New Product Strategy Sales Forecasting February 27, 2007

Estimating Sales Potential

Sales Potential Estimation Often used to interpret concept test results

The Concept Statement

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

Sales Potential Estimation

Translating Intent into Sales Potential Example: Aerosol Hand Cleaner After 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:

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

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

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

ATAR

An A-T-A-R Model of Innovation Diffusion 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 Figure 8.5

The A-T-A-R Model: Definitions 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. Figure 8.6

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

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.

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.

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

Forecasters Are Often Right In 1967 they said we would have: Artificial organs in humans by Human organ transplants by Credit cards almost eliminating currency by Automation throughout industry including some managerial decision making by Landing on moon by Three of four Americans living in cities or towns by Expenditures for recreation and entertainment doubled by 1986.

“Futurists” Consumer insight Ethnographies Trend reports

Forecasters Can Be Very Wrong They also said we would have: Permanent base on moon by Manned planetary landings by Most urbanites living in high-rises by Private cars barred from city cores by Primitive life forms created in laboratory by Full color 3D TV globally available. Source: a 1967 forecast by The Futurist journal. Note: about two-thirds of the forecasts were correct!

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

Trends!

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.

Bass Model Forecast of Product Diffusion Figure 11.4

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.