New Product Strategy

Sales ForecastingFebruary 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

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:

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)

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 DiffusionProfits = 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 playersx 40% Percent awareness after one yearx 20% Percent of "aware" owners who will try productx 70% Percent availability at electronics retailersx 20% Percent of triers who will buy a second unitx $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

Item MarketResearch

Concept Test Product UseTest

ComponentTesting

Market Test

Market Units XX X X XAwareness X X X XTrial XX X XAvailability X XXRepeat XX XConsumption X X X XXPrice/Unit X X X X XXCost/Unit X XX

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.

“Futurists” Consumer insight Ethnographies Trend reports

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!

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 enjoymentIdealist

(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 initiatedby 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 ofProduct Diffusion

Figure 11.4

Hurdle Rates on Returns and Other Measures

Hurdle RateProduct Strategic Role or Purpose Sales Return on

InvestmentMarket Share

IncreaseA Combat competitive entry $3,000,000 10% 0 PointsB Establish foothold in new

market$2,000,000 17% 15 Points

C Capitalize on existingmarkets

$1,000,000 12% 1 Point

Explanation: the hurdles should reflect a product’s purpose,or assignment. Example: we might accept a very lowshare increase for an item that simply capitalized on ourexisting market position.