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  • People getting used to the idea of buying in auctions. Auctions help determine the correct price for products. Many antique dealers now use eBay to gauge the price of their inventory and products.
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  • Explain logic of projecting trends – at the end of the 20 th century, trends would indicate that by 1980, we would have trains traveling at 500 miles per hour! It happened, but with different technology (airplane).
  • Group your knowledge into two areas: (1) things you believe you know something about, and (2) elements you consider uncertain or unknowable. The first component casts the past forward, recognizing that our world possesses considerable momentum and continuity. For example, we can safely make assumptions about demographic shifts (such as increases in the average age) and substitution effects of new technologies (e.g., digital recording will eventually replace analog recording). Your challenge is to separate aspects you are very confident about (and willing to bet the farm on) from those that are largely uncertain. There are at least three tests of internal consistency: trends, outcome combinations, and reactions of major stakeholders. First, are the trends compatible within the chosen time frame? If not, remove the trends that don't fit. Second, do the scenarios combine outcomes of uncertainties that indeed go together? Japanese analog standards and evolution of digital technologies are not compatible with each other; so eliminate that possible pairing or scenario. Third, are the major stakeholders (e.g., TV studios) see themselves placedin positions they do not like, and can change?
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Transcript

  • 1. New Product Decisions MGT 453
  • 2. Managerial Issues Related to Forecasting
    • 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?
  • 3. 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)
  • 4. Forecasting Based on “ Newness ” of Products New to World New to Company
    • Repositioning
    • Pre-test market model
    • Line Extensions
    • Simple pre-test market models (e.g., Bases)
    • Breakthroughs—Major Product Modifications
    • Bass model/Conjoint
    • “ Me Too” Products
    • Conjoint/Pre-test market models
    Lo Hi Lo Hi
  • 5. 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
  • 6. 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?
  • 7. 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
  • 8. million Source: eBay/SEC filings Number of Registered Users eBay (by Quarter) 1997
  • 9. The Bass Diffusion Model for Durables
    • 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
  • 10. Assumptions of the Basic 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.
  • 11. Representation as an Equation N(t) : Cumulative number of adopters until time t.
  • 12. 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 .
  • 13. 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.
  • 14. Forecasting Using the Bass Model—Room Temperature Control Unit Cumulative Quarter Sales Sales Market Size = 16,000 (At Start Price) 0 0 0 1 160 160 Innovation 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 15,987 Initial Price = $400 28 1 15,999 32 0 16,000 Final 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 = 160 Sales in Quarter 2 = 0.01 ´ 16,000 + (0.40) ´ 160 – (0.41/16,000) ´ (160) 2 = 223.35
  • 15. Factors Affecting the Rate 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
  • 16. Some Extensions to the Basic 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
  • 17. Effects of Network Structure (Household Products) Distant links = 0 Distant links > 0 Average Density of Links q – Degree of Influence
  • 18. DirecTV History 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
  • 19. DirecTV Data 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) .
    ME New Product Forecasting 2006 -
  • 20. 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?)
  • 21. 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
  • 22. 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).
  • 23. 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.
  • 24. Steps in Scenario Planning for 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.
  • 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 $3,000 and are seen as a luxury item, little programming is available so new features (such as use as computer monitors and compatibility with analog signals) are integrated to justify purchases. Art studios and other display locations become innovators as they purchase units for displays. Interior designers realize the benefits of HDTV plasma screens and suggest purchases to their wealthiest clients. HDTV becomes a “nouveau riche” item, a status symbol much like luxury cars. By 2000, the manufacturing costs of Plasma and other flat-screen displays decrease drastically from standards integration and increased competition. Middle-class customers can now afford HDTV displays. The movie industry embraces digital recordings because of the ease in editing and persistent quality. New movie features (screen and TV) are filmed in 16:9 digital format. Subsequent releases on DVD show higher quality. Public TV stations cannot justify the cost of upgrading, but cable channels such as HBO and Showtime commit to upgrading in 2003. Their recent entry into movie-making and their purchase of new high-tech digital recording equipment coincides with the need to upgrade transmission hardware. Customers are then driven to adopt technology not for increased quality on regular programming, but for movie watching, design, and display of other items.
    ME New Product Forecasting 2006 -
  • 26. ME New Product Forecasting 2006 - Population (billions) Gross World Product ($ 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
  • 27. 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
    ME New Product Forecasting 2006 -
  • 28. 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
  • 29. 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
  • 30. Estimating Cannibalization and 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:
    • L ij M j =  ––– I N
    • L´ ij L´ ij M ´ j = E n  ––– + (1 – E n )  –––
    • I N I N
    • Then the market share drawn by the new product from each of the existing products is given by:
    • D j = M j – M´ j
    ME New Product Forecasting 2006 -
  • 31. Example: Preference Ratings
    • V ij (Pre-use) V ´ ij (Post-use)
    • Customer B 1 B 2 B 3 B 4 B 1 B 2 B 3 B 4 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
    ME New Product Forecasting 2006 -
  • 32. Choice Probabilities
    • L ij (Pre-use) L ´ ij (Post-use) Customer B 1 B 2 B 3 B 4 B 1 B 2 B 3 B 4 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 product’s draw from each brand (Unweighted %) 9.9 3.5 7.5 3.4
    • New product’s draw from each brand (Weighted by E n in %) 2.0 0.7 1.5 0.7
    ME New Product Forecasting 2006 -