CHAPTER 11 CHAPTER 11 SALES FORECASTING SALES FORECASTING AND FINANCIAL ANALYSIS AND FINANCIAL ANALYSIS McGraw-Hill/Irwin Copyright ©2006 The McGraw-Hill Companies, Inc. All right reserved. Why Financial Analysis for New Why Financial Analysis for New Products is Difficult Products is Difficult  Target users don’t know.  If they know they might not tell us.  Poor execution of market research.  Market dynamics.  Uncertainties about marketing support.  Biased internal attitudes.  Poor accounting.  Rushing products to market.  Basing forecasts on history.  Technology revolutions. Forecasters Are Often Right Forecasters Are Often Right  In 1967 they said we would have:  Artificial organs in humans by 1982.  Human organ transplants by 1987.  Credit cards almost eliminating currency by 1986.  Automation throughout industry including some managerial decision making by 1987.  Landing on moon by 1970.  Three of four Americans living in cities or towns by 1986.  Expenditures for recreation and entertainment doubled by 1986. Figure 11.1 Forecasters Can Be Very Wrong Forecasters Can Be Very Wrong  They also said we would have:  Permanent base on moon by 1987.  Manned planetary landings by 1980.  Most urbanites living in high-rises by 1986.  Private cars barred from city cores by 1986.  Primitive life forms created in laboratory by 1989.  Full color 3D TV globally available. Figure 11.1 (cont’d.) Source: a 1967 forecast by The Futurist journal. Note: about two-thirds of the forecasts were correct! Commonly Used Forecasting Commonly Used Forecasting Techniques Techniques Figure 11.2 Handling Problems in Financial Analysis Handling Problems in Financial Analysis  Improve your existing new products process.  Use the life cycle concept of financial analysis.  Reduce dependence on poor forecasts.  Forecast what you know.  Approve situations, not numbers (recall Campbell Soup example)  Commit to low-cost development and marketing.  Be prepared to handle the risks.  Don’t use one standard format for financial analysis.  Improve current financial forecasting methods. Forecasting Sales Using Purchase Forecasting Sales Using Purchase Intentions Intentions  Use top-two-boxes scores obtained in concept testing, appropriately adjusted or calibrated.  Example: Recall for hand cleanser from Chapter 9:  Definitely buy = 5%  Probably buy = 36%  Based on history, calibrate as follows:  80% of “definitelies” actually buy  33% of “probablies” actually buy  Forecasted market share = (0.8)(5%) + (0.33) (36%) = 16%. Forecasting Sales Using Purchase Forecasting Sales Using Purchase Intentions (continued) Intentions (continued)  The 16% forecast assumes 100% awareness and availability.  Adjust downwards to account for incomplete awareness and availability.  If 60% of the market is aware of the product and has it available, market share is recalculated to (0.6) (16%) = 9.6%. Forecasting Sales Using A-T-A-R Model Forecasting Sales Using A-T-A-R Model  Assume awareness = 90% and availability =67%.  Trial rate = 16% (16% of the market that is aware of the product and has it available tries it at least once).  R S = proportion who switch to new product = 70%.  R r = proportion who repeat purchase the new product = 60%.  R t = Long-run repeat purchase = R S /(1+R s -R r ) = 63.6%.  Market Share = T x R t x Awareness x Availability = 16% x 63.6% x 90% x 67% = 6.14%. The following bar chart shows this procedure graphically. A-T-A-R Model Results: Bar Chart A-T-A-R Model Results: Bar Chart Format Format Figure 11.3 [...]... of Product Diffusion Figure 11. 4 The Life Cycle of Assessment Figure 11. 5 Calculating New Product’s Required Rate of Return Figure 11. 6 % Return Reqd Rate of Return Cost of Capital Avg Risk Risk on of Firm Proposed Product Risk Hurdle Rates on Returns and Other Measures Figure 11. 8 Product A B C Strategic Role or Purpose Sales Combat competitive entry Establish foothold in new market Capitalize on existing... Robert G Cooper, Scott J Edgett, and Elko J Kleinschmidt Portfolio Management for New Products, McMaster University, Hamilton, Ontario, Canada, 1997, pp 24-28 Specialty Minerals Scoring Model        Management interest Customer interest Sustainability of competitive advantage Technical feasibility Business case strength Fit with core competencies Profitability and impact Manufacturing Firm Scoring... should reflect a product’s purpose, or assignment Example: we might accept a very low share increase for an item that simply capitalized on our existing market position Hoechst-U.S Scoring Model Figure 11. 9 Key Factors Probability of Technical Success Probability of Commercial Success Reward Business-Strategy Fit Strategic Leverage 1 ……… . CHAPTER 11 CHAPTER 11 SALES FORECASTING SALES FORECASTING AND FINANCIAL ANALYSIS AND FINANCIAL ANALYSIS McGraw-Hill/Irwin Copyright ©2006 The. Techniques Techniques Figure 11. 2 Handling Problems in Financial Analysis Handling Problems in Financial Analysis  Improve your existing new products process.  Use the life cycle concept of financial analysis.  Reduce. and marketing.  Be prepared to handle the risks.  Don’t use one standard format for financial analysis.  Improve current financial forecasting methods. Forecasting Sales Using Purchase Forecasting