C H A P T E R

1. 6 C H A P T E R Market Potential and Sales Forecasting Market = Industry (Category) Think about the product/industry of your choice

2. Major Topics for Ch. 6 • Potential versus Forecasting • Estimating Market Potential and Sales Potential • Sales Forecasting & Methods* • Forecasting Method Usage* • What You need: Forecast for (the industry and your firm)

3. Definitions of Key Terms • Potential • Maximum sales (Saturation) attainable under a given set of conditions within a specified period of time • 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. 1. Potential versus Forecasts Present Condition Possibilities Sales Potential Sales Forecast Firm/Brand Industry (Category) Market Forecast Market Potential

5. Measuring Potential Market Potential - Prosperity Demand Market Minimum Marketing Expenditure

6. 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. Market Potential (Cont’d) 4. Major Drivers of Market Potential* • Relative Advantage • Compatibility • Risk • Role of Similar Products (caveat) • Coffee: Starbucks • Video game console: Nintendo

8. Deriving Potential Estimates

9. 2. Estimating Market Potential 1.Determine the “potential” buyers or users of the product.  customer analysis 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

10. Example:Market Potential for Electric Coil

11. Estimating Area Potential (for Retailing) 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)

12. 1. Potential versus Forecasts Present Condition Possibilities Sales Potential Sales Forecast Firm/Brand Industry (Category) Market Forecast Market Potential

13. 3. Sales Forecasting 1. How Are Forecasts Being 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 • Four Major Variables to Consider* • Customer Behavior • Past and Planned Product Strategies • Competition • Environment (ex: national economic condition)

14. Four Sales Forecasting Methods** 1. Judgment methods, which rely on pure opinions. 2. Customer-based methods, which use customer data. 3. Sales Extrapolation methods. 4. Association/causal methods, model relating market factors to sales.

15. 1. Four 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.

16. 2. Two Customer-based Methods • Market testing - uses primary data collection methods to predict sales. • Market surveys - using purchase intention questions to predict demand. (especially for B2B products)

17. 3. Three 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.

18. Sales s = 85.4 + 9.88 (time) • 174.5 • • • • • • • • • • • • • • • • Time Time-Series Extrapolation

19. Moving Average

20. 4. Four Association/Causal Methods • Correlation. Ex) Soft drink • Regression Analysis* : Time + Other Relevant Explanatory Variables • Leading Indicators. • Econometric Models: Multiple Equations

21. Forecasting Method Usage

22. An Example of Forecasting:Developing Regression Models for Forecasting • 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 • Develop and Run the regression • Determine the significant predictors

23. Cereal Sales Data (Monthly)

24. Cereal Data

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

26. Regression Results: Cereal Data* Numbers in ( ) are standard errors

27. Format for Reporting a Regression Model Based Forecast*

28. The Impact of Uncertain Predictors on Forecasting

29. Using Forecasts in Practice • Some points to remember • Do sensitivity analysis • Examine Big Residuals* • You will likely miss turning points • Report Format

30. Sample Format for Summarizing Forecasts

31. What You Need for the Term Project Get Forecast for • industry sales • your firm sales

32. Four Sales Forecasting Methods** 1. Judgment methods, which rely on pure opinions. 2. Customer-based methods, which use customer data. 3. Sales Extrapolation methods. 4. Association/causal methods, model relating market factors to sales.