【導(dǎo)讀】NewProducts–1. adoptions(BassModel). NewProducts–2. ―Newness‖ofProducts. Newto. World. NewtoCompany. Repositioning. LineExtensions. Breakthroughs—Major. ―MeToo‖Products. NewProducts–3. CorporateStrategy. Markets. Products. Existing. Existing. New. New. Market. Peration. Market. Development. NewProduct. Development(Diversification). NewProducts–4. Process. Design. forecasting. Marketdefinition. Ideageneration. Testing. Advertising&producttesting. Testmarketing. Introduction. Launchplanning. Trackingthelaunch. GoNo. GoNo. GoNo. GoNo. RepositionHarvest. ProductSuccess. 18.4. 58. 98. 0. 50. 100. S. u. c. c. e. s. s. r. a. t. e. %. MktShare. 11.6%. ProductSuperiority. MktShare. 32.4%. MktShare. 53.5%. NewProducts–5. ProductSuccess. 26.2. 64.2. 85.4. 0. 50. 100. S. u. c. c. e. s. s. r. a. t. e. %. MktShare. 22.9. PoorModerateStrong. ProductDefinition. MktShare. 36.5. MktShare. 37.3%. NewProducts–6. ProductSuccess. 73.9. 61.5. 42.5. 0. 50. 100. S. u. c. c. e. s. s. r. a. t. e. %. MktShare. 31.7. LowModerateHigh. MktShare. 33.7. MktShare. 36.5%. NewProducts–7. 57. 315.3. 435.9. 148.4. 553.2. 203.8. 0. 100. 200. 300. 400. 500. 600. Predevelopment. Activities. Productdevelopment. &producttesting. Commercialization. MeanExpenditure. MeanPerson-Days. NewProducts–8. NewProducts–9. ValueofGoodDesign. Source:Mckinsey&CompanyReport. NewProducts–10. maketradeoffsbetwe

　　

【正文】 of the Bass Model The Bass Model proposes that the likelihood that someone in the population will purchase a new product at a particular time t given that she has not already purchased the product until then, is summarized by the following mathematical. Formulation Let: 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 Once f(t) is specified, then F(t) is simply the cumulative distribution of f(t), and from Bayes Theorem, it follows that: L(t) = f(t)/[1–F(t)] (1) New Products–55 Technical Specification of the Bass Model cont’d The Bass model proposes that L(t) is a linear function: q L(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 t is equal to Nf(t) . From (1), it then follows that: q Nf(t) = [ p + –– N(t)][1 – N(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 to time t [ = N(t)]. New Products–56 Bass Model cont’d 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): q Nf(t) = [ p + –– N(t)][1 – N(t)] (3) N After simplification, this gives the basic diffusion equation for predicting new product sales: q n (t) = pN + (q – p) [N(t)] – –– [N(t)]2 (4) N New Products–57 Estimating the Parameters of the Bass Model Using NonLinear Regression An equivalent way to represent N(t) in the Bass model is the following equation: q n(t) = p + –– N(t–1) [N – N(t–1)] N Given four or more values of N(t) we can estimate the three parameters of the above equation to minimize the sum of squared deviations. New Products–58 Estimating the Parameters of the Bass Model Using Regression The discretized version of the Bass model is obtained from (4): n(t) = a + bN(t–1) + cN 2(t–1) a, b, and c may be determined from ordinary least squares regression. The values of the model parameters are then obtained as follows: –b – b2 – 4ac N = –––––––––––––– 2c a p = –– N q = p + b To be consistent with the model, N 0, b 0, and c 0. ?New Products–59 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 = 4 425 1,118 (Parameter p) 8 1,234 4,678 12 1,646 11,166 Imitation Rate = 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 putations n(t) = pN + (q–p) N(t–1) – q N(t–1) 2/N Sales in Quarter 1 = 180。 16,000 + (–) 180。 0 – (,000) 180。 (0)2 = 160 Sales in Quarter 2 = 180。 16,000 + () 180。 160 – (,000) 180。 (160)2 = New Products–60 Factors Affecting the Rate of Diffusion Productrelated ? High relative advantage over existing products ? High degree of patibility with existing approaches ? Low plexity ? Can be tried on a limited basis ? Benefits are observable Marketrelated ? Type of innovation adoption decision (eg, does it involve switching from familiar way of doing things?) ? Communication channels used ? Nature of “l(fā)inks” among market participants ? Nature and effect of promotional efforts New Products–61 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. ? Incorporation of 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 ? Multistage diffusion process Awareness ? Interest ? Adoption ? Word of mouth New Products–62 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 pretest market services ? Yankelovich, Skelly, and White ? Bases ? Assessor New Products–63 ASSESSOR Model Objectives ? Predict new product?s longterm market share, and sales volume over time ? Estimate the sources of the new product?s share, which includes “cannibalization” of the firm?s existing products, and the “draw” from petitor brands ? Generate diagnostics to improve the product and its marketing program ? Evaluate impact of alternative marketing mix elements such as price, package, etc. New Products–64 Overview of ASSESSOR Modeling Procedure Management Input (Positioning Strategy) (Marketing Plan) Reconcile Outputs Draw amp。 Cannibalization Estimates Diagnostics Unit Sales Volume Preference Model Trial amp。 Repeat Model Brand Share Prediction Consumer Research Input (Laboratory Measures) (PostUsage Measures) New Products–65 Overview of ASSESSOR Measurements Design Procedure Measurement O1 Respondent screening and Criteria for targetgroup identification recruitment (personal interview) (eg, productclass usage) O2 Premeasurement for established Composition of ?relevant set? of brands (selfadministr