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C H A P T E R

6. C H A P T E R. Market Potential and Sales Forecasting. Major Topics. Potential versus Forecasting Estimating Market and Sales Potential Sales Forecasting & Methods* Forecasting Method Usage* What You need: Forecast (market and your firm). Definitions of Key Terms. Potential

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C H A P T E R

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  1. 6 C H A P T E R Market Potential and Sales Forecasting

  2. Major Topics • Potential versus Forecasting • Estimating Market and Sales Potential • Sales Forecasting & Methods* • Forecasting Method Usage* • What You need: Forecast (market 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. Potential versus Forecasts Expectations Possibilities Sales Potential Sales Forecast Firm/Brand Market Forecast Market Potential Category

  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 Potential • Relative Advantage • Compatibility • Risk • Role of Similar Products (caveat)

  8. 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. Market Potential: Electric Coil

  10. 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. 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 • Four Major Variables to Consider • Customer Behavior • Past and Planned Product Strategies • Competition • Environment (ex: national economy)

  12. 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. 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. 2. Customer-based Methods • Market testing - uses primary data collection methods to predict sales. • Market surveys - using purchase intention questions to predict demand.

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

  17. Moving Average

  18. 4. Association/Causal Methods • Correlation. • Regression Analysis : Time + Other Variables • Leading Indicators. • Econometric Models: Multiple Equations

  19. Forecasting Method Usage

  20. 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. Cereal Sales Data (Monthly)

  22. Cereal Data

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

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

  25. Format for Reporting a Regression Model Based Forecast*

  26. The Impact of Uncertain Predictors on Forecasting

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

  28. Sample Format for Summarizing Forecasts

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