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New Product Models. Pre-Test Market Models Product Design using Conjoint Analysis Forecasting with Diffusion Models. New Product Decision Models. Product design using conjoint analysis Forecasting the pattern of new product adoptions (Bass Model)

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new product models
New Product Models
  • Pre-Test Market Models
  • Product Design using Conjoint Analysis
  • Forecasting with Diffusion Models
new product decision models
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)
the new product development process

Opportunity Identification

Market definitionIdea generation

Life-Cycle Management

Market response analysis & fine tuning the marketing mix; Competitor monitoring & defenseInnovation at maturity

Introduction

Launch planningTracking the launch

Go

No

Testing

Advertising & product testingPretest & prelaunch forecastingTest marketing

The New Product Development Process

Reposition

Harvest

Go

No

Design

Identifying customer needs Sales forecasting Product positioning Engineering Marketing mix assessment Segmentation

Go

No

Go

No

value of concept testing tv series
Value of Concept Testing:TV Series

2,000 Ideas

100 Scripts

20 Pilots

5 Scheduled for prime time

1 Success (?)

new product testing from a customer s perspective
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

questions answered in concept testing
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?
questions answered in alpha beta testing
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?
questions answered in laboratory test markets
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?
questions answered in test markets
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?
pretest market models
Pretest Market Models
  • Objective

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

  • Conceptual model

AwarenessèAvailabilityèTrialèRepeat

  • Commercial pre-test market services
    • Yankelovich, Skelly, and White
    • Bases
    • Assessor
a ssessor model
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.
overview of a ssessor modeling procedure

Consumer Research Input

(Laboratory Measures)

(Post-Usage Measures)

Management Input

(Positioning Strategy)

(Marketing Plan)

Preference

Model

Trial &

Repeat Model

Reconcile

Outputs

Draw &

Cannibalization

Estimates

Brand Share

Prediction

Unit Sales

Volume

Diagnostics

Overview of ASSESSORModeling Procedure
overview of a ssessor measurements
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

trial repeat model
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.

trial model
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)

repeat model
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 tOtherp(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)

preference model purchase probabilities before new product use
Preference Model: Purchase Probabilities Before New Product Use

(Vij)b

Lij = ––––––––

Ri

å (Vik)b

k=1

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)

preference model purchase probabilities after new product use
Preference Model: Purchase Probabilities After New Product Use

(Vij)b

L´ij = –––––––––––––––––

Ri

(Vin)b + å (Vik)b

k=1

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´inM´n = Enå –––IN

n = index for new product

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

N = number of respondents

estimating cannibalization and draw
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:

LinMj = å ––– IN

L´inL´inM´j = Enå ––– + (1 – En)å –––

IN IN

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

Dj = Mj – M´j

example preference ratings
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

choice probabilities
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 0.00

2 0.20 0.05 0.75 0.00 0.21 0.03 0.03 0.00 0.73

3 0.43 0.57 0.00 0.00 0.42 0.16 0.00 0.00 0.42

4 0.46 0.54 0.00 0.00 0.47 0.50 0.00 0.00 0.03

5 0.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00

6 1.00 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.20

7 0.01 0.35 0.00 0.64 0.03 0.61 0.00 0.36 0.00

8 0.89 0.11 0.00 0.00 0.02 0.00 0.00 0.00 0.98

9 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 24.3

New 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

assessor trial repeat model

Long-term

market share

from advertising

0.39

Assessor Trial & Repeat Model

Response Mode

Manual Mode

% making first purchase

GIVEN awareness &

availability

0.23

Prob. of awareness

0.70

Prob. of availability

0.85

Prob. of switching

TO brand

0.16

Prob. of repurchase

of brand

0.60

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

Awareness

estimate

Distribution

estimate (Agree)

Switchback rate of

non-purchasers

Repurchase rate

of simulation

purchasers

% making first

purchase due to

advertising

0.137

Retention rate

GIVEN trial

for ad purchasers

0.286

Source: Thomas Burnham, University of Texas at Austin

assessor trial repeat model1

Correction for sampling/ad

overlap (take out those who

tried sampling, but would

have tried due to ad)

0.035

Market share trying

samples

0.251

Long-term

market share

from sampling

0.02

Assessor Trial & Repeat Model

Market Share Due to Sampling

Sampling

coverage (%) 0.503

% Delivered 0.90

% of those delivered

hitting target 0.80

Simulation sample

use

Switchback rate of

non-purchasers

Repurchase rate of

simulation

non-purchasers

% hitting target

that get used

0.60

Prob. of switching

to brand

0.16

Prob. of repurchase

of brand

0.427

Retention rate

GIVEN trial

for sample receivers

0.218

Source: Thomas Burnham, University of Texas at Austin

assessor preference model summary

Draw &

cannibalization

calculations

Assessor Preference Model Summary

Pre-use preference

ratings

Pre-use choices

Post-use preference

ratings

Proportion of

consumers who

consider product

0.137

Pre-entry market

shares

Post-entry market

shares (assuming

consideration

0.274

Weighted

post entry

market shares

0.038

Beta (B) for

choice model

Pre-use constant

sum evaluations

Post-use constant

sum evaluations

Cumulative trial

from ad

(T&R model)

0.137

Source: Thomas Burnham, University of Texas at Austin

assessor market share to financial results diagrams

Market share

0.059

Market size

60M

Sales per person

$5

JWC

factory sales

16.7

Average

unit margin

0.541

Ad/sampling

expense

4.5/3.5

JWC

factory sales

Industry average

sales $ for

market share

17.7

Unit-dollar

adjustment

0.94

Frequency of use

differences

0.9

Price differences

1.04

Net

contribution

JWC

factory sales

16.7

Return

on sales

Assessor Market Share to Financial Results Diagrams

Source: Thomas Burnham, University of Texas at Austin

predicted and observed market shares for a ssessor
Predicted and Observed Market Shares for ASSESSOR

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

Deodorant 13.3 11.0 10.4 2.9 0.6

Antacid 9.6 10.0 10.5 –0.9 –0.5

Shampoo 3.0 3.0 3.2 –0.2 –0.2

Shampoo 1.8 1.8 1.9 –0.1 –0.1

Cleaner 12.0 12.0 12.5 –0.5 –0.5

Pet Food 17.0 21.0 22.0 –5.0 –1.0

Analgesic 3.0 3.0 2.0 1.0 1.0

Cereal 8.0 4.3 4.2 3.8 0.1

Shampoo 15.6 15.6 15.6 0.0 0.0

Juice Drink 4.9 4.9 5.0 –0.1 –0.1

Frozen Food 2.0 2.0 2.2 –0.2 –0.2

Cereal 9.0 7.9 7.2 1.8 0.7

Etc. ... ... ... ... ...

Average 7.9 7.5 7.3 0.6 0.2

Average Absolute Deviation — — — 1.5 0.6

Standard Deviation of Differences — — — 2.0 1.0

yankelovich skelly and white model
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.

some issues in validating pre test models
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