new product decision models
DESCRIPTION
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). “ Newness ” of Products. Repositioning. - PowerPoint PPT PresentationTRANSCRIPT
New Products–1
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–2
“Newness” of Products
New to World
New to Company
• Repositioning
• Line Extensions
• Breakthroughs—Major Product Modifications
• “Me Too” Products
New Products–3
New Products as Part of Corporate Strategy
Markets
Products
Existing
Existing
New
New
Market Penetration
Market Development
New ProductDevelopment
(Diversification)
New Products–4
The New Product Development Process
DesignIdentifying customer needs Sales forecasting
Product positioningEngineering
Marketing mix assessmentSegmentation
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
Impact of Product Superiority on Product Success
18.4
58
98
0
50
100
Su
cc
es
s r
ate
(%
)
Mkt Share11.6%
Minimal Moderate Maximal
Product Superiority
Mkt Share32.4%
Mkt Share53.5%
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
26.2
64.285.4
0
50
100
Su
cc
es
s r
ate
(%
)
Mkt Share22.9
Poor Moderate Strong
Product Definition
Mkt Share36.5
Mkt Share37.3%
Source: Robert G. Cooper, Winning at New Products (1993)
New Products–6
Impact of Market Attractiveness on Product Success
73.961.542.5
0
50
100
Su
cces
s ra
te (
%)
Mkt Share31.7
Low Moderate High
Market Attractiveness
Mkt Share33.7
Mkt Share36.5%
Source: Robert G. Cooper, Winning at New Products (1993)
New Products–7
Resources Allocated at Each Stage of NPD
57
315.3
435.9
148.4
553.2
203.8
0
100
200
300
400
500
600
PredevelopmentActivities
Product development& product testing
Commercialization
Mean Expenditure($000K)
Mean Person-Days
Source: Robert G. Cooper (1993)
New Products–8
New Products–9
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–10
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 productattributes.
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
What is Conjoint Analysis?
New Products–11
Customer Value Assessment Procedures
CustomerValue
Attitude-Based
Direct Questions
UnconstrainedFocus groupsDirect survey questionsImportance and attitude ratingsrule-based system/AI/expert systems
Constrained/Compositional MethodsMultiattribute value analysisBenchmarking
Indirect/(Decompositional Methods)Conjoint analysisPreference Regression
Behavior-BasedChoice modelsNeural networksDiscriminant analysis
Inferential/Value-BasedInternal engineering assessmentIndirect survey questionsField value-in-use assessment
New Products–12
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–13
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?
Conjoint Analysis in Product Design
New Products–14
Measuring Importance of Attributes
When choosing a restaurant, how important is… Circle one
Not Very
Important Important
Price 1 2 3 4 5 6 7 8 9
Quality of Food 1 2 3 4 5 6 7 8 9
Location 1 2 3 4 5 6 7 8 9
Decor 1 2 3 4 5 6 7 8 9
New Products–15
P rod uc tO p tion
C uisin e D ista n ce P r ice R a n ge P re ferenceR a n k
1 Ita lia n N ea r $ 1 02 Ita lia n N ea r $ 1 53 Ita lia n F a r $ 1 04 Ita lia n F a r $ 1 55 T h a i N ea r $ 1 06 T h a i N ea r $ 1 57 T h a i F a r $ 1 08 T h a i F a r $ 1 5
Simple Example ofConjoint Analysis
New Products–16
Simple Example ofConjoint Analysis
Prod uctO ption
C uisine D istance Price R ange PreferenceR ank
1 Ita lian N ear $10 82 Ita lian N ear $15 63 Ita lian Far $10 44 Ita lian Far $15 25 T hai N ear $10 76 T hai N ear $15 57 T hai Far $10 38 T hai Far $15 1
New Products–17
Example: Italian vs Thai = 20 – 16 = 4 util units $10 vs $15 = 22 – 14 = 8 util units
So “hai”is worth $2.50 more than “Italian” for this customer:
)5 0.2$)1 01 5(8
4(
Can use to obtain value to customer of service (non-price) attributes.
How to Use in Design/Tradeoff Evaluation
New Products–18
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.
Conjoint Study Process
New Products–19
An Example to Illustrate the Concepts of Conjoint Analysis: Designing a Frozen Pizza
Attributes Type of crust (3 types) Topping (4 varieties)
Type of cheese (3 types) Amount of cheese (3 levels) Price (3 levels)
Crust Type of Cheese Price Pan Romano $ 9.99 Thin Mixed cheese $ 8.99 Thick Mozzeralla $ 7.99
Topping Amount of Cheese Pineapple 2 oz. Veggie 4 oz. Sausage 6 oz. Pepperoni
A total of 324 (3 4 3 3 3) different pizzas can be developed from these options!
New Products–20
Designing a Frozen Pizza:A More Complete Design
Attributes Type of crust (3) Amount of meat (3) Types of peppers (3)
Type of cheese (3) Type of sauce (3) Presence of olives (2)Amount of cheese (3) Amount of sauce (3) Presence of oil (2)
Type of meat (3) Presence of mushrooms (2) Price (3)
Prototypes 81 prototype pizzas from 105,000 possible profiles.
Person Attributes Sex Household size Category
usage Age Favorite brand Region Presence of teenagers
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–21
Example Paired Comparison
Aloha Meat-lover’sSpecial treat
Crust Pan Thick
Topping Pineapple Pepperoni
Type of cheese Mozzarella Mixed cheese
Amount of cheese 4 oz 6 oz
Price $8.99 $9.99
Which do you prefer?
Which one would you buy?
New Products–22
Example Ratings
Product Example Bundle Type of AmountPreferenceNumber Crust Topping Cheese of Cheese Price Score
1 Pan Pineapple Romano 2 oz $9.9902 Thin Pineapple Mixed 6 oz $8.99433 Thick Pineapple Mozzarella4oz $8.99534 Thin Pineapple Mixed 4 oz $7.99565 Pan Veggie Mixed 4 oz $8.99416 Thin Veggie Romano 4 oz $7.99637 Thick Veggie Mixed 6 oz $9.99388 Thin Veggie Mozzarella2 oz $8.99539 Thick Pepperoni Mozzarella6 oz $7.996810 Thin Pepperoni Mixed 2 oz $8.994611 Pan Pepperoni Romano 4 oz $8.998012 Thin Pepperoni Mixed 4 oz $9.995813 Pan Sausage Mixed 4oz $8.9961 14 Thin Sausage Mozzarella4oz $9.99
57 15 Thick Sausage Mixed 2oz$7.99 83 16 Thin Sausage Romano6 oz $8.99 70
New Products–23
Example Computed Part-Worth for Attributes
New Products–24
Example Part-Worths for Attribute Options
New Products–25
Conjoint Computations
m ki
U(P) =aij xij
i=1 j=1
where:
P = a particular product/concept of interest,
U(P) = the utility associated with product P,
aij = Utility associated with the jth level (j = 1, 2, 3, . . . , ki) on the ith attribute (part-worth),
ki = number of levels of attribute i,
m = number of attributes, and
xij = 1 if the jth level of the ith attribute is present in product P, 0 otherwise.
New Products–26
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.
Market Share and Revenue Share Forecasts
New Products–27
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:
K Consumers who prefer iMS (Pi) = ––––––––––––––––
K=1 K
where K is the number of consumers who participated in the study.
New Products–28
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–29
Example Market Share Computation (Frozen Pizza Example)
Market consists of three products and three consumers
Product
(P1) (P2) (P3) Aloha Meat-lover’s Veggie Special Treat Delite
Crust Pan Thick Thin
Topping Pineapple Pepperoni Veggie
Type of cheese Mozzarella Mixed cheese Romano
Amt. of cheese 4 oz. 6 oz. 2 oz.
Price $8.99 $9.99 $7.99
New Products–30
Example Market Share Computation (Frozen Pizza Example)
Consumers’ Part-Worths
C1 C2 C3
Pan 0 10 26Thin 9 37 0Thick 11 0 10Pineapple 17 3 0Veggie 6 0 14Sausage 13 3 7Pepperoni 0 0 19Romano 52 0 21Mixed cheese13 9 0Mozzarella 0 3 142 oz 0 0 04 oz. 8 39 166 oz. 10 21 12$9.99 0 0 0$8.99 10 4 18$7.99 10 12 16
New Products–31
Example Market Share Computation (Frozen Pizza Example)
Computed Utility for Products Customer P1 P2 P3
C1 35 34 77C2 59 30 49C3 74 41 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–32
Share of Utility Rule
Describe competitive set
Assign individual weights if any
Compute market share
wi pij imj = ––––––––––
wi pij j i
mj: market share of product jwi: weights assigned to individual i
New Products–33
Example: Italian vs Thai = 20 – 16 = 4 util units $10 vs $15 = 22 – 14 = 8 util units
So “hai”is worth $2.50 more than “Italian” for this customer:
)5 0.2$)1 01 5(8
4(
Can use to obtain value to customer of service (non-price) attributes.
How to Use in Design/Tradeoff Evaluation
New Products–34
Another Example of Conjoint AnalysisAir 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 efficiencyDelivery timeList priceDelivery terms
Q: What to offer? Who will buy/who to target? Where will share come from?
New Products–35
New Products–36
Attributes Price (4 options) Delivery_terms (4 options)
Efficiency Delivery time List PriceExceed by 9% 6 months $600kExceed by 5% 9 months $700kMeets target 12 months $800kShort by 5% 15 months $900k
Delivery termsInstalled, 2-year guaranteeInstalled, 1-year guaranteeInstalled, service contractFOB seller, service contract
A total of 256 (4x4x4x4) different offerings can be designed from these options!
An Example Conjoint Study:Air Pollution Control Equipment
Performance specs (4 options) Delivery time (4 options)
New Products–37
Market Share Computation:(Air Pollution Control Equipment)
Sunoco Mattel ICIBase 0 0 0 Meets target 5 10 10Exceed 5% 35 0 40Exceed 9% 40 0 50 12 months 20 5 39 months 30 20 86 months 40 10 10$800k 5 20 2$700K 8 35 5$600K 10 50 10Inst_ser 6 5 10Inst_1Yr 8 10 20Inst_2Yr 10 20 30
Customer’s Utility
New Products–38
Market consists of three products and three customers
Product
Market Share Computation (Air Pollution Control Equipment)
Waste watch Thermatrix Advanced Air
Performance specs Exceed 5% Exceed 20% Meet SpecsDelivery time 9 months 9 months 6 monthsList Price $800k $900k $700k Delivery terms FOB_ser Inst_1Yr Inst_ser
New Products–39
Computed Utility for Products
Market Share Computation:(Air Pollution Control Equipment)
WasteWatch ThermatrixAdvanced Air
Sunoco 70 78 61
Mattel 40 30 75
ICI 50 78 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–40
Identifying Segments Based onConjoint Part Worths (Airpol.pwr)
Analyze Airpol.pwr file in Cluster Analysis to obtain the above results .
New Products–41
Members in Each Segment
Segment 1. Companies in this Segment include Cummins Engineering, Illinois Tools, Mattel, Neste-
Resin, 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–42
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–43
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–44
Other Aspects to Consider
Incorporate revenue potential of a product Market share Incremental margin over base product
Design optimal product by segment 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.
New Products–45
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–46
Some Commercial Applications of Conjoint Analysis
Consumer Industrial/BusinessNon-Durables Goods Other Products
1. Bar soaps 1. Copying machines 1. Automotive styling2. Hair shampoos 2. Printing equipment 2. Automobile tires3. Carpet cleaners 3. Fax machines 3. Car batteries4. Synthetic-fiber garments 4. Data transmission 4. Ethical drugs5. Gasoline pricing 5. Lap top computer 5. Employee benefit6. Pantyhose 6. Job offers to MBA’s package
Financial Services Transportation Other Services
1. Branch bank services 1. Air Canada 1. Car rental agencies2. Auto insurance policies 2. IATA 2. Telephone service pricing3. Health insurance policies 3. American Airlines 3. Hotels4. Credit card features 4. Canadian National Railway 4. Medical laboratories5. Consumer discount card 5. Amtrak 5. Employment agencies
New Products–47
Methods for ForecastingNew 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–48
The Bass Diffusion Model
Model designed to answer the question:
When will customers adopt a new product
or technology?
New Products–49
Assumptions of theBasic 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–50
Adoption Probability over Time
Time (t)
Cumulative Probability of
Adoption up to Time t
F(t)
Introduction of product
(a)
Time (t)
Density Function: Likelihood of
Adoption at Time t
f(t) = d(F(t))dt
(b)
1.0
New Products–51
Number of Cellular Subscribers
Source: Cellular Telecommunication Industry Association
9,000,000
1983 1 2 3 4 5 6 7 8 9
1,000,000
5,000,000
Years Since Introduction
New Products–52
Sales Growth Model for Durables (The Bass Diffusion Model)
St =p Remaining + q Adopters Potential Remaining Potential
Innovation Imitation Effect Effect
where:
St = sales at time t
p = “coefficient of innovation”
q = “coefficient of imitation”
# Adopters = S0 + S1 + • • • + St–1
Remaining = Total Potential – # AdoptersPotential
New Products–53
Parameters of the Bass Model in Several Product Categories
Innovation ImitationProduct/ parameter
parameter Technology (p) (q)
B&W TV 0.108 0.231Color TV 0.059 0.146Room Air conditioner 0.006 0.185Clothes dryers 0.009 0.143Ultrasound Imaging 0.000 0.534CD Player 0.055 0.378Cellular telephones 0.008 0.421Steam iron 0.031 0.128Oxygen Steel Furnace (US) 0.002 0.435Microwave Oven 0.002 0.357Hybrid corn 0.000 0.797Home PC 0.121 0.281
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–54
Technical Specificationof 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–55
Technical Specificationof the Bass Model cont’d
The Bass model proposes that L(t) is a linear function:
qL(t) = p + ––
N(t) (2)N
where
p = Coefficient of innovation (or coefficient of external influence)
q = Coefficient of imitation (or coefficient of internal influence)
N(t) = Total number of adopters of the product up to time t
N = 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:
qNf(t) = [ p + ––
N(t)][1 – N(t)] (3)N
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–56
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):
qNf(t) = [ p + –– N(t)][1 – N(t)] (3)
N
After simplification, this gives the basic diffusion equation for predicting new product sales:
qn (t) = pN + (q – p) [N(t)] – –– [N(t)]2 (4)
N
New Products–57
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:
qn(t) = p + –– N(t–1) [N –
N(t–1)]N
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–58
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 – 4acN = ––––––––––––––
2c
ap = ––
N
q = p + b
To be consistent with the model, N > 0, b 0, and c < 0.
New Products–59
Forecasting Using the Bass Model—Room Temperature Control Unit
Cumulative Quarter Sales Sales
Market Size = 16,000(At Start Price) 0 0 0
1 160 160Innovation Rate = 0.01 4 425 1,118
(Parameter p) 8 1,234 4,678 12 1,646 11,166
Imitation Rate = 0.41 16 555 15,106(Parameter q) 20 78 15,890
24 9 15,987Initial Price = $400 28 1 15,999
32 0 16,000Final Price = $400 36 0 16,000
Example computations
n(t) = pN + (q–p) N(t–1) – q N(t–1) 2/N
Sales in Quarter 1 = 0.01 16,000 + (0.41–0.01) 0 – (0.41/16,000) (0)2 = 160Sales in Quarter 2 = 0.01 16,000 + (0.40) 160 – (0.41/16,000) (160)2 =
223.35
New Products–60
Factors Affecting theRate 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–61
Some Extensions to theBasic Bass Model
Varying market potentialAs a function of product price, reduction in uncertainty in product
performance, and growth in population, and increases in retail outlets.
Incorporation of marketing variablesCoefficient 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 processAwareness Interest Adoption Word of mouth
New Products–62
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–63
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–64
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–65
Overview of ASSESSOR Measurements
Design Procedure Measurement
O1Respondent screening and Criteria for target-group identification recruitment (personal interview) (eg, product-class usage)
O2Pre-measurement for established Composition of ‘relevant set’ of brands (self-administrated established brands, attribute weights questionnaire) and ratings, and preferences
X1Exposure 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
X2Simulated shopping trip and exposure to display of new and established brands
O4Purchase opportunity (choice recorded Brand(s) purchased by research personnel)
X3Home use/consumption of new brand
O5Post-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 brandO = Measurement; X = Advertsing or product exposure
New Products–66
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–67
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–68
Repeat Model
Obtained as long-run equilibrium of the switching matrix estimated from (O2 and O5):
Time (t+1)New Other
New 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–69
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–70
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
–––IN
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–71
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–72
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–73
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 0.0010 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
New Products–74
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–75
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 tryingsamples0.251
Source: Thomas Burnham, University of Texas at Austin
New Products–76
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–77
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–78
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–79
BASES Model
Trial volume estimate
CalibratedDistribution AwarenessPt =
intent scoreintensityt levelt
Tt = Pt U0 (1/Sit) (TM) (1/CDI)
where:
Pt = Cumulative penetration up to time t Tt = Total trial volume until time t in a particular
target market U0 = Average units purchased at trial (t = 0)Sit = Seasonality index at time = tTM = Size of target marketCDI = Category development index for target
market
New Products–80
Repeat volume estimate
Rt = Ni–1,t Yit
Uii=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.
BASES Model cont’d
New Products–81
BASES Model cont’d
Total volume estimate
St = Tt Rt + Adjustments for promotional volume
New Products–82
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–83
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