1. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. C H A P T E R Market Potential and Sales Forecasting 6

2. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Major Topics 1.Potential versus Forecasting 2.Estimating Market and Sales Potential 3.Sales Forecasting & Methods* 4.Forecasting Method Usage* 5.What You need: Forecast (market and your firm)

3. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Definitions of Key Terms 1.Potential Maximum sales (Saturation) attainable under a given set of conditions within a specified period of time 2.Demand Customer wants that are backed by buying power 3. Forecast Amount of sales expected to be achieved under a set of conditions within a specified period of time

4. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Potential versus Forecasts Expectations Possibilities Firm/Brand Category Sales Forecast Sales Potential Market Forecast Market Potential

5. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Marketing Expenditure Demand Market Potential - Prosperity Market Minimum Measuring Potential

6. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Market Potential 1. Hard to get it right 2. Fixed or Dynamic?* 3. Major Uses of Market Potential Estimates To make entry / exit decisions To make resource-level decisions (firm level) To make location and other resource allocation decisions (product level) To set objectives and evaluate performance As a base for sales forecasting

7. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Market Potential (Cont’d) 4. Major Drivers of Potential Relative Advantage Compatibility Risk Role of Similar Products (caveat)

8. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Estimating Market Potential 1. Determine the potential buyers or users of the product. 2. Determine how many individual customers are in the potential groups of buyers defined in step 1. 3. Estimate the potential purchasing or usage rate. 4. 2 X 3  Market potential

9. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. 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. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Area Potential Sales and Marketing Management Magazine: Buying Power Index :.2 * (percentage of the population of the area) +.3 * (percentage of the retail sales of the area) +.5 * (percentage of the disposable income)

11. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Sales Forecasting 1. How Are Forecasts Used? To answer “what if” questions To help set budgets To provide a basis for a monitoring system To aid in production planning By financial analysts to value a company 2.Four Major Variables to Consider Customer Behavior Past and Planned Product Strategies Competition Environment (ex: national economy)

12. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Sales Forecasting Methods* Judgment methods, which rely on pure opinions. Customer-based methods, which use customer data. Sales Extrapolation methods. Association/causal methods, model relating market factors to sales.

13. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. 1. Judgmental Methods Naïve extrapolation - takes most current sales and adds a judgmentally determined x%. Sales Force - ask salespeople calling on retail account to forecast sales. Executive Opinion - marketing manager opinion to predict sales based on experience.* Delphi Method - a jury of experts sent a questionnaire and estimates sales and justifies the number.

14. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. 2. Customer-based Methods Market testing - uses primary data collection methods to predict sales. Market surveys - using purchase intention questions to predict demand.

15. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. 3. Sales Extrapolation Methods Extrapolation - linearly extrapolates time series data. Moving Averages - uses averages of historical sales figures to make a forecast. Exponential Smoothing - relies on the historical sales data and is more complicated than the moving average.

16. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Time-Series Extrapolation 174.5 s = 85.4 + 9.88 (time) Time Sales

17. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Moving Average

18. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. 4. Association/Causal Methods Correlation. Regression Analysis : Time + Other Variables Leading Indicators. Econometric Models: Multiple Equations

19. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Forecasting Method Usage

20. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Example: 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

21. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Cereal Sales Data (Monthly)

22. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Cereal Data

23. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Cereal Data Correlation Matrix* The numbers in each cell are presented as: correlation, (sample size), significant level

24. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Regression Results: Cereal Data* Numbers in ( ) are standard errors

25. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Format for Reporting a Regression Model Based Forecast*

26. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. The Impact of Uncertain Predictors on Forecasting

27. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Using Forecasts in Practice Some points to remember Do sensitivity analysis Examine Big Residuals You will miss turning points Report Format

28. McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. Sample Format for Summarizing Forecasts