New Product Strategy

1. New Product Strategy Sales Forecasting February 27, 2007

2. Estimating Sales Potential

3. Sales Potential Estimation • Often used to interpret concept test results

4. The Concept Statement

5. Sales Potential Estimation • Often used from concept test results • Assumes awareness and availability • Translating “Intent” into sales potential: • Develop the “norms” carefully for a specific market and for specific launch practices • Examples: • Services: 45% chance that the “definitely would buys” actually will buy; 15% for the “probably will”s • Consumer Packaged Goods: 70-80% chance that the “definites” will buy; 33% chance for the “probably will”s

6. Sales Potential Estimation

7. Sales Potential Estimation • Translating Intent into Sales Potential • Example: Aerosol Hand CleanerAfter examining norms for comparable existing products, you determine that: • 90% of the “definites” • 40% of the “probables” • 10% of the “mights” • 0% of the “probably nots” and “definitely nots”will actually purchase the product • Apply those %age to Concept Test results:

8. Sales Potential Estimation • Translating Intent into Sales Potential • Apply those %age to Concept Test results: • 90% of the “definites” (5% of sample) = .045 • 40% of the “probables” (36%) = .144 • 10% of the “mights” (33%) = .033 • 0% of the last 2 categories = .000 • Sum them to determine the %age who would actually buy: .045+.144+.033= .22 • Thus, 22% of sample population would buy(remember: this % is conditioned on awareness & availability)

9. From Potential to Forecast • With Sales Potential Estimates: • To remove the conditions of awareness and availability, multiply by the appropriate percentages: • If 60% of the sample will be aware (via advertising, etc.) and the product will be available in 80% of the outlets, then: • (.22) X (.60) X (.80) = .11 • 11% of the sample is likely to buy

10. Sales Forecasts • With Sales Potential Estimates • A-T-A-R Models • Best used with incremental innovations • Based on diffusion theory: • Awareness, Trial, Availability, Repeat

11. ATAR

12. An A-T-A-R Model of Innovation Diffusion Figure 8.5 Profits = Units Sold x Profit Per Unit Units Sold = Number of buying units x % aware of product x % who would try product if they can get it x % to whom product is available x % of triers who become repeat purchasers x Number of units repeaters buy in a year Profit Per Unit = Revenue per unit - cost per unit

13. The A-T-A-R Model: Definitions Figure 8.6 • Buying Unit: Purchase point (person or department/buying center). • Aware: Has heard about the new product with some characteristic that differentiates it. • Available: If the buyer wants to try the product, the effort to find it will be successful (expressed as a percentage). • Trial: Usually means a purchase or consumption of the product. • Repeat: The product is bought at least once more, or (for durables) recommended to others.

14. A-T-A-R Model Application 10 million Number of owners of Walkman-like CD players x 40% Percent awareness after one year x 20% Percent of "aware" owners who will try product x 70% Percent availability at electronics retailers x 20% Percent of triers who will buy a second unit x $50 Price per unit minus trade margins and discounts ($100) minus unit cost at the intended volume ($50) = $5,600,000 Profits

15. Points to Note About A-T-A-R Model 1. Each factor is subject to estimation. Estimates improve with each step in the development phase. 2. Inadequate profit forecast can be improved by changing factors. If profit forecast is inadequate, look at each factor and see which can be improved, and at what cost.

16. 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. Why Financial Analysis for New Products is Difficult

17. Getting the Estimates for A-T-A-R Model Figure 8.7 xx: Best source for that item. x: Some knowledge gained.

18. 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.

19. “Futurists” • Consumer insight • Ethnographies • Trend reports

20. 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. Source: a 1967 forecast by The Futurist journal. Note: about two-thirds of the forecasts were correct!

21. Forecast: Generational Shifts Civic (Millennials) (GI Generation) • Correct ills of Reactive • Era of prosperity and strength • Pervasive optimism • Uplifting patriotic sentiment Reactive(GenX) Adaptive (Silent) • Follow trends from Civic • More complacent • Head down hard work and life enjoyment • Left reacting to changes initiatedby Idealists • Often era of economic downturn • Feelings of negativity and disenfranchisement ubiquitous Idealist (Boomers) • Change agents as tired of / rebel against status quo of Adaptive • Era of volatility (economic, political, social, etc.)

22. Trends!

23. 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 • 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.

24. Bass Model Forecast ofProduct Diffusion Figure 11.4

25. 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.