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
Pre-test market model
Simple pre-test market models (e.g., Bases)
Breakthroughs—Major Product Modifications
“ Me Too” Products
Conjoint/Pre-test market models
Lo Hi Lo Hi
Overview of “Stage-Gate” New Product Development Process Design Identifying customer needs Sales forecasting Product positioning Engineering Marketing mix assessment Segmentation Go No Go No Go No Reposition Harvest Opportunity Identification Market definition Idea generation Testing Advertising & product testing Pretest & prelaunch forecasting Test marketing Introduction Launch planning Tracking the launch Life-Cycle Management Market response analysis & fine tuning the marketing mix; Competitor monitoring & defense Innovation at maturity Go No
The Bass Diffusion Model of New 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-cumulative Adoptions, n(t) pN Adoptions due to external influence Adoptions due to internal influence
million Source: eBay/SEC filings Number of Registered Users eBay (by Quarter) 1997
n t = p Remaining + q Adopter Proportion Potential Remaining Potential
Innovation Imitation Effect Effect
n t = number of adopters at time t (Sales) p = “coefficient of innovation” (External influence) q = “coefficient of imitation” (Internal influence) = Eventual number of adopters # Adopters = n 0 + n 1 + • • • + n t –1 Remaining = Total Potential – # Adopters Potential
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.
Representation as an Equation N(t) : Cumulative number of adopters until time t.
Parameters of the Bass Model in Several Product Categories Innovation Imitation Product/ parameter parameter Technology ( p ) ( q ) B&W 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 .
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?)
Adjusting Stated Intentions to Get Actual Purchase Behavior ME New Product Forecasting 2006 - Some Who Say They Won’t, Do! Some Who Say They Will, Don’t Probability of Purchase Increases with Stated Intention
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).
Using Scenario Analysis for 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.
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.
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 $3,000 and are seen as a luxury item, little programming is available so new features (such as use as computer monitors and compatibility with analog signals) are integrated to justify purchases. Art studios and other display locations become innovators as they purchase units for displays. Interior designers realize the benefits of HDTV plasma screens and suggest purchases to their wealthiest clients. HDTV becomes a “nouveau riche” item, a status symbol much like luxury cars. By 2000, the manufacturing costs of Plasma and other flat-screen displays decrease drastically from standards integration and increased competition. Middle-class customers can now afford HDTV displays. The movie industry embraces digital recordings because of the ease in editing and persistent quality. New movie features (screen and TV) are filmed in 16:9 digital format. Subsequent releases on DVD show higher quality. Public TV stations cannot justify the cost of upgrading, but cable channels such as HBO and Showtime commit to upgrading in 2003. Their recent entry into movie-making and their purchase of new high-tech digital recording equipment coincides with the need to upgrade transmission hardware. Customers are then driven to adopt technology not for increased quality on regular programming, but for movie watching, design, and display of other items.
ME New Product Forecasting 2006 -
ME New Product Forecasting 2006 - Population (billions) Gross World Product ($ trillions) 1990 10 5 20 250 Comparative Trajectories of Population/GDP From Global Scenario Group Great Transition Conventional Worlds Barbarization Fortress World Breakdown Policy Reform Reference Eco-communalism New sustainability paradigm
Forecast sales/share for new product before a real test market or product launch
Awareness Availability Trial Repeat
Commercial pre-test market services
Yankelovich, Skelly, and White
ME New Product Forecasting 2006 -
Preference Model: Purchase Probabilities Before New Product Use where : V ij = Preference rating from product j by participant i L ij = Probability that participant i will purchase product j R i = 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) ( V ij ) b L ij = –––––––– R i å ( V ik ) b k =1
Preference Model: Purchase Probabilities After New Product Use
w here :
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 = E n ––– I N
n = index for new product
E n = proportion of participants who include new product in their relevant sets
N = number of respondents
ME New Product Forecasting 2006 - ( V ij ) b L´ ij = ––––––––––––––––– R i ( V in ) b + å ( V ik ) b k =1