New Product Decisions

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New Product Decisions

• MGT 453

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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?

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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)

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Forecasting Based on Newness of Products

• BreakthroughsMajor Product Modifications
• Bass model/Conjoint

• Repositioning
• Pre-test market model

Hi
New to World

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

• Me Too Products
• Conjoint/Pre-test market models

Lo
Hi
Lo
New to Company
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Overview of Stage-Gate New Product Development
Process
Reposition
Harvest
Go
No
Design Identifying customer needs Sales
forecasting Product positioning Engineering Mark
eting mix assessment Segmentation
Go
No
Go
No
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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?

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Graphical Representation of The Bass Model (Cell
Phone Adoption)
Adoptions due to internal influence
Non-cumulative Adoptions, n(t)
Adoptions due to external influence
pN
Time
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Number of Registered Users eBay (by Quarter)
million
1997
Source eBay/SEC filings
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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
nt1 Remaining Total Potential
Adopters Potential
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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.

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Representation as an Equation
N(t) Cumulative number of adopters until time t.
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Parameters of the Bass Model in Several Product
Categories
Innovation Imitation Product/ parameter
parameter Technology (p) (q) BW
TV 0.108 0.231 Color TV 0.059 0.146 Room Air
conditioner 0.006 0.185 Clothes
dryers 0.009 0.143 Ultrasound Imaging 0.000 0.534
CD Player 0.055 0.378 Cellular telephones 0.008 0.
421 Steam iron 0.031 0.128 Oxygen Steel Furnace
(US) 0.002 0.435 Microwave Oven 0.002 0.357 Hybrid
corn 0.000 0.797 Home 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.
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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.

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Forecasting Using the Bass ModelRoom Temperature
Control Unit
Cumulative Quarter Sales
Sales Market Size 16,000 (At Start
Price) 0 0 0 1 160 160 Innovation
Rate 0.01 4 425 1,118 (Parameter
p) 8 1,234 4,678 12 1,646 11,166 Imita
tion Rate 0.41 16 555 15,106 (Parameter
q) 20 78 15,890 24 9 15,987 Initial
Price 400 28 1 15,999 32 0 16,000 Fin
al Price 400 36 0 16,000 Example
computations 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
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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

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Some Extensions to theBasic 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.
• Incorporating 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 ? Interest ? Adoption ? Word of
  mouth
• Incorporating Network Structure

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Effects of Network Structure(Household Products)
Distant links 0 Distant links gt 0
q Degree of Influence
Average Density of Links
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DirecTVHistory and Technology

• 1984 FCC grants GM Hughes approval to construct a
  Direct Broadcast Satellite system (DBS)
• High Ku-Band frequency
• Early 1990s technological breakthrough in
  digital compression-Result Affordable product
  and non-obtrusive dish and equipment
• Changed economics of DTH broadcasting
• 1991 DIRECTV founded

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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).

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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 years predicted sales
  for awareness and availability (remember Kirin
  case?)

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Adjusting Stated Intentions to Get Actual
Purchase Behavior
Probability of Purchase Increases with Stated
Intention
Some Who Say They Will, Dont
Some Who Say They Wont, Do!
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Multi-Year Forecast and Actual
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).
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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.

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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.

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Example Middle of the Road Scenario(Zenith
HDTV case)

• The FCC makes a commitment to the 169 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 169 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.

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Comparative Trajectories of Population/GDP From
Global Scenario Group
250
Conventional Worlds
Great Transition
Eco-communalism
Policy Reform
Reference
Gross World Product ( trillions)
New sustainability paradigm
Fortress World
20
1990
Breakdown
Barbarization
10
5
Population (billions)
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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

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Preference Model Purchase Probabilities Before
New Product Use
(Vij)b Lij Ri å (Vik)b k1
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 1.53.0)
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Preference Model Purchase Probabilities After
New Product Use
(Vij)b Lij Ri (Vin)
b å (Vik)b k1

• where
• Lit 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
• Lin Mn En å I N
• n index for new product
• En proportion of participants who include new
  product in their relevant sets
• N number of respondents

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Estimating Cannibalizationand Draw

• Partition the group of participants into two
  those who include new product in their
  consideration sets, and those who dont. The
  weighted pre- and post- market shares are then
  given by
• Lij Mj å I N
• Lij Lij Mj En å (1
  En) å
• I N I N
• Then the market share drawn by the new product
  from each of the existing products is given by
• Dj Mj Mj

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Example Preference Ratings

• Vij (Pre-use) Vij (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

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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 0.03
• 5 0.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00
• 6 1.00 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.20
• 7 0.01 0.35 0.00 0.64 0.03 0.61 0.00 0.36 0.00
• 8 0.89 0.11 0.00 0.00 0.02 0.00 0.00 0.00 0.98
• 9 0.79 0.21 0.00 0.00 0.82 0.18 0.00 0.00 0.00
• 10 0.02 0.00 0.98 0.00 0.04 0.00 0.89 0.00 0.07
• Unweighted market share () 38.0 28.3 23.6 10.
  1 28.1 24.8 16.1 6.7 24.3
• New products draw from each brand (Unweighted
  ) 9.9 3.5 7.5 3.4
• New products draw from each brand (Weighted
  by En in ) 2.0 0.7 1.5 0.7