?

Which attributes are more important (provide more value) to the consumers?

Which segments are worth pursuing with this concept?

What amount of cannibalization of current products might occur?

New Products–7

Questions Answered in Alpha/Beta Testing

Are the product features/benefits important in use?

What problems are encountered in the use of the product?

What are the costs in use for our consumers?

Does the product perform better than the competing brands?

New Products–8

Questions Answered in Laboratory Test Markets

What is the potential number of triers of the product?

What is the potential repeat?

What is the potential frequency of purchase?

What is the projected sales/share for new product in the first two years of introduction?

What should we do to improve the product’s chances of market success?

New Products–9

Questions Answered in Test Markets

What is the level of awareness generated by the marketing program?

What trial, repeat, and usage rates are generated by the program?

What changes in attitude are accomplished by the program?

What levels of marketing expenditures are optimal?

What specific levels of advertising, packaging, distribution, etc. will work?

What should we do to improve the product’s chances of market success?

New Products–10

Pretest Market Models

Objective

Forecast sales/share for new product before a real test market or product launch

Conceptual model

Awareness Availability Trial Repeat

Commercial pre-test market services Yankelovich, Skelly, and White

Bases

Assessor

New Products–11

ASSESSOR Model

Objectives

Predict new product’s long-term market share, and sales volume over time

Estimate the sources of the new product’s share, which includes “cannibalization” of the firm’s existing products, and the “draw” from competitor brands

Generate diagnostics to improve the product and its marketing program

Evaluate impact of alternative marketing mix elements such as price, package, etc.

New Products–12

Overview of ASSESSOR Modeling Procedure

Management Input(Positioning Strategy)

(Marketing Plan)

ReconcileOutputs

Draw &Cannibalization

Estimates DiagnosticsUnit SalesVolume

Preference Model

Trial &Repeat Model

Brand Share Prediction

Consumer Research Input(Laboratory Measures)(Post-Usage Measures)

New Products–13

Overview of ASSESSOR Measurements

Design Procedure Measurement

O1 Respondent screening and Criteria for target-group identification recruitment (personal interview) (eg, product-class usage)

O2 Pre-measurement for established Composition of ‘relevant set’ of brands (self-administrated established brands, attribute weights questionnaire) and ratings, and preferences

X1 Exposure to advertising for established brands and new brands

[O3] Measurement of reactions to the Optional, e.g. likability and advertising materials (self- believability ratings of advertising administered questionnaire) materials

X2 Simulated shopping trip and exposure to display of new and established brands

O4 Purchase opportunity (choice recorded Brand(s) purchased by research personnel)

X3 Home use/consumption of new brand

O5 Post-usage measurement (telephone New-brand usage rate, satisfaction ratings, and repeat-purchase propensity; attribute ratings and preferences for ‘relevant set’ of established brands plus the new brand

O = Measurement; X = Advertsing or product exposure

New Products–14

Trial/Repeat Model

Market share for new product

Mn = T R W

where:

T =long-run cumulative trial rate (estimated from measurement at O4)

R =long-run repeat rate (estimated from measurements at O5)

W=relative usage rate, with w = 1 being the average market usage rate.

New Products–15

Trial Model

T = FKD + CU – (FKD) (CU)

where:

F =long-run probability of trial given 100% awareness and 100% distribution (from O4)

K =long-run probability of awareness (from managerial judgment)

D=long-run probability of product availability where target segment shops (managerial judgment and experience)

C =probability of consumer receiving sample (Managerial judgment)

U=probability that consumer who receives a product will use it (from managerial judgment and past experience)

New Products–16

Repeat Model

Obtained as long-run equilibrium of the switching matrix estimated from (O2 and O5):

Time (t+1)New Pr. Other

New Pr. p(nn) p(no)Time t

Other p(on) p(oo)

p(.) are probabilities of switching where

p(nn) + p(no) = 1.0; p(on) + p(oo) = 1.0

Long-run repeat given by:

p(on) r = ––––––––––––––

1 + p(on) – p(nn)

New Products–17

Preference Model: Purchase Probabilities Before New Product Use

where:

Vij=Preference rating from product j by participant i

Lij =Probability that participant i will purchase product j

Ri =Products that participant i will consider for purchase (Relevant set)

b =An index which determines how strongly preference for a product will translate to choice of that product (typical range: 1.5–3.0)

(Vij)b

Lij = ––––––––Ri

(Vik)b

k=1

New Products–18

Preference Model: Purchase Probabilities After New Product Use

where:

L´it =Choice probability of product j after participant i has had an opportunity to try the new product

b =index obtained earlier

Then, market share for new product:

L´in M´n = En –––

I N

n =index for new product

En =proportion of participants who include new product in their relevant sets

N =number of respondents

(Vij)b

L´ij = –––––––––––––––––Ri

(Vin)b + (Vik)b

k=1

New Products–19

Estimating Cannibalizationand Draw

Partition the group of participants into two: those who include new product in their consideration sets, and those who don’t. The weighted pre- and post- market shares are then given by:

Lin Mj = –––

I N

L´in L´in M´j = En ––– + (1 – En) –––

I N I N

Then the market share drawn by the new product from each of the existing products is given by:

Dj = Mj – M´j

New Products–20

Example: Preference Ratings

Vij (Pre-use) V´ij (Post-use)

Customer B1 B2 B3 B4 B1 B2 B3 B4 New Product

1 0.1 0.0 4.9 3.7 0.1 0.0 2.6 1.7 0.2

2 1.5 0.7 3.0 0.0 1.6 0.6 0.6 0.0 3.1

3 2.5 2.9 0.0 0.0 2.3 1.4 0.0 0.0 2.3

4 3.1 3.4 0.0 0.0 3.3 3.4 0.0 0.0 0.7

5 0.0 1.3 0.0 0.0 0.0 1.2 0.0 0.0 0.0

6 4.1 0.0 0.0 0.0 4.3 0.0 0.0 0.0 2.1

7 0.4 2.1 0.0 2.9 0.4 2.1 0.0 1.6 0.1

8 0.6 0.2 0.0 0.0 0.6 0.2 0.0 0.0 5.0

9 4.8 2.4 0.0 0.0 5.0 2.2 0.0 0.0 0.3

10 0.7 0.0 4.9 0.0 0.7 0.0 3.4 0.0 0.9

New Products–21

Choice Probabilities

Lij (Pre-use) L´ij (Post-use)Customer B1 B2 B3 B4 B1 B2 B3 B4 New Product

1 0.00 0.00 0.63 0.37 0.00 0.00 0.69 0.31 0.002 0.20 0.05 0.75 0.00 0.21 0.03 0.03 0.00 0.733 0.43 0.57 0.00 0.00 0.42 0.16 0.00 0.00 0.424 0.46 0.54 0.00 0.00 0.47 0.50 0.00 0.00 0.035 0.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.006 1.00 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.207 0.01 0.35 0.00 0.64 0.03 0.61 0.00 0.36 0.008 0.89 0.11 0.00 0.00 0.02 0.00 0.00 0.00 0.989 0.79 0.21 0.00 0.00 0.82 0.18 0.00 0.00 0.00

10 0.02 0.00 0.98 0.00 0.04 0.00 0.89 0.00 0.07

Unweighted market share (%) 38.0 28.3 23.6 10.1 28.1 24.8 16.1 6.7 24.3New product’s draw from each brand (Unweighted %) 9.9 3.5 7.5 3.4 New product’s draw from each brand (Weighted by En in %) 2.0 0.7 1.5 0.7

New Products–22

Assessor Trial & Repeat Model

Market Share Due to Advertising

•Max trial with unlimited Ad•Ad$ for 50% max. trial•Actual Ad $

•Max awareness with unlimited Ad•Ad $ for 50% max. awareness•Actual Ad $

% buying brand in simulated shopping

Awarenessestimate

Distributionestimate (Agree)

Switchback rate ofnon-purchasers

Repurchase rate of simulation

purchasers

% making first purchaseGIVEN awareness &

availability0.23

Prob. of awareness0.70

Prob. of availability0.85

Prob. of switchingTO brand

0.16

Prob. of repurchaseof brand

0.60

% making first purchase due to

advertising0.137

Retention rateGIVEN trial

for ad purchasers0.286

Response Mode Manual Mode

Long-term market share

from advertising0.39

Source: Thomas Burnham, University of Texas at Austin

New Products–23

Assessor Trial & Repeat Model

Market Share Due to Sampling

Samplingcoverage (%) 0.503

% Delivered 0.90

% of those deliveredhitting target 0.80

Simulation sampleuse

Switchback rate of non-purchasers

Repurchase rate ofsimulation

non-purchasers

Prob. of switchingto brand

0.16

Prob. of repurchaseof brand

0.427

Long-term market share

from sampling0.02

% hitting target that get used

0.60

Retention rate GIVEN trial

for sample receivers0.218

Correction for sampling/adoverlap (take out those whotried sampling, but would

have tried due to ad)0.035

Market share tryingsamples

0.251

Source: Thomas Burnham, University of Texas at Austin

New Products–24

Assessor Preference Model Summary

Source: Thomas Burnham, University of Texas at Austin

Pre-use constantsum evaluations

Post-use constantsum evaluations

Cumulative trialfrom ad

(T&R model)0.137

Beta (B) forchoice model

Pre-entry market shares

Post-entry marketshares (assuming

consideration0.274

Weighted post entry

market shares0.038

Pre-use preferenceratings

Pre-use choices

Post-use preferenceratings

Proportion of consumers who

consider product 0.137 Draw &

cannibalization calculations

New Products–25

Assessor Market Share to Financial Results Diagrams

Market share0.059

Market size60M

Sales per person$5

JWC factory sales

16.7

Average unit margin

0.541

Ad/samplingexpense4.5/3.5

Net contribution

JWCfactory sales

16.7

Industry averagesales $ for

market share17.7

JWCfactory sales

Frequency of usedifferences

0.9

Unit-dollar adjustment

0.94

Price differences1.04

Returnon sales

Source: Thomas Burnham, University of Texas at Austin

New Products–26

Predicted and Observed Market Shares for ASSESSOR

Deviation Deviation Product Description Initial Adjusted Actual (Initial – (Adjusted – Actual) Actual)

Deodorant 13.3 11.0 10.4 2.9 0.6Antacid 9.6 10.0 10.5 –0.9 –0.5Shampoo 3.0 3.0 3.2 –0.2 –0.2Shampoo 1.8 1.8 1.9 –0.1 –0.1Cleaner 12.0 12.0 12.5 –0.5 –0.5Pet Food 17.0 21.0 22.0 –5.0 –1.0Analgesic 3.0 3.0 2.0 1.0 1.0Cereal 8.0 4.3 4.2 3.8 0.1Shampoo 15.6 15.6 15.6 0.0 0.0Juice Drink 4.9 4.9 5.0 –0.1 –0.1Frozen Food 2.0 2.0 2.2 –0.2 –0.2Cereal 9.0 7.9 7.2 1.8 0.7Etc. ... ... ... ... ...

Average 7.9 7.5 7.3 0.6 0.2Average Absolute Deviation — — — 1.5 0.6Standard Deviation of Differences — — — 2.0 1.0

New Products–27

Yankelovich, Skelly and White Model

Forecast market share = S N C R U K

where:

S =Lab store sales (indicator of trial),

N =Novelty factor of being in lab market. Discount sales by 20–40% based on previous experience that relate trial in lab markets to trial in actual markets,

C =Clout factor which retains between 25% and 75% of SN determined, based on proposed marketing effort versus ad and distribution weights of existing brands in relation to their market share,

R =Repurchase rate based on percentage of those trying who repurchase,

U =Usage rate based on usage frequency of new product as compared to the new product category as a whole, and

K =Judgmental factor based on comparison of S N C R U

K with Yankelovich norms. The comparison is with respect to factors such as size and growth of category, new product’s share derived from category expansion versus conversion from existing brand.

New Products–28

Some Issues in ValidatingPre-Test Models

Validation does not include products that were withdrawn as a result of model predictions

Pre-test and actual launch are separated in time, often by a year or more

Marketing program as implemented could be different from planned program