NEW PRODUCTS MANAGEMENT Merle Crawford Anthony Di Benedetto

Chapter 11 Sales Forecasting and Financial Analysis 11 -2

Why Financial Analysis for New 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. 11 -3

Forecasting the Demand For Satellite Radio • In 2000: forecast for 2007 was 36 million subscribers. • In 2001: forecast revised to 16 million. • By end of 2006: actual number of subscribers = 11 million. Source: Sarah Mc. Bride, “Until Recently Full Of Promise, Satellite Radio Runs Into Static, ” Wall Street Journal, August 15, 2006, pp. A 1 -A 9. 11 -4

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. 11 -5

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 3 D TV globally available. Source: a 1967 forecast by The Futurist journal. Note: about two-thirds of the forecasts were correct! 11 -6

Commonly Used Forecasting Techniques 11 -7

Forecasting Satellite Radio Sales Using Purchase Intentions • • • In 2000, 213 million vehicles in U. S. 95% availability, 40% awareness. Market potential = 213 million x 95% x 40% = 81 million. Assume half can afford satellite radio = 40. 5 million. Percentage that will be among the first to try the new technology = 16%. Forecast for first year = 40. 5 million x 16% = 6. 4 million. Projected yearly growth rate = 10%. Assuming this growth rate, by end of 2006, expected total sales = about 10 million. Note: not too far from the attained number = 11 million! 11 -8

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. 11 -9

Forecasting Sales Using Purchase 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%. 11 -10

Forecasting Sales Using Purchase 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%. 11 -11

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). • RS = proportion who switch to new product = 70%. • Rr = proportion who repeat purchase the new product = 60%. • Rt = Long-run repeat purchase = RS /(1+Rs-Rr) = 63. 6%. • Market Share = T x Rt x Awareness x Availability = 16% x 63. 6% x 90% x 67% = 6. 14%. The following bar chart shows this procedure graphically. 11 -12

A-T-A-R Model Results: Bar Chart Format 11 -13

Bass Model Forecast of Product Diffusion 11 -14

The Life Cycle of Assessment 11 -15

Calculating New Product’s Required Rate of Return % Return Reqd. Rate of Return Cost of Capital Avg. Risk of Firm Risk on Proposed Product Risk 11 -16

Real-Options Analysis in New Product Value Assessment Data: • Startup costs in Year 0: $70, 000. • The cash flows for Years 1 through 4 are estimated to be $40, 000 in a high-demand scenario, or $10, 000 in a lowdemand scenario. • The probabilities of a high- or low-demand scenario are both 50 percent. • The product concept could be abandoned after Year 1, and the equipment could be sold for $38, 000. • Discount rate = 12%. 11 -17

Real-Options Analysis (continued) Cash flow in Year 1 for each demand scenario: Demand Year 1 Year 2 Year 3 Year 4 Total High 40, 000 Low 10, 000 40, 000/(1. 12) = 35, 714 10, 000/(1. 12) = 8, 929 40, 000/(1. 12)2 = 40, 000/(1. 12)3 = $136, 073 31, 888 28, 471 2 = 7, 972 10, 000/(1. 12)3 = 7, 118 $34, 018 10, 000/(1. 12) Cash flow in Year 1 if option taken to abandon project and equipment is sold: Demand Year 1 Low 10, 000 Take Option to Abandon and Sell Equipment 38, 000 Total $48, 000 Therefore the project would be abandoned after Year 1. 11 -18

Real-Options Analysis (continued) Now assess NPV for each demand scenario, assuming project is abandoned after Year 1 if demand is low. Demand Year 0 Year 1 Year 2 Year 3 Year 4 Total High -70, 000 40, 000/(1. 12)2 = 31, 888 40, 000/(1. 12)3 = 28, 471 40, 000/(1. 12)4 = 25, 421 $51, 494 Low -70, 000 40, 000/(1. 12) = 35, 714 48, 000/(1. 12) = 42, 857 -$27, 143 Expected value of investment is: (0. 5)($51, 494) + (0. 5)(-27, 143) = $12, 176 Since this expected value is greater than zero, the firm should make the investment. Source: Edward Nelling, "Options and the Analysis of Technology Projects, " in V. K. Narayanan and Gina C. O'Connor (eds. ), Encyclopedia of Technology & Innovation Management, Chichester, UK: John Wiley, 2010, Chapter 8. 11 -19

Hurdle Rates on Returns and Other Measures Explanation: the hurdles 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. 11 -20

Hoechst-U. S. Scoring Model 11 -21

Specialty Minerals Scoring Model • • Management interest Customer interest Sustainability of competitive advantage Technical feasibility Business case strength Fit with core competencies Profitability and impact 11 -22

Manufacturing Firm Scoring Model (disguised) • Net Present Value • Internal Rate of Return • Strategic Importance of Project (how well it aligns with business strategy) • Probability of Technical Success Note how in each of these examples, the model contains financial as well as strategic criteria. 11 -23

A Tool for Concept Evaluation Strategic Fit Does the concept fit with corporate vision? Customer Fit Does the concept allow the customer to better meet consumer needs? Consumer Fit Does the concept satisfy an unmet consumer need? Market Attractiveness Is the concept unique relative to competition? Technical Feasibility Is the concept feasible and protectable? Financial Returns Will the project break even soon? Source: Erika B. Seamon, “Achieving Growth Through an Innovative Culture, ” in P. Belliveau, A. Griffin, and S. M. Somermeyer, The PDMA Handbook 3 For New Product Development, Wiley, 2004, Ch. 1. 11 -24