New Product Decisions

MGT 453

Managerial Issues Related toForecasting

• What is the purpose of developing the forecast?• What, specifically, do we want to forecast (e.g.,

market demand, technology trends)?• How important is the past in predicting the

future?• What influence do we have in constructing the

future?• What method(s) should we use to develop the

forecast?• What factors could change the forecast?

New Product Forecasting Models

• Forecasting using conjoint analysis

• Forecasting the pattern of new product adoptions (Bass Model)

• Forecasting market share for new products in established categories (Assessor pre-test market model)

Forecasting Based on “Newness” of Products

New to World

New to Company

• RepositioningPre-test market model

• Line ExtensionsSimple pre-test market models (e.g., Bases)

• Breakthroughs—Major Product ModificationsBass model/Conjoint

• “Me Too” ProductsConjoint/Pre-test market models

Lo Hi

L oH i

Overview of “Stage-Gate” New Product Development Process

DesignIdentifying customer needs Sales forecasting

Product positioning EngineeringMarketing mix assessment Segmentation

Opportunity IdentificationMarket definitionIdea generation

TestingAdvertising & product testingPretest & prelaunch forecastingTest marketing

IntroductionLaunch planningTracking 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

The Bass Diffusion Model ofNew Product Adoption

The model attempts to answer the question:

When will customers adopt a new product or technology?

Why is it important to address this question?

Graphical Representation of The Bass Model (Cell Phone Adoption)

Time

Non

-cu

mu

lati

ve A

dop

tion

s, n

(t)

pN Adoptions due to external influence

Adoptions due to internal influence

0102030405060708090

100110120130140150160170180190200210

1997 '98 '99 '00 '01 '02 '03 '04 '05 '06

million

Source: eBay/SEC filings

Number of Registered Users eBay (by Quarter)

Q1 0.09 Q2 0.15 Q3 0.25 Q4 0.40

1997

The Bass Diffusion Model for Durables

nt = p Remaining + q Adopter Proportion Potential Remaining Potential

Innovation Imitation Effect Effect

nt = number of adopters at time t (Sales)p = “coefficient of innovation” (External influence)q = “coefficient of imitation” (Internal influence)

= Eventual number of adopters# Adopters = n0 + n1 + • • • + nt–1

Remaining = Total Potential – # AdoptersPotential

N

Assumptions of theBasic Bass Model

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

included.• Uniform influence or complete mixing. That is, everyone in the

population knows everyone else, or is at least able to communicate with, or observe everyone else.

N

N

Representation as an Equation

)1...()(

)]([)(

N

tNqptNNtn

N(t) : Cumulative number of adopters until time t.

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.

Estimating the Parameters of the Bass Model

• Estimation using data– Regression– Specialized nonlinear estimation

• Estimation using analogous products– Select analogous products based on the similarity

in environmental context, market structure, buyer behavior, marketing-mix strategies of the firm, and innovation characteristics.

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

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

)()/()()()( 11 2 tNNqtNpqNptn

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

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.

• Incorporating 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

• Incorporating Network Structure

Effects of Network Structure(Household Products)

Distant links = 0 Distant links > 0

Average Density of Links

q –

Deg

ree

of I

nfl

uenc

e

DirecTVHistory and Technology

• 1984 FCC grants GM Hughes approval to construct a Direct Broadcast Satellite system (DBS)

• High Ku-Band frequency• Early 1990’s technological breakthrough in

digital compression-Result: Affordable product and non-obtrusive dish and equipment

• Changed economics of DTH broadcasting• 1991 DIRECTV founded

ME New Product Forecasting 2006 - 19

DirecTVData Collection Method

• CATI phone-mail-phone data collection-nationally representative sample of TV viewers.

• 15-minute phone interview. “Eligibles” assigned to one of two monadic concept-price cells (“Intent to Buy”).

• Respondents mailed a color brochure that described DIRECTV/RCA branded Direct Broadcast System concept.

• Phone callback interview (22 minutes)-Key inputs: Stated Intentions (Probability of Acquire and Perceived value and Affordability).

Obtaining p, q, and

• Guessing p and q from analogous previously introduced product

• from stated intentions in survey• Average stated intent from survey = 32%• Stated intentions overstate actual choices.

How much to discount stated intent to adopt?• Also, have to adjust each year’s predicted sales

for awareness and availability (remember Kirin case?)

N

N

ME New Product Forecasting 2006 - 21

Adjusting Stated Intentions to Get Actual Purchase Behavior

Probability of purchase given stated intent for new durable and non-durable products. From Jamieson, Linda F. and Frank M. Bass "Adjusting Stated Intention...To Predict Trial Purchase of New Products," JMR, August 1989.

0

5

10

15

20

25

30

35

40

45

Definitely Will Not Buy Probably Will Not Buy Might or Might Not Buy Probably Will Buy Definitely Will Buy

Pro

bab

ility

of

Pu

rch

ase

Actual Purchase Probablity Given Stated Intention for 5 Non-Durable Products Actual Purchase Probability Given Stated Intention for 5 Durable Products

Purchase Increases withStated Intention

Some Who SayThey Won’t, Do!

Some Who SayThey Will, Don’t

Probability of PurchaseIncreases with Stated Intention

Multi-Year Forecast and Actual

Year

1992 Forecast Number of TV Homes Acquiring Satellite Television (Million)

Actual Number of TV Homes Acquiring Satellite Television (Million)

1992 Forecast of Percent of TV Homes with Satellite Television (Percentage)

Actual Yearly Percent of TV Homes with Satellite Television (Percentage)

7/01/94 - 6/30/95 0.875 1.15 0.92 1.217/01/95 - 6/30/96 2.269 3.076 2.37 3.217/01/96 - 6/30/97 4.275 5.076 4.42 5.257/01/97 - 6/30/98 6.775 7.358 6.95 7.557/01/98 - 6/30/99 9.391 9.989 9.55 10.16

9.4 Million TV homes forecast for June 99; Actual = 9.9 Million

Forecast based on p and q of Cable TV (other alternative considered was Color TV) and maximum penetration set to 16% of population (half that in the stated intent survey).

Using Scenario Analysisfor Calibrating the Bass Model

• Structure a scenario as a flowing narrative, not as a set of numerical parameters. Include verbal descriptions such as “rapid experience effects,” “FCC adoption of digital standard,” etc. Ideally, each scenario should also include how the situation described in the scenario will be reached from the present position.

• Construct several scenarios that capture the richness and range of the “possibilities” relevant to a decision situation. Describe all the scenarios in the same manner, i.e., one is not more “vivid” than another. Focus your further analyses on scenarios that are internally consistent and plausible. Develop forecasts and strategies that are compatible with the scenarios:– Robust approaches that are resilient across scenarios (e.g.,

hedging, concurrent pursuit of multiple options, etc.)– Contingent approaches that postpone major commitments to the

future.

Steps in Scenario Planningfor Zenith HDTV

• Identify the major stakeholders.• Summarize the core trends that are relevant (technological,

economic, social, etc.) within the time frame of interest.• Articulate the main uncertainties (e.g., TV studio adoption of new

filming methods).• Construct an initial set of scenarios.• Assess the consistency and plausibility of the scenarios.• Create “themes” (i.e., a story with a name) that combine some

trends into meaningful composites (e.g., a Japanese domination of hardware and American domination of software).

• Identify areas where you need more research (e.g., consumer acceptance) and seek additional information.

• Associate the final set of scenarios with potential product analogs for diffusion model, and select p and q.

• Evaluate decision consequences based on the implications of the diffusion model.

ME New Product Forecasting 2006 - 25

Example “Middle of the Road” Scenario(Zenith HDTV case)

The FCC makes a commitment to the 16:9 NTSC HDTV standard in 1994, with promises to release details in a year. Initial HDTV sets cost over $3,000 and are seen as a luxury item, little programming is available so new features (such as use as computer monitors and compatibility with analog signals) are integrated to justify purchases. Art studios and other display locations become innovators as they purchase units for displays. Interior designers realize the benefits of HDTV plasma screens and suggest purchases to their wealthiest clients. HDTV becomes a “nouveau riche” item, a status symbol much like luxury cars. By 2000, the manufacturing costs of Plasma and other flat-screen displays decrease drastically from standards integration and increased competition. Middle-class customers can now afford HDTV displays. The movie industry embraces digital recordings because of the ease in editing and persistent quality. New movie features (screen and TV) are filmed in 16:9 digital format. Subsequent releases on DVD show higher quality. Public TV stations cannot justify the cost of upgrading, but cable channels such as HBO and Showtime commit to upgrading in 2003. Their recent entry into movie-making and their purchase of new high-tech digital recording equipment coincides with the need to upgrade transmission hardware. Customers are then driven to adopt technology not for increased quality on regular programming, but for movie watching, design, and display of other items.

ME New Product Forecasting 2006 - 26

Population (billions)

Gro

ss W

orld

Pro

du

ct (

$ tr

illio

ns)

1990

105

20

250

Comparative Trajectories of Population/GDP From Global Scenario Group

Great TransitionConventional

Worlds

Barbarization

Fortress World

Breakdown

Policy Reform

Reference

Eco-communalism

New sustainabilityparadigm

ME New Product Forecasting 2006 - 27

Pretest Market Models

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

• Conceptual modelAwareness Availability Trial Repeat

• Commercial pre-test market services– Yankelovich, Skelly, and White – Bases– Assessor

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

å (Vik)b

k=1

ME New Product Forecasting 2006 - 29

Preference Model: Purchase Probabilities After New Product Use

where:

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

b =index obtained earlier

Then, market share for new product:L´in

M´n = En –––I N

n =index for new product

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

N =number of respondents

(Vij)b

L´ij = –––––––––––––––––Ri

(Vin)b + å (Vik)b

k=1

ME New Product Forecasting 2006 - 30

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:

Lij Mj = –––I N

L´ij L´ij 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

ME New Product Forecasting 2006 - 31

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 0.22 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 2.34 3.1 3.4 0.0 0.0 3.3 3.4 0.0 0.0 0.75 0.0 1.3 0.0 0.0 0.0 1.2 0.0 0.0 0.06 4.1 0.0 0.0 0.0 4.3 0.0 0.0 0.0 2.17 0.4 2.1 0.0 2.9 0.4 2.1 0.0 1.6 0.18 0.6 0.2 0.0 0.0 0.6 0.2 0.0 0.0 5.09 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

ME New Product Forecasting 2006 - 32

Choice Probabilities

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

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

10 0.02 0.00 0.98 0.00 0.04 0.00 0.89 0.00 0.07Unweighted market share (%) 38.0 28.3 23.6 10.1 28.1 24.8 16.1 6.7 24.3New product’s draw from each brand (Unweighted %) 9.9 3.5 7.5 3.4 New product’s draw from each brand (Weighted by En in %) 2.0 0.7 1.5 0.7