newprod by methyae

VIEWS: 3 PAGES: 83

									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–1

―Newness‖ of Products

• Repositioning

New to World
• Line Extensions

• Breakthroughs—Major Product Modifications

• ―Me Too‖ Products

New to Company
New Products–2

New Products as Part of Corporate Strategy
Markets
Existing New

Existing

Market Penetration

Market Development

Products
New New Product Development (Diversification)

New Products–3

The New Product Development Process
Opportunity Identification
Market definition Idea generation

Reposition

Harvest Life-Cycle Management

Go

No

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

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

Go

No

Introduction
Launch planning Tracking the launch

Go Testing

No

Advertising & product testing Pretest & prelaunch forecasting Test marketing

Go

No

New Products–4

Impact of Product Superiority on Product Success
Success rate (%)

Mkt Share 53.5%

100
Mkt Share 32.4%

98

50 0

Mkt Share 11.6%

58

18.4
Minimal Moderate Product Superiority Maximal

Based on a study of 203 products in B2B -- Robert G. Cooper, Winning at New Products (1993) . Success measured using four factors: (1) whether it met or exceeded management’s criteria for success, (2) the profitability level (1-10 scale), (3) market share at the end of three years, and (4) whether it met company sales and profit objectives (1-10 scale).
New Products–5

Impact of Early Product Definition on Product Success
Success rate (%)

100 50 0
Poor

Mkt Share 37.3% Mkt Share 36.5 Mkt Share 22.9

85.4

64.2

26.2
Moderate Product Definition Strong

Source: Robert G. Cooper, Winning at New Products (1993) New Products–6

Impact of Market Attractiveness on Product Success
Success rate (%)

100
Mkt Share 31.7 Mkt Share 33.7

Mkt Share 36.5%

50 42.5 0
Low

61.5

73.9

Moderate Market Attractiveness

High

Source: Robert G. Cooper, Winning at New Products (1993) New Products–7

Resources Allocated at Each Stage of NPD 600 500 400 300
203.8 Mean Expenditure ($000K) Mean Person-Days 315.3 553.2 435.9

200 100 0
57

148.4

Predevelopment Activities

Product development Commercialization & product testing

Source: Robert G. Cooper (1993)

New Products–8

Value of Good Design
80% of a product’s manufacturing costs are incurred during the first 20% of its design (varies with product category).
Conjoint Analysis is a systematic approach for matching product design with the needs and wants of customers, especially in the early stages of the New Product Development process.
Source: Mckinsey & Company Report

New Products–9

What is Conjoint Analysis?
A way to understand and incorporate the structure of customer preferences into the new product design process. In particular, it enables one to evaluate how customers make tradeoffs between various product attributes.

The basic output of conjoint analysis are:
• A numerical assessment of the relative importance that customers attach to attributes of a product category

• The value (utility) provided to customers by each potential feature of a product
New Products–10

Customer Value Assessment Procedures
Customer Value
Inferential/Value-Based Internal engineering assessment Indirect survey questions Field value-in-use assessment

Attitude-Based

Behavior-Based Choice models Neural networks Discriminant analysis

Direct Questions

Indirect/(Decompositional Methods) Conjoint analysis Preference Regression
Constrained/Compositional Methods Multiattribute value analysis Benchmarking
New Products–11

Unconstrained Focus groups Direct survey questions Importance and attitude ratings rule-based system/AI/expert systems

Why is Customer Value Assessment through Conjoint Useful?
     

Design new products that enhance customer value. Forecast sales/market share/profit of alternative product concepts. Identify market segments for which a given concept offers high value. Identify the ―best‖ concept for a target segment. Explore impact of alternative pricing and service strategies. Help production planning in flexible manufacturing systems.

New Products–12

Conjoint Analysis in Product Design

Should we offer our business travelers more room space or a fax machine in their room? Given a target cost for a product, should we enhance product reliability or its performance? Should we use a steel or aluminum casing to increase customer preference for the new equipment?

New Products–13

Measuring Importance of Attributes
When choosing a restaurant, how important is…
Circle one
Not Important Very Important

Price

1 2 3 4 5 6 7 8 9

Quality of Food
Location Decor

1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

New Products–14

Simple Example of Conjoint Analysis
Product Option 1 2 3 4 5 6 7 8 Cuisine Italian Italian Italian Italian Thai Thai Thai Thai Distance Near Near Far Far Near Near Far Far Price Range $10 $15 $10 $15 $10 $15 $10 $15 Preference Rank

New Products–15

Simple Example of Conjoint Analysis
Product Option 1 2 3 4 5 6 7 8 Cuisine Italian Italian Italian Italian Thai Thai Thai Thai Distance Near Near Far Far Near Near Far Far Price Range $10 $15 $10 $15 $10 $15 $10 $15 Preference Rank 8 6 4 2 7 5 3 1

New Products–16

How to Use in Design/Tradeoff Evaluation
 Example: Italian vs Thai = 20 – 16 = 4 util units $10 vs $15 = 22 – 14 = 8 util units  So ―Thai” is worth $2.50 more than “Italian” for this customer:

4 (   (15  10)  $2.50) 8
 Can use to obtain value to customer of service (non-price) attributes.

New Products–17

Conjoint Study Process
Stage 1—Design the conjoint study:
Step 1.1: Select attributes relevant to the product or service category, Step 1.2: Select levels for each attribute, and Step 1.3: Develop the product bundles to be evaluated.

Stage 2—Obtain data from a sample of respondents:
Step 2.1: Design a data-collection procedure, and Step 2.2: Select a computation method for obtaining part-worth functions.

Stage 3—Evaluate product design options:
Step 3.1: Segment customers based on their part-worth functions, Step 3.2: Design market simulations, and Step 3.3: Select choice rule.

New Products–18

An Example to Illustrate the Concepts of Conjoint Analysis: Designing a Frozen Pizza
Attributes
 Type of crust (3 types)  Type of cheese (3 types)  Price (3 levels)   Topping (4 varieties) Amount of cheese (3 levels)

Crust
Pan Thin Thick

Type of Cheese
Romano Mixed cheese Mozzeralla

Price
$ 9.99 $ 8.99 $ 7.99

Topping
Pineapple Veggie Sausage Pepperoni

Amount of Cheese
2 oz. 4 oz. 6 oz.

A total of 324 (3  4  3  3  3) different pizzas can be developed from these options!
New Products–19

Designing a Frozen Pizza: A More Complete Design
Attributes
   

Type of crust (3) Type of cheese (3) Amount of cheese (3) Type of meat (3)

   

Amount of meat (3) Type of sauce (3) Amount of sauce (3) Presence of mushrooms (2)

   

Types of peppers (3) Presence of olives (2) Presence of oil (2) Price (3)

Prototypes
81 prototype pizzas from 105,000 possible profiles.

Person Attributes
  

Sex Age Presence of teenagers

 

Household size Favorite brand

 

Category usage Region

Study Approach
   

Each respondent rates 3 of the 81 prototypes along with a “control”. Likelihood of purchase, conditioned on price. Appropriateness for various meals/snacks. Appropriateness for various family members. New Products–20

Example Paired Comparison

Aloha Special Crust Topping Type of cheese Amount of cheese Price Which do you prefer? Which one would you buy? Pan Pineapple Mozzarella 4 oz $8.99

Meat-lover’s treat Thick Pepperoni Mixed cheese 6 oz $9.99

New Products–21

Example Ratings
Product Bundle Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Type of Cheese Romano Mixed Mozzarella Mixed Mixed Romano Mixed Mozzarella Mozzarella Mixed Romano Mixed Mixed Mozzarella Mixed Romano Amount of Cheese 2 oz 6 oz 4 oz 4 oz 4 oz 4 oz 6 oz 2 oz 6 oz 2 oz 4 oz 4 oz 4 oz 4 oz 2 oz 6 oz Example Preference Score 0 43 53 56 41 63 38 53 68 46 80 58 61 57 83 70
New Products–22

Crust Pan Thin Thick Thin Pan Thin Thick Thin Thick Thin Pan Thin Pan Thin Thick Thin

Topping Pineapple Pineapple Pineapple Pineapple Veggie Veggie Veggie Veggie Pepperoni Pepperoni Pepperoni Pepperoni Sausage Sausage Sausage Sausage

Price $9.99 $8.99 $8.99 $7.99 $8.99 $7.99 $9.99 $8.99 $7.99 $8.99 $8.99 $9.99 $8.99 $9.99 $7.99 $8.99

Example Computed Part-Worth for Attributes

New Products–23

Example Part-Worths for Attribute Options

New Products–24

Conjoint Computations
m ki

U(P) =   aij xij
i=1 j=1

where: P U(P) aij ki m xij = = = = = = a particular product/concept of interest, the utility associated with product P, Utility associated with the jth level (j = 1, 2, 3, . . . , ki) on the ith attribute (part-worth), number of levels of attribute i, number of attributes, and 1 if the jth level of the ith attribute is present in product P, 0 otherwise.
New Products–25

{

Market Share and Revenue Share Forecasts
 Define the competitive set -- these are the products from

which the target segment make choices. Some of theses may be existing products and, others concepts being evaluated. We denote this set of products as P1, P2,...PN.
 Select Choice rule
   

Maximum utility rule Share of preference rule Logit choice rule Alpha rule

 Software also has a ―Revenue index option‖ wherein you can

compute the revenue index of any product compared to the revenue index of 100 for a base product you select.

New Products–26

Market Share Forecast (Maximum Utility Rule)


The relevant market consists of products P1, P2, . . . , PN. Some of theses may be existing products and, others concepts being evaluated.
Each consumer will prefer to buy the product with the highest utility among those available. Then forecasted market share for products Pi is given by:



MS (Pi) =  ––––––––––––––––
K=1

K

Consumers who prefer i K

where K is the number of consumers who participated in the study.
New Products–27

Other Choice Rules
Share of utility rule: Under this choice rule, the consumer selects each product with a probability that is proportional to the utility of that compared to the total utility derived from all the products in the choice set. Logit choice rule: This is similar to the share of utility rule, except that it gives larger weights to more preferred alternatives and smaller weights to less preferred alternatives. Alpha rule: Modified version of share of utility rule. Before applying the share of utility, the utility functions are modified by an ―alpha‖ factor so that the computed market shares of existing products are as close as possible to their actual market shares.
New Products–28

Example Market Share Computation (Frozen Pizza Example)
 Market consists of three products and three consumers
Product (P2) Meat-lover’s Treat Thick Pepperoni Mixed cheese 6 oz. $9.99

(P1) Aloha Special Crust Topping Type of cheese Amt. of cheese Price Pan Pineapple Mozzarella 4 oz. $8.99

(P3) Veggie Delite Thin Veggie Romano 2 oz. $7.99
New Products–29

Example Market Share Computation (Frozen Pizza Example)
Consumers‟ Part-Worths C1 Pan Thin Thick Pineapple Veggie Sausage Pepperoni Romano Mixed cheese Mozzarella 2 oz 4 oz. 6 oz. $9.99 $8.99 $7.99 0 9 11 17 6 13 0 52 13 0 0 8 10 0 10 10 C2 10 37 0 3 0 3 0 0 9 3 0 39 21 0 4 12 C3 26 0 10 0 14 7 19 21 0 14 0 16 12 0 18 16
New Products–30

Example Market Share Computation (Frozen Pizza Example)
Customer C1 C2 C3


Computed Utility for Products P1 P2 P3 35 59 74 34 30 41 77 49 51

Infrequently purchased products: Consumers only buy the brand with the highest utility. Then, the market share for Product 1 is 66.6% and Product 3 is 33.4%.
Frequently purchased products (Share of utility rule) Assume each consumer buys the same amount. Then



Market share of P1 = (0.24 +0.43+0.45)/3 = 37.3% Market share of P2 = (0.23+0.22+0.25)/3 = 23.3% Market share of P3 = (0.53+0.35+0.30)/3 = 39.4%
New Products–31

Share of Utility Rule

  

Describe competitive set Assign individual weights if any Compute market share

 wi pij i mj = ––––––––––   wi pij
j i mj: market share of product j wi: weights assigned to individual i
New Products–32

How to Use in Design/Tradeoff Evaluation
 Example: Italian vs Thai = 20 – 16 = 4 util units $10 vs $15 = 22 – 14 = 8 util units  So ―Thai” is worth $2.50 more than “Italian” for this customer:

4 (   (15  10)  $2.50) 8
 Can use to obtain value to customer of service (non-price) attributes.

New Products–33

Another Example of Conjoint Analysis Air Pollution Control Systems
Dürr Environmental is developing a new air pollution control system (thermal oxidizer) to compete against existing offerings from Waste Watch, Thermatrix, and Advanced Air. Key offering attributes:  Thermal efficiency  Delivery time  List price  Delivery terms Q: What to offer? Who will buy/who to target? Where will share come from?

New Products–34

New Products–35

An Example Conjoint Study: Air Pollution Control Equipment
Attributes
 Performance specs (4 options)  Delivery time (4 options)

 Price (4 options)  Delivery_terms (4 options)

Efficiency
Exceed by 9% Exceed by 5% Meets target Short by 5%

Delivery time
6 months 9 months 12 months 15 months

List Price
$600k $700k $800k $900k

Delivery terms
Installed, 2-year guarantee Installed, 1-year guarantee Installed, service contract FOB seller, service contract A total of 256 (4x4x4x4) different offerings can be designed from these options!
New Products–36

Market Share Computation: (Air Pollution Control Equipment)
Customer’s Utility Base Meets target Exceed 5% Exceed 9% 12 months 9 months 6 months $800k $700K $600K Inst_ser Inst_1Yr Inst_2Yr Sunoco 0 5 35 40 20 30 40 5 8 10 6 8 10 Mattel 0 10 0 0 5 20 10 20 35 50 5 10 20 ICI 0 10 40 50 3 8 10 2 5 10 10 20 30
New Products–37

Market Share Computation (Air Pollution Control Equipment)
 Market consists of three products and three customers
Product Waste watch Performance specs Exceed 5% Delivery time 9 months List Price $800k Delivery terms FOB_ser

Thermatrix
Exceed 20% 9 months $900k Inst_1Yr

Advanced Air
Meet Specs 6 months $700k Inst_ser

New Products–38

Market Share Computation: (Air Pollution Control Equipment)
Computed Utility for Products Waste Watch Thermatrix Advanced Air

Sunoco
Mattel ICI


70
40 50

78
30 78

61
75 40

Maximum Utility Rule: If we assume customers will only buy the product with the highest utility, the market share for Thermatrix is 2/3 and 1/3 for Wahlco. Share of preference rule: If we assume that each customer will buy each product in proportion to its utility relative to the other products, then market shares for the three products are:



Waste Watch: 30.3%

Thermatrix: 34.8

Advanced Air: 34.9

New Products–39

Identifying Segments Based on Conjoint Part Worths (Airpol.pwr)

Analyze Airpol.pwr file in Cluster Analysis to obtain the above results.
New Products–40

Members in Each Segment
Segment 1. Companies in this Segment include


Cummins Engineering, Illinois Tools, Mattel, NesteResin, Ralston Purina, New World Technologies, Baltimore Gas, Applied Coatings, Pharmasyn, and Thermal Electric. These are smaller companies that operate in industries without major pollution problems. They want an equipment that meets EPA efficiency target, medium delivery times, have high price sensitivity, and require installation and warranty.



New Products–41

Members in Each Segment
Segment 2. Companies in this Segment include


ICI, Mobil, Maytag, Texaco, Union Carbide, Dow Chemicals, Boise Cascade, and 3M. These are large chemical and paper companies that have pollution issues to deal with. They want an equipment that Exceeds EPA efficiency target, have long delivery times (perhaps for installation in new factories that they build), have moderate price sensitivity, and do not require installation help or warranty (FOB).



New Products–42

Members in Each Segment
Segment 3. Companies in this Segment include


Deere, Intel, Air Products, Sunoco, HP, Conagra, Kimberly-Clark, Hershey, and Westinghouse Electric.



These are large companies that seem to operate in industries with less severe pollution problems. They want an equipment that Exceeds EPA efficiency target, prefer quick/medium delivery, have low price sensitivity, and moderately prefer installation and warranty.

New Products–43

Other Aspects to Consider
 

Incorporate revenue potential of a product


Market share  Incremental margin over base product Segment 1 (Value segment): A product that meets EPA target, with delivery of 6 months, priced at 600K, and with installation and 2-year warranty has the potential to get 42% share of the market and good revenue potential against the three existing competitors. Segment 3 (Premium segment): A product that exceeds EPA target by 5%, with delivery of 9 months, priced at 700K, and with installation and 2-year warranty has the potential to get 31% share and high revenue potential.

Design optimal product by segment




New Products–44

Situations Where Conjoint Analysis Might Be Valuable

 

The new concept involves important tradeoffs affecting design, production, marketing, or other operational variables.
Product/service is realistically decomposable into a set of basic attributes. Product/service choice tends to be high involvement.


 

Factorial combinations of basic attribute levels are believable.
Desirable new-product alternatives can be synthesized from basic alternatives. Product/service alternatives can be realistically described, either verbally or pictorially. (Otherwise, actual product formulations should be considered).

New Products–45

Some Commercial Applications of Conjoint Analysis
Consumer Non-Durables
1. 2. 3. 4. 5. 6. Bar soaps Hair shampoos Carpet cleaners Synthetic-fiber garments Gasoline pricing Pantyhose

Industrial/Business Goods
1. 2. 3. 4. 5. 6. Copying machines Printing equipment Fax machines Data transmission Lap top computer Job offers to MBA‟s

Other Products
1. 2. 3. 4. 5. Automotive styling Automobile tires Car batteries Ethical drugs Employee benefit package

Financial Services
1. 2. 3. 4. 5. Branch bank services Auto insurance policies Health insurance policies Credit card features Consumer discount card

Transportation
1. 2. 3. 4. 5. Air Canada IATA American Airlines Canadian National Railway Amtrak

Other Services
1. 2. 3. 4. 5. Car rental agencies Telephone service pricing Hotels Medical laboratories Employment agencies New Products–46

Methods for Forecasting New Product Sales
Early stages of development Chain ratio method Judgmental methods Scenario Analysis Diffusion modeling Later stages of development Pre-test market methods Test-market methods
New Products–47

The Bass Diffusion Model

Model designed to answer the question:

When will customers adopt a new product or technology?

New Products–48

Assumptions of the Basic Bass Model


Diffusion process is binary (consumer either adopts, or waits to adopt)
Constant maximum potential number of buyers (N) Eventually, all N will buy the product No repeat purchase, or replacement purchase The impact of the word-of-mouth is independent of adoption time Innovation is considered independent of substitutes The marketing strategies supporting the innovation are not explicitly included
New Products–49

   

 

Adoption Probability over Time
(a)
1.0

Cumulative Probability of Adoption up to Time t
Introduction of product

F(t)

Time (t)

(b)
f(t) = d(F(t)) dt

Density Function: Likelihood of Adoption at Time t

Time (t)

New Products–50

Number of Cellular Subscribers
9,000,000

5,000,000

1,000,000 1983 1 2 3 4 5 6 7 8 9

Years Since Introduction
Source: Cellular Telecommunication Industry Association New Products–51

Sales Growth Model for Durables (The Bass Diffusion Model)
St = p  Remaining + q  Adopters  Potential Remaining Potential
Innovation Effect
where: St p q # Adopters Remaining Potential = = = = = sales at time t “coefficient of innovation” “coefficient of imitation” S0 + S1 + • • • + St–1 Total Potential – # Adopters
New Products–52

Imitation Effect

Parameters of the Bass Model in Several Product Categories
Product/ Technology Innovation parameter (p) 0.108 0.059 0.006 0.009 0.000 0.055 0.008 0.031 0.002 0.002 0.000 0.121 Imitation parameter (q) 0.231 0.146 0.185 0.143 0.534 0.378 0.421 0.128 0.435 0.357 0.797 0.281

B&W TV Color TV Room Air conditioner Clothes dryers Ultrasound Imaging CD Player Cellular telephones Steam iron Oxygen Steel Furnace (US) Microwave Oven Hybrid corn Home PC

A study by Sultan, Farley, and Lehmann in 1990 suggests an average value of 0.03 for p and an average value of 0.38 for q.

New Products–53

Technical Specification of the Bass Model
The Bass Model proposes that the likelihood that someone in the population will purchase a new product at a particular time t given that she has not already purchased the product until then, is summarized by the following mathematical.

Formulation
Let: L(t): Likelihood of purchase at t, given that consumer has not purchased until t f(t): Instantaneous likelihood of purchase at time t F(t): Cumulative probability that a consumer would buy the product by time t Once f(t) is specified, then F(t) is simply the cumulative distribution of f(t), and from Bayes Theorem, it follows that:

L(t) = f(t)/[1–F(t)]

(1)
New Products–54

Technical Specification of the Bass Model cont’d
The Bass model proposes that L(t) is a linear function:

q L(t) = p + –– N(t) N
where p q N(t) N

(2)

= = = =

Coefficient of innovation (or coefficient of external influence) Coefficient of imitation (or coefficient of internal influence) Total number of adopters of the product up to time t Total number of potential buyers of the new product

Then the number of customers who will purchase the product at time t is equal to Nf(t) . From (1), it then follows that:

q Nf(t) = [ p + –– N(t)][1 – N(t)] N

(3)

Nf(t) may be interpreted as the number of buyers of the product at time t [ = (t)]. Likewise, NF(t ) is equal to the cumulative number of buyers of the product up to time t [ = N(t)].
New Products–55

Bass Model cont’d

Noting that [n(t) = Nf(t)] is equal to the number of buyers at time t, and [N(t) = NF(t)] is equal to the cumulative number of buyers until time t, we get from (2): q Nf(t) = [ p + –– N(t)][1 – N(t)] N (3)

After simplification, this gives the basic diffusion equation for predicting new product sales: q n (t) = pN + (q – p) [N(t)] – –– [N(t)]2 N (4)

New Products–56

Estimating the Parameters of the Bass Model Using Non-Linear Regression
An equivalent way to represent N(t) in the Bass model is the following equation:

n(t) =

q p + –– N(t–1) N

[N – N(t–1)]

Given four or more values of N(t) we can estimate the three parameters of the above equation to minimize the sum of squared deviations.

New Products–57

Estimating the Parameters of the Bass Model Using Regression
The discretized version of the Bass model is obtained from (4): n(t) = a + bN(t–1) + cN 2(t–1) a, b, and c may be determined from ordinary least squares regression. The values of the model parameters are then obtained as follows: –b – b2 – 4ac N = –––––––––––––– 2c a p = –– N q = p + b To be consistent with the model, N > 0, b  0, and c < 0.
New Products–58

Forecasting Using the Bass Model—Room Temperature Control Unit
Quarter Market Size = 16,000 (At Start Price) Innovation Rate = 0.01 (Parameter p) Imitation Rate = 0.41 (Parameter q) Initial Price = $400 Final Price = $400 Example computations n(t) = pN + (q–p) N(t–1) –
q

Sales 0 160 425 1,234 1,646 555 78 9 1 0 0

Cumulative Sales 0 160 1,118 4,678 11,166 15,106 15,890 15,987 15,999 16,000 16,000

0 1 4 8 12 16 20 24 28 32 36

N(t–1) 2/N

Sales in Quarter 1 = 0.01  16,000 + (0.41–0.01)  0 – (0.41/16,000)  (0)2 = 160 Sales in Quarter 2 = 0.01  16,000 + (0.40)  160 – (0.41/16,000)  (160)2 = 223.35 New Products–59

Factors Affecting the Rate of Diffusion
Product-related
   

High relative advantage over existing products High degree of compatibility with existing approaches Low complexity Can be tried on a limited basis



Benefits are observable

Market-related


Type of innovation adoption decision (eg, does it involve switching from familiar way of doing things?)


 

Communication channels used
Nature of “links” among market participants Nature and effect of promotional efforts
New Products–60

Some Extensions to the Basic Bass Model
 Varying market potential
As a function of product price, reduction in uncertainty in product performance, and growth in population, and increases in retail outlets.

 Incorporation of marketing variables
Coefficient of innovation (p) as a function of advertising

p(t) = a + b ln A(t).
Effects of price and detailing.

 Incorporating repeat purchases

 Multi-stage diffusion process
Awareness  mouth Interest  Adoption  Word of
New Products–61

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–62

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–63







Overview of ASSESSOR Modeling Procedure
Management Input (Positioning Strategy) (Marketing Plan)

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

Preference Model Reconcile Outputs

Trial & Repeat Model

Draw & Cannibalization Estimates

Brand Share Prediction

Unit Sales Volume

Diagnostics
New Products–64

Overview of ASSESSOR Measurements
Design O1 O2 X1 [O3] X2 O4 X3 O5 Procedure Respondent screening and recruitment (personal interview) Pre-measurement for established brands (self-administrated questionnaire) Exposure to advertising for established brands and new brands Measurement of reactions to the advertising materials (selfadministered questionnaire) Simulated shopping trip and exposure to display of new and established brands Purchase opportunity (choice recorded by research personnel) Home use/consumption of new brand Post-usage measurement (telephone Measurement Criteria for target-group identification (eg, product-class usage) Composition of „relevant set‟ of established brands, attribute weights and ratings, and preferences

Optional, e.g. likability and believability ratings of advertising materials

Brand(s) purchased

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–65

Trial/Repeat Model

Market share for new product

Mn = T  R  W
where: T = R = W = long-run cumulative trial rate (estimated from measurement at O4) long-run repeat rate (estimated from measurements at O5) relative usage rate, with w = 1 being the average market usage rate.

New Products–66

Trial Model
T = FKD + CU – (FKD)  (CU)
where:

F =
K = D = C = U =

long-run probability of trial given 100% awareness and 100% distribution (from O4)
long-run probability of awareness (from managerial judgment) long-run probability of product availability where target segment shops (managerial judgment and experience) probability of consumer receiving sample (Managerial judgment) probability that consumer who receives a product will use it (from managerial judgment and past experience)
New Products–67

Repeat Model
Obtained as long-run equilibrium of the switching matrix estimated from (O2 and O5):
Time (t+1)
New New Other

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: r = p(on) –––––––––––––– 1 + p(on) – p(nn)
New Products–68

Preference Model: Purchase Probabilities Before New Product Use
Lij

(Vij)b = ––––––––
k=1

 (Vik)b

Ri

where: Vij Lij = = Preference rating from product j by participant i Probability that participant i will purchase product j

Ri
b

= Products that participant i will consider for purchase (Relevant set)
= An index which determines how strongly preference for a product will translate to choice of that product (typical range: 1.5–3.0)
New Products–69

Preference Model: Purchase Probabilities After New Product Use
L´ij (Vij)b = –––––––––––––––––

(Vin)b + 
where: L´it =

Ri

k=1

(Vik)b

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:

M´n = En
n En N = = = index for new product

L´in  ––– N I

proportion of participants who include new product in their relevant sets number of respondents
New Products–70

Estimating Cannibalization and 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 postmarket shares are then given by:

Lin Mj =  ––– N I M´j = En L´in  ––– + (1 – En) N I L´in  ––– N I

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

Dj = Mj – M´j
New Products–71

Example: Preference Ratings
Vij (Pre-use) V´ij (Post-use)

Customer
1 2 3 4 5 6 7 8

B1
0.1 1.5 2.5 3.1 0.0 4.1 0.4 0.6

B2
0.0 0.7 2.9 3.4 1.3 0.0 2.1 0.2

B3
4.9 3.0 0.0 0.0 0.0 0.0 0.0 0.0

B4
3.7 0.0 0.0 0.0 0.0 0.0 2.9 0.0

B1
0.1 1.6 2.3 3.3 0.0 4.3 0.4 0.6

B2
0.0 0.6 1.4 3.4 1.2 0.0 2.1 0.2

B3
2.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0

B4
1.7 0.0 0.0 0.0 0.0 0.0 1.6 0.0

New Product
0.2 3.1 2.3 0.7 0.0 2.1 0.1 5.0

9
10

4.8
0.7

2.4
0.0

0.0
4.9

0.0
0.0

5.0
0.7

2.2
0.0

0.0
3.4

0.0
0.0

0.3
0.9
New Products–72

Choice Probabilities
Customer 1 2 3 4 5 6 7 8 9 10 B1 0.00 0.20 0.43 0.46 0.00 1.00 0.01 0.89 0.79 0.02 Lij (Pre-use) B2 B3 B4 0.00 0.05 0.57 0.54 1.00 0.00 0.35 0.11 0.21 0.00 0.63 0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.98 0.37 0.00 0.00 0.00 0.00 0.00 0.64 0.00 0.00 0.00 10.1 B1 0.00 0.21 0.42 0.47 0.00 0.80 0.03 0.02 0.82 0.04 28.1 9.9 2.0 B2 0.00 0.03 0.16 0.50 1.00 0.00 0.61 0.00 0.18 0.00 24.8 3.5 0.7 L´ij (Post-use) B3 B4 New Product 0.69 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.89 16.1 7.5 1.5 0.31 0.00 0.00 0.00 0.00 0.00 0.36 0.00 0.00 0.00 6.7 3.4 0.7
New Products–73

0.00 0.73 0.42 0.03 0.00 0.20 0.00 0.98 0.00 0.07 24.3

Unweighted market share (%) 38.0 28.3 23.6 New product‟s draw from each brand (Unweighted %) New product‟s draw from each brand (Weighted by En in %)

Assessor Trial & Repeat Model
Market Share Due to Advertising
Response Mode •Max trial with unlimited Ad •Ad$ for 50% max. trial •Actual Ad $ •Max awareness with unlimited Ad •Ad $ for 50% max. awareness •Actual Ad $ Manual Mode % buying brand in simulated shopping Awareness estimate % making first purchase GIVEN awareness & availability 0.23

Prob. of awareness 0.70 Prob. of availability 0.85

% making first purchase due to advertising 0.137 Long-term market share from advertising 0.39 Retention rate GIVEN trial for ad purchasers 0.286

Distribution estimate (Agree)

Switchback rate of non-purchasers Repurchase rate of simulation purchasers
Source: Thomas Burnham, University of Texas at Austin

Prob. of switching TO brand 0.16

Prob. of repurchase of brand 0.60

New Products–74

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 % hitting target that get used 0.60 Prob. of switching TO brand 0.16 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

Switchback rate of non-purchasers Repurchase rate of simulation non-purchasers

Prob. of repurchase of brand 0.427

Retention rate GIVEN trial for sample receivers 0.218

Source: Thomas Burnham, University of Texas at Austin

New Products–75

Assessor Preference Model Summary
Pre-use preference ratings Pre-use constant sum evaluations Pre-use choices Beta (B) for choice model Pre-entry market shares

Post-use constant sum evaluations
Cumulative trial from ad (T&R model) 0.137

Post-use preference ratings Proportion of consumers who consider product 0.137

Post-entry market shares (assuming consideration 0.274

Weighted post entry market shares 0.038

Draw & cannibalization calculations

Source: Thomas Burnham, University of Texas at Austin

New Products–76

Assessor Market Share to Financial Results Diagrams
Market share 0.059 Market size 60M Sales per person $5 Industry average sales $ for market share 17.7 JWC factory sales Unit-dollar adjustment 0.94 Frequency of use differences 0.9 Net contribution Return on sales JWC factory sales 16.7 Price differences 1.04

JWC factory sales 16.7

Average unit margin 0.541
Ad/sampling expense 4.5/3.5

Source: Thomas Burnham, University of Texas at Austin

New Products–77

Predicted and Observed Market Shares for ASSESSOR
Product Description Deodorant Antacid Shampoo Shampoo Cleaner Pet Food Analgesic Cereal Shampoo Juice Drink Frozen Food Cereal Etc. Average Average Absolute Deviation Standard Deviation of Differences Initial 13.3 9.6 3.0 1.8 12.0 17.0 3.0 8.0 15.6 4.9 2.0 9.0 ... 7.9 — — Adjusted Actual 11.0 10.0 3.0 1.8 12.0 21.0 3.0 4.3 15.6 4.9 2.0 7.9 ... 7.5 — — 10.4 10.5 3.2 1.9 12.5 22.0 2.0 4.2 15.6 5.0 2.2 7.2 ... 7.3 — — Deviation (Initial – Actual) 2.9 –0.9 –0.2 –0.1 –0.5 –5.0 1.0 3.8 0.0 –0.1 –0.2 1.8 ... 0.6 1.5 2.0 Deviation (Adjusted – Actual) 0.6 –0.5 –0.2 –0.1 –0.5 –1.0 1.0 0.1 0.0 –0.1 –0.2 0.7 ... 0.2 0.6 1.0 New Products–78

BASES Model
Trial volume estimate

Pt =

Calibrated intent score



Distribution intensityt



Awareness levelt

Tt = Pt  U0  (1/Sit)  (TM)  (1/CDI)
where:

Pt Tt U0 Sit TM CDI

= = = = = =

Cumulative penetration up to time t Total trial volume until time t in a particular target market Average units purchased at trial (t = 0) Seasonality index at time = t Size of target market Category development index for target market
New Products–79

BASES Model cont’d
Repeat volume estimate Rt =  Ni–1,t Yit Ui
i=1
where: Ni–1,t = Cumulative number of consumers who repeat at least i–1 times by week t (N0,t = initial trial volume) Yit = Conditional cumulative ith repeat purchase rate at week t given that i–1 repeat purchases were made up to week t Ui = Average units purchased at repeat level i Ni–1,t & Yit are estimated based on consumers’ stated ―after use intended purchase frequency‖ and estimate of long-run decay in repeat rate. Ui is estimated based on consumers’ stated purchase quantities.
New Products–80



BASES Model cont’d

Total volume estimate St = Tt  Rt + Adjustments for promotional
volume

New Products–81

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–82

Some Issues in Validating Pre-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

New Products–83


								
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