NEW PRODUCT MANAGEMENT

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.

*

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 McBride, “Until Recently Full Of Promise, Satellite Radio Runs Into Static,” Wall Street Journal, August 15, 2006, pp. A1-A9.

*

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.

Figure 11.1

*

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!
Figure 11.1
(cont’d.)

*

Commonly Used Forecasting Techniques

Figure 11.2

*

Technique

Time Horizon

Cost

Comments

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

New Product Forecasting Strategies
Source: Adapted from K. B. Kahn, “Forecasting New Products,” in K. B. Kahn, S. E. Kay, R. J. Slotegraaf, and S. Uban (Eds.), The PDMA Handbook of New Product Development (Hoboken, NJ: John Wiley), 2013, Ch. 16, p. 276.
Figure 11.3

  Current Product Technology New Product Technology

Current Market Type of innovation: cost reductions and process improvements
 
Type of forecasting: sales analysis Type of innovation: line extension
 
 
Type of forecasting: product line analysis, life cycle analysis

New Market Type of innovation: new market or new product uses
 
Type of forecasting: customer analysis, market analysis Type of innovation: new-to-the-world or new-to-the-firm
Type of forecasting: scenario or “what-if” analysis

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!

*

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.

*

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%.

*

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%.

*

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.

*

A-T-A-R Model Results:
Bar Chart Format
Figure 11.4

*

Bass Model Forecast of

Product Diffusion
Figure 11.5

*

The Life Cycle of Assessment
Figure 11.6

*

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 11.7

*

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 low-demand 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%.

Figure 11.8

*

Real-Options Analysis (continued)
Cash flow in Year 1 for each demand scenario:
Cash flow in Year 1 if option taken to abandon project and equipment is sold:
Therefore the project would be abandoned after Year 1.
Figure 11.8

Demand Year 1 Year 2 Year 3 Year 4 Total

High 40,000 40,000/(1.12)
=35,714 40,000/(1.12)2 = 31,888 40,000/(1.12)3 = 28,471 $136,073

Low 10,000 10,000/(1.12)
= 8,929 10,000/(1.12)2 = 7,972 10,000/(1.12)3 = 7,118 $34,018

Demand Year 1 Take Option to Abandon and Sell Equipment Total

Low 10,000 38,000 $48,000

*

Real-Options Analysis (continued)
Now assess NPV for each demand scenario, assuming project is abandoned after Year 1 if demand is low.
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.
Figure 11.8

Demand Year 0 Year 1 Year 2 Year 3 Year 4 Total

High -70,000 40,000/(1.12)
=35,714 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 48,000/(1.12)
= 42,857 -$27,143

*

Hurdle Rates on Returns and Other Measures
Figure 11.9
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.

*

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

Hoechst-U.S. Scoring Model
Figure 11.9

*

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 -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. Specialty Minerals Scoring Model Management interest Customer interest Sustainability of competitive advantage Technical feasibility case strength Fit with core competencies Profitability and impact * 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. * 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. Figure 11.11 * Technique Time Horizon Cost Comments 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 0.9 0.603 0.0965 0.0614 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% AwareAvailableTrialRepeat 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 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 -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.