1. 7-1

2. McGraw-Hill/Irwin Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. 7 Market Potential And Sales Forecasting

3. 7-3 Forecasts vs. Potential Expectations Possibilities Firm/BrandSales ForecastSales Potential CategoryMarket ForecastMarket Potential

4. 7-4 Major Uses of Potential Estimates 1.To make entry / exit decisions 2.To make resource level decisions 3.To make location and other resource allocation decisions 4.To set objectives and evaluate performance 5.As an input to forecasts

5. 7-5 Deriving Potential Estimates Potential estimate Past sales data Secondary data Surveys/ Primary data Model/Statistical method Judgment Secondary sources Data CalculationsResult

6. 7-6 Useful Sources for Potential Estimates Government Sources Trade Associations Private Companies Financial and Industry Analysts Popular Press The Internet

7. 7-7 New or Growing Product Potential Relative Advantage Is the new product superior in key benefits? To what degree? Compatibility What level of change is required to understand and use a new product? For customers? Intermediaries? The company? Risk How great is the risk involved? What is the probability someone will buy a new product?

8. 7-8 Methods of Estimating Market and Sales Potential Analysis-Based Estimates 1.Determine the potential buyers or users of the product 2.Determine how many are in each potential group of buyers defined by step 1 3.Estimate the purchasing or usage rate

9. 7-9 Market Potential: Electric Coil SICIndustryPurchases of Product Number of Workers Average Purchase /Worker National Number of Workers Estimated Potential 3611Electrical Measuring $1603,200$.0534,913$1,746 3612Power Transformers 5,0154,6161.0942,58746,249 3621Motors and Generators 2,84010,896.26119,33030,145 3622Electrical Industry Controls 4,0104,678.8646,80540,112 $12,025 $119,252

10. 7-10 How Are Sales Forecasts Used? 1.To answer “what if” questions 2.To help set budgets 3.To provide a basis for a monitoring system 4.To aid in production planning 5.By financial analysts to value a company

11. 7-11 Judgment-based Forecasting Methods Naïve extrapolation Sales force composite Jury of expert opinion Delphi method

12. 7-12 Customer-Based Forecasting Methods Market testing Situations in which potential customers are asked to respond to a product concept Mall Intercept Surveys Focus Groups Market surveys A form of primary market research in which potential customers are asked to give some indication of their likelihood of purchasing a product

13. 7-13 Time-Series Forecasting Methods Moving Averages Exponential Smoothing Regression Analysis

14. 7-14 Time-Series Extrapolation 1 12 174.5 s = 85.4 + 9.88 (time) Time Sales

15. 7-15 Model-Based Methods Regression analysis Leading indicators Econometric models

16. 7-16 Forecasting Method Usage

17. 7-17 Use of New Product Forecasting Techniques by All Responding Firms

18. 7-18 Developing Regression Models Plot Sales Over Time Consider the Variables that Are Relevant to Predicting Sales Collect Data Analyze the Data Examine the correlations among the independent variables Run the regression Determine the significant predictors

19. 7-19 Cereal Sales Data (Monthly)

20. 7-20 Cereal Data

21. 7-21 Cereal Data Correlation Matrix* The numbers in each cell are presented as: correlation, (sample size), significant level

22. 7-22 Regression Results: Cereal Data* Numbers in ( ) are standard errors

23. 7-23 Format for Reporting a Regression Model Based Forecast

24. 7-24 The Impact of Uncertain Predictors on Forecasting

25. 7-25 Potential Energy Bar Customers

26. 7-26 Power Bar Data

27. 7-27 Bass Model: PDA Actual vs. Predicted

28. 7-28 Sample Format for Summarizing Forecasts

29. 7-29 Scenario-Based Forecasts

30. 7-30 Summary of Forecasting Methods

31. 7-31 Graphical Eyeball Forecasting Time Sales Range ƍ Forecast

32. 7-32 Potential Customers by Industry and Size

33. 7-33 Sample Data

34. 7-34 Time-Series Regression Example 1 100 2 110 3 105 4 130 5 140 6 120 7 160 8 175 Time Sales Input Data Computer/ Calculator Sales=85.4+9.88 (time) Prediction 94.3 105.2 115.0 124.9 134.8 144.7 154.6 164.4 Ŝ

35. 7-35 Trial over Time for a New Product Time Number who try a new product for first time

36. 7-36 Summary of Forecasting Methods (cont.)