New Products Management- CHAPTER 11 SALES FORECASTING AND FINANCIAL ANALYSIS doc

CHAPTER 11SALES FORECASTING AND FINANCIAL ANALYSIS... Why Financial Analysis for New Products is Difficult  Target users don’t know.. Commonly Used Forecasting Techniques Simple Regres

CHAPTER 11

SALES FORECASTING AND FINANCIAL ANALYSIS

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.

Forecasters Are Often Right

 In 1967 they said we would have:

managerial decision making by 1987.

 Expenditures for recreation and entertainment doubled by 1986.

Figure 11.1

Forecasters Can Be Very Wrong

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

Techniques

Simple Regression Short Low Easy to learn

Multiple Regression Short-medium Moderate More difficult to

learn and interpret Econometric

Analysis

Short-medium Moderate to high Complex

Simple time series Short Very low Easy to learn

Advanced time

series (e.g.,

smoothing)

Short-medium Low to high,

depending on method

Can be difficult to learn but results are easy to interpret Jury of executive

opinion Medium Low Interpret with caution

Scenario writing Medium-long Moderately high Can be complex

Delphi probe Long Moderately high Difficult to learn

and interpret

Figure 11.2

Handling Problems in Financial Analysis

 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

Intentions

appropriately adjusted or calibrated.

 Definitely buy = 5%

 Probably buy = 36%

 80% of “definitelies” actually buy

 33% of “probablies” actually buy

16%.

Forecasting Sales Using Purchase Intentions (continued)

and availability.

awareness and availability.

and has it available, market share is

recalculated to (0.6) (16%) = 9.6%.

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

0.603

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

A-T-A-R Model Results: Bar Chart

Format

Figure 11.3

Bass Model Forecast of

Product Diffusion

Figure 11.4

The Life Cycle of Assessment

Figure 11.5

Calculating New Product’s Required

Rate of Return

Risk

% Return

Reqd Rate

of Return

Cost of

Capital

Avg Risk

of Firm

Risk on Proposed Product

Figure 11.6

Hurdle Rates on Returns and Other

Measures

Figure 11.8

Hurdle Rate

Product Strategic Role or Purpose Sales Return on

Investment

Market Share Increase

A Combat competitive entry $3,000,000 10% 0 Points

B Establish foothold in new

market $2,000,000 17% 15 Points

C Capitalize on existing

markets $1,000,000 12% 1 Point

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.

Hoechst-U.S Scoring Model

Key Factors Rating Scale (from 1 - 10)

1 ……… 4 ……… 7 ……… 10 Probability of Technical

Success

<20% probability >90% probability

Probability of Commercial

Success

<25% probability >90% probability

Reward Small Payback < 3 years

Business-Strategy Fit R&D independent of R&D strongly supports

business strategy business strategy Strategic Leverage "One-of-a-kind"/ Many proprietary

dead end opportunities

Source: Adapted from Robert G Cooper, Scott J Edgett, and Elko J Kleinschmidt Portfolio Management

for New Products, McMaster University, Hamilton, Ontario, Canada, 1997, pp 24-28.

Figure 11.9

Specialty Minerals Scoring Model

Manufacturing Firm Scoring Model

(disguised)

aligns with business strategy)

Note: how in each of these examples, the model contains financial as well as strategic criteria.