New Prod

8/3/2019 New Prod

1/83

New Products1

New Product Decision Models

Product design using conjoint analysis

Forecasting the pattern of new product

adoptions (Bass Model)

Forecasting market share for new products inestablished categories (Assessor model)

8/3/2019 New Prod

2/83

New Products2

Newness of Products

New to

World

New to Company

Repositioning

Line Extensions

BreakthroughsMajorProduct Modifications

Me Too Products

8/3/2019 New Prod

3/83

New Products3

New Products as Part ofCorporate Strategy

Markets

Products

Existing

Existing

New

New

Market

Penetration

Market

Development

New Product

Development(Diversification)

8/3/2019 New Prod

4/83

New Products4

The New Product DevelopmentProcess

DesignIdentifying customer needs Sales

forecasting

Product positioning Engineering

Marketing mix assessment Segmentation

Opportunity IdentificationMarket definition

Idea generation

TestingAdvertising & product testing

Pretest & prelaunch forecasting

Test marketing

IntroductionLaunch planning

Tracking the launch

Life-Cycle ManagementMarket response analysis & fine tuning the

marketing mix; Competitor monitoring & defense

Innovation at maturity

Go No

Go No

Go No

Go No

RepositionHarvest

8/3/2019 New Prod

5/83

Impact of Product Superiority on

Product Success

18.4

58

98

0

50

100

Succes

srate(%)

Mkt Share11.6%

Minimal Moderate Maximal

Product Superiority

Mkt Share

32.4%

Mkt Share

53.5%

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 managements 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 Products5

8/3/2019 New Prod

6/83

Impact of Early Product Definition onProduct Success

26.2

64.285.4

0

50

100

Successra

te(%)

Mkt Share

22.9

Poor Moderate Strong

Product Definition

Mkt Share

36.5

Mkt Share

37.3%

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

New Products6

8/3/2019 New Prod

7/83

Impact of Market Attractiveness onProduct Success

73.961.5

42.5

0

50

100

S

uccessrate

(%)

Mkt Share31.7

Low Moderate High

Market Attractiveness

Mkt Share

33.7

Mkt Share

36.5%

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

New Products7

8/3/2019 New Prod

8/83

Resources Allocated at Each Stage of NPD

57

315.3

435.9

148.4

553.2

203.8

0

100

200300

400

500

600

PredevelopmentActivities Product development& product testing Commercialization

Mean Expenditure($000K)

Mean Person-Days

Source: Robert G. Cooper (1993)

New Products8

8/3/2019 New Prod

9/83New Products9

Value of Good Design

80% of a products manufacturing costs areincurred during the first 20% of its design(varies with product category).

Conjoint Analysis is a systematic approachfor matching product design with the needsand wants of customers, especially in the

early stages of the New ProductDevelopment process.

Source: Mckinsey & Company Report

8/3/2019 New Prod

10/83New Products10

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 productcategory

The value (utility) provided to customers by each

potential feature of a product

What is Conjoint Analysis?

8/3/2019 New Prod

11/83New Products11

Customer Value AssessmentProcedures

Customer

Value

Attitude-Based

Direct Questions

Unconstrained

Focus groups

Direct survey questions

Importance and attitude ratings

rule-based system/AI/expert systems

Constrained/Compositional Methods

Multiattribute value analysis

Benchmarking

Indirect/(Decompositional Methods)

Conjoint analysis

Preference Regression

Behavior-Based

Choice models

Neural networks

Discriminant

analysis

Inferential/Value-Based

Internal engineering assessment

Indirect survey questions

Field value-in-use assessment

8/3/2019 New Prod

12/83New Products12

Why is Customer Value Assessmentthrough Conjoint Useful?

Design new products that enhance customer value.

Forecast sales/market share/profit of alternative productconcepts.

Identify market segments for which a given conceptoffers high value.

Identify the best concept for a target segment.

Explore impact of alternative pricing and servicestrategies.

Help production planning in flexible manufacturingsystems.

8/3/2019 New Prod

13/83

New Products13

Should we offer our business travelers more room space or afax machine in their room?

Given a target cost for a product, should we enhance productreliability or its performance?

Should we use a steel or aluminum casing to increase

customer preference for the new equipment?

Conjoint Analysis in Product Design

8/3/2019 New Prod

14/83

New Products14

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 9Decor 1 2 3 4 5 6 7 8 9

8/3/2019 New Prod

15/83

New Products15

ProductOption

Cuisine Distance Price Range PreferenceRank

1 Italian Near $10

2 Italian Near $153 Italian Far $104 Italian Far $155 Thai Near $106 Thai Near $157 Thai Far $108 Thai Far $15

Simple Example ofConjoint Analysis

8/3/2019 New Prod

16/83

New Products16

Simple Example ofConjoint Analysis

ProductOption

Cuisine Distance Price Range PreferenceRank

1 Italian Near $10 8

2 Italian Near $15 63 Italian Far $10 44 Italian Far $15 25 Thai Near $10 76 Thai Near $15 5

7 Thai Far $10 38 Thai Far $15 1

8/3/2019 New Prod

17/83

New Products17

Example: Italian vs Thai = 2016 = 4 util units$10 vs $15 = 2214 = 8 util units

SoItalianis worth $2.50 more than Thai for thiscustomer:

)50.2$)1015(8

4(

Can use to obtain value to customer ofservice (non-price) attributes.

How to Use in Design/TradeoffEvaluation

8/3/2019 New Prod

18/83

New Products18

Stage 1Design 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 2Obtain 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-worthfunctions.

Stage 3Evaluate 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

8/3/2019 New Prod

19/83

New Products19

An Example to Illustrate the Concepts of ConjointAnalysis: 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 PricePan Romano $ 9.99Thin Mixed cheese $ 8.99Thick Mozzeralla $ 7.99

Topping Amount of CheesePineapple 2 oz.Veggie 4 oz.Sausage 6 oz.Pepperoni

A total of 324 (3 4 3 3 3) different pizzas can be developed fromthese options!

8/3/2019 New Prod

20/83

New Products20

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)

Prototypes81 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.

8/3/2019 New Prod

21/83

New Products21

Example Paired Comparison

Aloha Meat-loversSpecial treat

Crust Pan ThickTopping 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?

8/3/2019 New Prod

22/83

New Products22

Example Ratings

Product ExampleBundle Type of Amount PreferenceNumber Crust Topping Cheese of Cheese Price Score

1 Pan Pineapple Romano 2 oz $9.99 0

2 Thin Pineapple Mixed 6 oz $8.99 433 Thick Pineapple Mozzarella 4 oz $8.99 534 Thin Pineapple Mixed 4 oz $7.99 565 Pan Veggie Mixed 4 oz $8.99 416 Thin Veggie Romano 4 oz $7.99 637 Thick Veggie Mixed 6 oz $9.99 388 Thin Veggie Mozzarella 2 oz $8.99 539 Thick Pepperoni Mozzarella 6 oz $7.99 68

10 Thin Pepperoni Mixed 2 oz $8.99 4611 Pan Pepperoni Romano 4 oz $8.99 8012 Thin Pepperoni Mixed 4 oz $9.99 5813 Pan Sausage Mixed 4 oz $8.99 6114 Thin Sausage Mozzarella 4 oz $9.99 5715 Thick Sausage Mixed 2 oz $7.99 8316 Thin Sausage Romano 6 oz $8.99 70

8/3/2019 New Prod

23/83

New Products23

Example Computed Part-Worthfor Attributes

8/3/2019 New Prod

24/83

New Products24

Example Part-Worths forAttribute Options

8/3/2019 New Prod

25/83

New Products25

Conjoint Computations

m ki

U(P) = aij xiji=1 j=1

where:

P = a particular product/concept of interest,

U(P) = the utility associated with productP,

aij = Utility associated with thejth level (j = 1, 2, 3, . . . ,ki) on the ithattribute (part-worth),

ki = number of levels of attribute i,

m = number of attributes, and

xij = 1 if thejth level of the ith attribute is present in productP,0 otherwise.{

8/3/2019 New Prod

26/83

New Products26

Define the competitive set -- these are the products fromwhich the target segment make choices. Some of theses maybe existing products and, others concepts being evaluated. Wedenote 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 therevenue index of 100 for a base product you select.

Market Share and Revenue ShareForecasts

8/3/2019 New Prod

27/83

New Products27

Market Share Forecast(Maximum Utility Rule)

The relevant market consists of productsP1,P2, . . . ,PN. Some oftheses may be existing products and, others concepts beingevaluated.

Each consumer will prefer to buy the product with the highestutility among those available.

Then forecasted market share for productsPi is given by:

K Consumers who prefer i

MS (Pi) = K=1 K

whereKis the number of consumers who participated in thestudy.

8/3/2019 New Prod

28/83

New Products28

Other Choice Rules

Share of utility rule: Under this choice rule, the consumerselects each product with a probability that is proportional tothe utility of that compared to the total utility derived fromall 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 preferredalternatives and smaller weights to less preferredalternatives.

Alpha rule: Modified version of share of utility rule. Beforeapplying the share of utility, the utility functions are modifiedby an alpha factor so that the computed market shares ofexisting products are as close as possible to their actualmarket shares.

8/3/2019 New Prod

29/83

New Products29

Example Market Share Computation(Frozen Pizza Example)

Market consists of three products and three consumers

Product(P1) (P2) (P3)

Aloha Meat-lovers VeggieSpecial 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

8/3/2019 New Prod

30/83

New Products30


Consumers Part-Worths

C1 C2 C3

Pan 0 10 26Thin 9 37 0

Thick 11 0 10Pineapple 17 3 0Veggie 6 0 14Sausage 13 3 7Pepperoni 0 0 19Romano 52 0 21Mixed cheese 13 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

8/3/2019 New Prod

31/83

New Products31


Computed Utility for ProductsCustomer P1 P2 P3

C1 35 34 77

C2 59 30 49C3 74 41 51

Infrequently purchased products:Consumers only buy the brand with the highest utility. Then, themarket 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%

8/3/2019 New Prod

32/83

New Products32

Share of Utility Rule

Describe competitive set

Assign individual weights if any

Compute market share

wi pijimj = w

ip

ijj i

mj: market share of productj

wi: weights assigned to individual i

8/3/2019 New Prod

33/83

New Products33

Example: Italian vs Thai = 2016 = 4 util units$10 vs $15 = 2214 = 8 util units

SoThaiis worth $2.50 more than Italian for thiscustomer:

)50.2$)1015(8

4(

Can use to obtain value to customer ofservice (non-price) attributes.

How to Use in Design/TradeoffEvaluation

8/3/2019 New Prod

34/83

New Products34

Another Example of Conjoint AnalysisAir Pollution Control Systems

Drr 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?

8/3/2019 New Prod

35/83

New Products35

8/3/2019 New Prod

36/83

New Products36

Attributes

Price (4 options)

Delivery_terms (4 options)

Efficiency Delivery time List Price

Exceed by 9% 6 months $600kExceed by 5% 9 months $700k

Meets target 12 months $800k

Short by 5% 15 months $900k

Delivery terms

Installed, 2-year guaranteeInstalled, 1-year guarantee

Installed, service contract

FOB 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)

8/3/2019 New Prod

37/83

New Products37

Market Share Computation:(Air Pollution Control Equipment)

Sunoco Mattel ICI

Base 0 0 0

Meets target 5 10 10

Exceed 5% 35 0 40Exceed 9% 40 0 50

12 months 20 5 3

9 months 30 20 8

6 months 40 10 10$800k 5 20 2

$700K 8 35 5

$600K 10 50 10

Inst_ser 6 5 10

Inst_1Yr 8 10 20Inst_2Yr 10 20 30

Customers Utility

8/3/2019 New Prod

38/83

New Products38

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 Specs

Delivery time 9 months 9 months 6 months

List Price $800k $900k $700kDelivery terms FOB_ser Inst_1Yr Inst_ser

8/3/2019 New Prod

39/83

New Products39

Computed Utility for Products

Market Share Computation:(Air Pollution Control Equipment)

WasteWatch Thermatrix Advanced Air

Sunoco 70 78 61

Mattel 40 30 75ICI 50 78 40

Maximum Utility Rule: If we assume customers will only buy the productwith the highest utility, the market share for Thermatrix is 2/3 and 1/3 forWahlco.

Share of preference rule: If we assume that each customer will buy eachproduct in proportion to its utility relative to the other products, thenmarket shares for the three products are:

Waste Watch: 30.3% Thermatrix: 34.8 Advanced Air: 34.9

8/3/2019 New Prod

40/83

New Products40

Identifying Segments Based onConjoint Part Worths (Airpol.pwr)

Analyze Airpol.pwr file in Cluster Analysis to obtain the above results.

8/3/2019 New Prod

41/83

New Products41

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, andThermal Electric.

These are smaller companies that operate inindustries without major pollution problems. Theywant an equipment that meets EPA efficiency target,

medium delivery times, have high price sensitivity,and require installation and warranty.

8/3/2019 New Prod

42/83

New Products42



ICI, Mobil, Maytag, Texaco, Union Carbide, DowChemicals, Boise Cascade, and 3M.

These are large chemical and paper companies thathave pollution issues to deal with. They want anequipment that Exceeds EPA efficiency target, havelong delivery times (perhaps for installation in newfactories that they build), have moderate price

sensitivity, and do not require installation help orwarranty (FOB).

8/3/2019 New Prod

43/83

New Products43



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

Electric.

These are large companies that seem to operate inindustries with less severe pollution problems.They want an equipment that Exceeds EPAefficiency target, prefer quick/medium delivery,

have low price sensitivity, and moderately preferinstallation and warranty.

8/3/2019 New Prod

44/83

New Products44

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 EPAtarget, with delivery of 6 months, priced at 600K, and withinstallation and 2-year warranty has the potential to get42% share of the market and good revenue potentialagainst the three existing competitors.

Segment 3 (Premium segment): A product that exceedsEPA target by 5%, with delivery of 9 months, priced at

700K, and with installation and 2-year warranty has thepotential to get 31% share and high revenue potential.

Sit ti Wh C j i t A l i

8/3/2019 New Prod

45/83

New Products45

Situations Where Conjoint AnalysisMight 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 basicattributes.

Product/service choice tends to be high involvement.

Factorial combinations of basic attribute levels are believable.

Desirable new-product alternatives can be synthesized frombasic alternatives.

Product/service alternatives can be realistically described,either verbally or pictorially. (Otherwise, actual productformulations should be considered).

8/3/2019 New Prod

46/83

New Products46

Some Commercial Applications ofConjoint Analysis

Consumer Industrial/Business

Non-Durables Goods Other Products

1. Bar soaps 1. Copying machines 1. Automotive styling

2. Hair shampoos 2. Printing equipment 2. Automobile tires

3. Carpet cleaners 3. Fax machines 3. Car batteries

4. Synthetic-fiber garments 4. Data transmission 4. Ethical drugs

5. Gasoline pricing 5. Lap top computer 5. Employee benefit

6. Pantyhose 6. Job offers to MBAs package

Financial Services Transportation Other Services

1. Branch bank services 1. Air Canada 1. Car rental agencies

2. Auto insurance policies 2. IATA 2. Telephone service pricing

3. Health insurance policies 3. American Airlines 3. Hotels

4. Credit card features 4. Canadian National Railway 4. Medical laboratories

5. Consumer discount card 5. Amtrak 5. Employment agencies

8/3/2019 New Prod

47/83

New Products47

Methods for ForecastingNew Product Sales

Early stages of development

Chain ratio method

Judgmental methodsScenario Analysis

Diffusion modeling

Later stages of development

Pre-test market methods

Test-market methods

8/3/2019 New Prod

48/83

New Products48

The Bass Diffusion Model

Model designed to answer the question:

When will customers adopt a new

productor technology?

8/3/2019 New Prod

49/83

New Products49

Assumptions of theBasic Bass Model

Diffusion process is binary (consumer either adopts,

or waits to adopt)

Constant maximum potential number of buyers (N)

Eventually, allNwill 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 innovationare not explicitly included

8/3/2019 New Prod

50/83

New Products50

Adoption Probability over Time

Time (t)

CumulativeProbability of

Adoption up to

Timet

F(t)

Introductionof product

(a)

Time (t)

Density Function:Likelihood of

Adoption at Timet

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

(b)

1.0

8/3/2019 New Prod

51/83

New Products51

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

8/3/2019 New Prod

52/83

New Products52

Sales Growth Model for Durables(The Bass Diffusion Model)

St = p Remaining + q Adopters Potential Remaining Potential

Innovation ImitationEffect Effect

where:

St = sales at time t

p = coefficient of innovation

q = coefficient of imitation

# Adopters = S0 + S1 + + St1

Remaining = Total Potential # AdoptersPotential

8/3/2019 New Prod

53/83

New Products53

Parameters of the Bass Model inSeveral Product Categories

Innovation ImitationProduct/ parameter parameter

Technology (p) (q)

B&W TV 0.108 0.231

Color 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 0.421

Steam 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 forp and an average value of 0.38 for q.

8/3/2019 New Prod

54/83

New Products54

Technical Specificationof the Bass Model

The Bass Model proposes that the likelihood that someone in the population willpurchase a new product at a particular time tgiven that she has not alreadypurchased the product until then, is summarized by the following mathematical.

FormulationLet:

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

Oncef(t) is specified, then F(t) is simply the cumulative distribution off(t), andfrom Bayes Theorem, it follows that:

L(t) = f(t)/[1F(t)] (1)

S

8/3/2019 New Prod

55/83

New Products55

Technical Specificationof the Bass Model contd

The Bass model proposes thatL(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 tis equal to

Nf(t) . From (1), it then follows that:q

Nf(t) = [p + N(t)][1N(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 totime t[ =N(t)].

8/3/2019 New Prod

56/83

New Products56

Bass Model contd

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)][1N(t)] (3)

N

After simplification, this gives the basic diffusion equation for

predicting new product sales:

qn (t) = pN + (qp) [N(t)] [N(t)]2 (4)

N

E i i h P f h B

8/3/2019 New Prod

57/83

New Products57

Estimating the Parameters of the BassModel Using Non-Linear Regression

An equivalent way to representN(t) in the Bass modelis the following equation:

qn(t) = p + N(t1) [NN(t1)]

N

Given four or more values ofN(t) we can estimate thethree parameters of the above equation to minimizethe sum of squared deviations.

E i i h P f h

8/3/2019 New Prod

58/83

New Products58

Estimating the Parameters of theBass Model Using Regression

The discretized version of the Bass model is obtained from (4):

n(t) = a + bN(t1) + cN2(t1)

a, b, and c may be determined from ordinary least squares regression.The values of the model parameters are then obtained as follows:

b b24acN =

2c

ap =

N

q = p + b

To be consistent with the model,N> 0, b 0, and c < 0.

8/3/2019 New Prod

59/83

New Products59

Forecasting Using the Bass ModelRoomTemperature Control Unit

CumulativeQuarter Sales Sales

Market Size = 16,000(At Start Price) 0 0 0

1 160 160

Innovation Rate = 0.01 4 425 1,118(Parameterp) 8 1,234 4,678

12 1,646 11,166Imitation 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 + (qp)N(t1) qN(t1) 2/N

Sales in Quarter 1 = 0.01 16,000 + (0.410.01) 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

F t Aff ti th

8/3/2019 New Prod

60/83

New Products60

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 involveswitching from familiar way of doing things?)

Communication channels used

Nature of links among market participants

Nature and effect of promotional efforts

Some Extensions to the

8/3/2019 New Prod

61/83

New Products61

Some Extensions to theBasic Bass Model

Varying market potentialAs a function of product price, reduction in uncertainty inproduct performance, and growth in population, andincreases in retail outlets.

Incorporation of marketing variablesCoefficient of innovation (p) as a function of advertising

p(t) = a + b lnA(t).

Effects of price and detailing.

Incorporating repeat purchases

Multi-stage diffusion processAwareness Interest Adoption Word ofmouth

8/3/2019 New Prod

62/83

New Products62

Pretest Market Models

Objective

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

Conceptual model

AwarenessAvailabilityTrialRepeat

Commercial pre-test market services

Yankelovich, Skelly, and White

Bases

Assessor

8/3/2019 New Prod

63/83

New Products63

ASSESSOR Model

Objectives

Predict new products long-term market share, and

sales volume over time

Estimate the sources of the new products share,

which includes cannibalization of the firms

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.

O i f A

8/3/2019 New Prod

64/83

New Products64

Overview of ASSESSORModeling Procedure

Management Input

(Positioning Strategy)

(Marketing Plan)

Reconcile

Outputs

Draw &

Cannibalization

Estimates DiagnosticsUnit Sales

Volume

Preference

Model

Trial &

Repeat Model

Brand Share

Prediction

Consumer Research Input

(Laboratory Measures)

(Post-Usage Measures)

8/3/2019 New Prod

65/83

New Products65

Overview of ASSESSOR Measurements

Design Procedure Measurement

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

O2 Pre-measurement for established Composition of relevant set ofbrands (self-administrated established brands, attribute weightsquestionnaire) and ratings, and preferences

X1 Exposure to advertising for establishedbrands and new brands

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

X2 Simulated shopping trip and exposureto 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, andrepeat-purchase propensity; attribute ratingsand preferences for relevant set ofestablished brands plus the new brand

O = Measurement; X= Advertsing or product exposure

8/3/2019 New Prod

66/83

New Products66

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.

8/3/2019 New Prod

67/83

New Products67

Trial Model

T = FKD + CU (FKD) (CU)

where:

F = long-run probability of trial given 100% awareness and100% 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 willuse it (from managerial judgment and past experience)

8/3/2019 New Prod

68/83

New Products68

Repeat Model

Obtained as long-run equilibrium of the switching matrix estimatedfrom (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)

Preference Model: Purchase

8/3/2019 New Prod

69/83

New Products69

Preference Model: PurchaseProbabilities Before New Product Use

where:

Vij = Preference rating from productj by participant i

Lij = Probability that participant i will purchase productj

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: 1.53.0)

(Vij)b

Lij = Ri

(Vik)b

k=1

Preference Model: Purchase

8/3/2019 New Prod

70/83

New Products70

Preference Model: PurchaseProbabilities After New Product Use

where:Lit = Choice probability of productj after participant i

has had an opportunity to try the new product

b = index obtained earlier

Then, market share for new product:

Lin

Mn = En I N

n = index for new product

En = proportion of participants who include new productin their relevant sets

N = number of respondents

(Vij)b

Lij = Ri

(Vin)b + (Vik)

bk=1

Estimating Cannibalization

8/3/2019 New Prod

71/83

New Products71

Estimating Cannibalizationand Draw

Partition the group of participants into two: those who include new product intheir consideration sets, and those who dont. The weighted pre- and post-market shares are then given by:

Lin

Mj = I N

Lin LinMj = En + (1En)

I N I N

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

Dj = Mj Mj

8/3/2019 New Prod

72/83

New Products72

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 3.13 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

8/3/2019 New Prod

73/83

New Products73

Choice Probabilities

Lij (Pre-use) Lij (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 0.035 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 marketshare (%) 38.0 28.3 23.6 10.1 28.1 24.8 16.1 6.7 24.3

New products draw from each brand(Unweighted %) 9.9 3.5 7.5 3.4

New products draw from each brand(Weighted byEn in %) 2.0 0.7 1.5 0.7

8/3/2019 New Prod

74/83

New Products74

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

Distribution

estimate (Agree)

Switchback rate of

non-purchasers

Repurchase rate

of simulation

purchasers

% 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

% making first

purchase due to

advertising

0.137

Retention rate

GIVEN trial

for ad purchasers

0.286

Response Mode Manual Mode

Long-term

market share

from advertising

0.39

Source: Thomas Burnham, University of Texas at Austin

8/3/2019 New Prod

75/83

New Products75

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

Prob. of switching

TO brand

0.16

Prob. of repurchase

of brand

0.427

Long-term

market share

from sampling

0.02

% hitting target

that get used

0.60

Retention rate

GIVEN trial

for sample receivers

0.218

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


Assessor Preference Model

8/3/2019 New Prod

76/83

New Products76

Assessor Preference ModelSummary


Pre-use constant

sum evaluations

Post-use constant

sum evaluations

Cumulative trialfrom ad

(T&R model)

0.137

Beta (B) for

choice model

Pre-entry market

shares

Post-entry market

shares (assuming

consideration

0.274

Weighted

post entry

market shares

0.038

Pre-use preference

ratings

Pre-use choices

Post-use preference

ratings

Proportion of

consumers who

consider product

0.137 Draw &

cannibalization

calculations

Assessor Market Share to

8/3/2019 New Prod

77/83

New Products77

Assessor Market Share toFinancial 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

Net

contribution

JWC

factory sales

16.7

Industry average

sales $ for

market share

17.7

JWC

factory sales

Frequency of use

differences

0.9

Unit-dollar

adjustment

0.94

Price differences

1.04

Return

on sales


Predicted and Observed Market

8/3/2019 New Prod

78/83

New Products78

Predicted and Observed MarketShares for ASSESSOR

Deviation DeviationProduct 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 0.5

Shampoo 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 0.1

Frozen 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

8/3/2019 New Prod

79/83

New Products79

BASES Model

Trial volume estimate

Calibrated Distribution AwarenessPt = intent score intensityt leveltTt = Pt U0 (1/Sit) (TM) (1/CDI)

where:

Pt = Cumulative penetration up to timet

Tt = Total trial volume until timet in a particular target marketU0 = Average units purchased at trial (t = 0)Sit = Seasonality index at time =t

TM = Size of target market

CDI = Category development index for target market

8/3/2019 New Prod

80/83

New Products80

Repeat volume estimate

Rt = Ni1,tYit Ui

i=1

where:

Ni1,t = Cumulative number of consumers who repeat at least i1 times byweekt (N0,t= initial trial volume)

Yit = Conditional cumulative ith repeat purchase rate at weekt given thati1 repeat purchases were made up to weekt

Ui = Average units purchased at repeat level i

Ni1,t & Yit are estimated based on consumers stated after use intendedpurchase frequency and estimate of long-run decay in repeat rate.

Ui is estimated based on consumers stated purchase quantities.

BASES Model contd

8/3/2019 New Prod

81/83

New Products81

BASES Model contd

Total volume estimate

St = Tt Rt + Adjustments for promotionalvolume

Yankelovich Skelly and White

8/3/2019 New Prod

82/83

New Products82

Yankelovich, Skelly and WhiteModel

Forecast market share = S N C R U Kwhere:

S = Lab store sales (indicator of trial),

N = Novelty factor of being in lab market. Discount sales by 2040% basedon previous experience that relate trial in lab markets to trial in actualmarkets,

C = Clout factor which retains between 25% and 75% ofSNdetermined,based on proposed marketing effort versus ad and distribution weightsof 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 thenew product category as a whole, and

K = Judgmental factor based on comparison ofS N C R U Kwith Yankelovich norms. The comparison is with respect to factors suchas size and growth of category, new products share derived fromcategory expansion versus conversion from existing brand.

Some Issues in Validating

8/3/2019 New Prod

83/83

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 bedifferent from planned program

New Prod

Documents

Transcript of New Prod