1 / 28

New Product Models

New Product Models. Pre-Test Market Models Product Design using Conjoint Analysis Forecasting with Diffusion Models. New Product Decision Models. Product design using conjoint analysis Forecasting the pattern of new product adoptions (Bass Model)

rocco
Download Presentation

New Product Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. New Product Models • Pre-Test Market Models • Product Design using Conjoint Analysis • Forecasting with Diffusion Models

  2. New Product Decision Models • Product design using conjoint analysis • Forecasting the pattern of new product adoptions (Bass Model) • Forecasting market share for new products in established categories (Assessor model)

  3. Opportunity Identification Market definitionIdea generation Life-Cycle Management Market response analysis & fine tuning the marketing mix; Competitor monitoring & defenseInnovation at maturity Introduction Launch planningTracking the launch Go No Testing Advertising & product testingPretest & prelaunch forecastingTest marketing The New Product Development Process Reposition Harvest Go No Design Identifying customer needs Sales forecasting Product positioning Engineering Marketing mix assessment Segmentation Go No Go No

  4. Value of Concept Testing:TV Series 2,000 Ideas 100 Scripts 20 Pilots 5 Scheduled for prime time 1 Success (?)

  5. New Product Testing from a Customer’s Perspective 1) Concept testing 2) Alpha/Beta/In-house testing 3) Laboratory test market 4) Full-scale test market

  6. Questions Answered in Concept Testing • Is the concept worth pursuing further? • Which attributes are more important (provide more value) to the consumers? • Which segments are worth pursuing with this concept? • What amount of cannibalization of current products might occur?

  7. Questions Answered in Alpha/Beta Testing • Are the product features/benefits important in use? • What problems are encountered in the use of the product? • What are the costs in use for our consumers? • Does the product perform better than the competing brands?

  8. Questions Answered in Laboratory Test Markets • What is the potential number of triers of the product? • What is the potential repeat? • What is the potential frequency of purchase? • What is the projected sales/share for new product in the first two years of introduction? • What should we do to improve the product’s chances of market success?

  9. Questions Answered in Test Markets • What is the level of awareness generated by the marketing program? • What trial, repeat, and usage rates are generated by the program? • What changes in attitude are accomplished by the program? • What levels of marketing expenditures are optimal? • What specific levels of advertising, packaging, distribution, etc. will work? • What should we do to improve the product’s chances of market success?

  10. Pretest Market Models • Objective Forecast sales/share for new product before a real test market or product launch • Conceptual model AwarenessèAvailabilityèTrialèRepeat • Commercial pre-test market services • Yankelovich, Skelly, and White • Bases • Assessor

  11. ASSESSOR Model Objectives • Predict new product’s long-term 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 competitor brands • Generate diagnostics to improve the product and its marketing program • Evaluate impact of alternative marketing mix elements such as price, package, etc.

  12. Consumer Research Input (Laboratory Measures) (Post-Usage Measures) Management Input (Positioning Strategy) (Marketing Plan) Preference Model Trial & Repeat Model Reconcile Outputs Draw & Cannibalization Estimates Brand Share Prediction Unit Sales Volume Diagnostics Overview of ASSESSORModeling Procedure

  13. Overview of ASSESSOR Measurements Design Procedure Measurement O1 Respondent screening and Criteria for target-group identification recruitment (personal interview) (eg, product-class usage) O2 Pre-measurement for established Composition of ‘relevant set’ of brands (self-administrated established brands, attribute weights questionnaire) and ratings, and preferences X1 Exposure to advertising for established brands and new brands [O3] Measurement of reactions to the Optional, e.g. likability and advertising materials (self- believability ratings of advertising administered questionnaire) materials X2 Simulated shopping trip and exposure to display of new and established brands O4 Purchase opportunity (choice recorded Brand(s) purchased by research personnel) X3 Home use/consumption of new brand O5 Post-usage measurement (telephone New-brand usage rate, satisfaction ratings, and repeat-purchase propensity; attribute ratings and preferences for ‘relevant set’ of established brands plus the new brand O = Measurement; X = Advertsing or product exposure

  14. Trial/Repeat Model Market share for new product Mn = T´R´W where: T = long-run cumulative trial rate (estimated from measurement at O4) R = long-run repeat rate (estimated from measurements at O5) W = relative usage rate, with w = 1 being the average market usage rate.

  15. Trial Model T = FKD + CU – (FKD)´ (CU) where: F = long-run probability of trial given 100% awareness and 100% distribution (from O4) K = long-run probability of awareness (from managerial judgment) D = long-run probability of product availability where target segment shops (managerial judgment and experience) C = probability of consumer receiving sample (Managerial judgment) U = probability that consumer who receives a product will use it (from managerial judgment and past experience)

  16. Repeat Model Obtained as long-run equilibrium of the switching matrix estimated from (O2 and O5): Time (t+1) New Pr. Other New Pr.p(nn) p(no) Time tOtherp(on) p(oo) p(.) are probabilities of switching where p(nn) + p(no) = 1.0; p(on) + p(oo) = 1.0 Long-run repeat given by: p(on) r = –––––––––––––– 1 + p(on) – p(nn)

  17. Preference Model: Purchase Probabilities Before New Product Use (Vij)b Lij = –––––––– Ri å (Vik)b k=1 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: 1.5–3.0)

  18. Preference Model: Purchase Probabilities After New Product Use (Vij)b L´ij = ––––––––––––––––– Ri (Vin)b + å (Vik)b k=1 where: 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´inM´n = Enå –––IN n = index for new product En = proportion of participants who include new product in their relevant sets N = number of respondents

  19. Estimating Cannibalizationand 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: LinMj = å ––– IN L´inL´inM´j = Enå ––– + (1 – En)å ––– IN IN Then the market share drawn by the new product from each of the existing products is given by: Dj = Mj – M´j

  20. Example: Preference Ratings Vij (Pre-use) V´ij (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

  21. Choice Probabilities Lij (Pre-use) L´ij (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 product’s draw from each brand (Unweighted %) 9.9 3.5 7.5 3.4 New product’s draw from each brand (Weighted by En in %) 2.0 0.7 1.5 0.7

  22. Long-term market share from advertising 0.39 Assessor Trial & Repeat Model Response Mode Manual Mode % making first purchase GIVEN awareness & availability 0.23 Prob. of awareness 0.70 Prob. of availability 0.85 Prob. of switching TO brand 0.16 Prob. of repurchase of brand 0.60 Market Share Due to Advertising • Max trial with • unlimited Ad • Ad$ for 50% • max. trial • Actual Ad $ • Max awareness • with unlimited Ad • Ad $ for 50% • max. awareness • Actual Ad $ % buying brand in simulated shopping Awareness estimate Distribution estimate (Agree) Switchback rate of non-purchasers Repurchase rate of simulation purchasers % making first purchase due to advertising 0.137 Retention rate GIVEN trial for ad purchasers 0.286 Source: Thomas Burnham, University of Texas at Austin

  23. Correction for sampling/ad overlap (take out those who tried sampling, but would have tried due to ad) 0.035 Market share trying samples 0.251 Long-term market share from sampling 0.02 Assessor Trial & Repeat Model Market Share Due to Sampling Sampling coverage (%) 0.503 % Delivered 0.90 % of those delivered hitting target 0.80 Simulation sample use Switchback rate of non-purchasers Repurchase rate of simulation non-purchasers % hitting target that get used 0.60 Prob. of switching to brand 0.16 Prob. of repurchase of brand 0.427 Retention rate GIVEN trial for sample receivers 0.218 Source: Thomas Burnham, University of Texas at Austin

  24. Draw & cannibalization calculations Assessor Preference Model Summary Pre-use preference ratings Pre-use choices Post-use preference ratings Proportion of consumers who consider product 0.137 Pre-entry market shares Post-entry market shares (assuming consideration 0.274 Weighted post entry market shares 0.038 Beta (B) for choice model Pre-use constant sum evaluations Post-use constant sum evaluations Cumulative trial from ad (T&R model) 0.137 Source: Thomas Burnham, University of Texas at Austin

  25. Market share 0.059 Market size 60M Sales per person $5 JWC factory sales 16.7 Average unit margin 0.541 Ad/sampling expense 4.5/3.5 JWC factory sales Industry average sales $ for market share 17.7 Unit-dollar adjustment 0.94 Frequency of use differences 0.9 Price differences 1.04 Net contribution JWC factory sales 16.7 Return on sales Assessor Market Share to Financial Results Diagrams Source: Thomas Burnham, University of Texas at Austin

  26. Predicted and Observed Market Shares for ASSESSOR Deviation DeviationProduct Description Initial Adjusted Actual (Initial – (Adjusted – Actual) Actual) Deodorant 13.3 11.0 10.4 2.9 0.6 Antacid 9.6 10.0 10.5 –0.9 –0.5 Shampoo 3.0 3.0 3.2 –0.2 –0.2 Shampoo 1.8 1.8 1.9 –0.1 –0.1 Cleaner 12.0 12.0 12.5 –0.5 –0.5 Pet Food 17.0 21.0 22.0 –5.0 –1.0 Analgesic 3.0 3.0 2.0 1.0 1.0 Cereal 8.0 4.3 4.2 3.8 0.1 Shampoo 15.6 15.6 15.6 0.0 0.0 Juice Drink 4.9 4.9 5.0 –0.1 –0.1 Frozen Food 2.0 2.0 2.2 –0.2 –0.2 Cereal 9.0 7.9 7.2 1.8 0.7 Etc. ... ... ... ... ... Average 7.9 7.5 7.3 0.6 0.2 Average Absolute Deviation — — — 1.5 0.6 Standard Deviation of Differences — — — 2.0 1.0

  27. Yankelovich, Skelly and White Model Forecast market share = S ´ N ´ C ´ R ´ U ´ K where: S = Lab store sales (indicator of trial), N = Novelty factor of being in lab market. Discount sales by 20–40% based on previous experience that relate trial in lab markets to trial in actual markets, C = Clout factor which retains between 25% and 75% of SN determined, based on proposed marketing effort versus ad and distribution weights of existing brands in relation to their market share, R = Repurchase rate based on percentage of those trying who repurchase, U = Usage rate based on usage frequency of new product as compared to the new product category as a whole, and K = Judgmental factor based on comparison of S ´ N ´ C ´ R ´ U´ K with Yankelovich norms. The comparison is with respect to factors such as size and growth of category, new product’s share derived from category expansion versus conversion from existing brand.

  28. Some Issues in ValidatingPre-Test Models • Validation does not include products that were withdrawn as a result of model predictions • Pre-test and actual launch are separated in time, often by a year or more • Marketing program as implemented could be different from planned program

More Related