【正文】 ? Factorial binations of basic attribute levels are believable. ? Desirable newproduct alternatives can be synthesized from basic alternatives. ? Product/service alternatives can be realistically described, either verbally or pictorially. (Otherwise, actual product formulations should be considered). New Products–46 Some Commercial Applications of Conjoint Analysis Consumer Industrial/Business NonDurables Goods Other Products 1. Bar soaps 1. Copying machines 1. Automotive styling 2. Hair shampoos 2. Printing equipment 2. Automobile tires 3. Carpet cleaners 3. Fax machines 3. Car batteries 4. Syntheticfiber garments 4. Data transmission 4. Ethical drugs 5. Gasoline pricing 5. Lap top puter 5. Employee benefit 6. Pantyhose 6. Job offers to MBA?s package Financial Services Transportation Other Services 1. Branch bank services 1. Air Canada 1. Car rental agencies 2. Auto insurance policies 2. IATA 2. Telephone service pricing 3. Health insurance policies 3. American Airlines 3. Hotels 4. Credit card features 4. Canadian National Railway 4. Medical laboratories 5. Consumer discount card 5. Amtrak 5. Employment agencies New Products–47 Methods for Forecasting New Product Sales Early stages of development Chain ratio method Judgmental methods Scenario Analysis Diffusion modeling Later stages of development Pretest market methods Testmarket methods New Products–48 The Bass Diffusion Model Model designed to answer the question: When will customers adopt a new product or technology? New Products–49 Assumptions of the Basic Bass Model ? Diffusion process is binary (consumer either adopts, or waits to adopt) ? Constant maximum potential number of buyers (N) ? Eventually, all N will buy the product ? No repeat purchase, or replacement purchase ? The impact of the wordofmouth is independent of adoption time ? Innovation is considered independent of substitutes ? The marketing strategies supporting the innovation are not explicitly included New Products–50 Adoption Probability over Time Time (t) Cumulative Probability of Adoption up to Time t F(t) Introduction of product (a) Time (t) Density Function: Likelihood of Adoption at Time t f(t) = d(F(t)) dt (b) New Products–51 Number of Cellular Subscribers Source: Cellular Telemunication Industry Association 9,000,000 1983 1 2 3 4 5 6 7 8 9 1,000,000 5,000,000 Years Since Introduction New Products–52 Sales Growth Model for Durables (The Bass Diffusion Model) St = p 180。W TV Color TV Room Air conditioner Clothes dryers Ultrasound Imaging CD Player Cellular telephones Steam iron Oxygen Steel Furnace (US) Microwave Oven Hybrid corn Home PC A study by Sultan, Farley, and Lehmann in 1990 suggests an average value of for p and an average value of for q. New Products–54 Technical Specification 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 Q