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Market Potential and Sales Forecasting: Methods and Uses

Explore market potential estimation methods, forecasting techniques, and practical uses for entry decisions, resource allocation, and performance evaluation. Learn how to derive potential estimates using surveys, statistical methods, and secondary sources.

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Market Potential and Sales Forecasting: Methods and Uses

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  1. Chapter 6 Market Potential And Sales Forecasting

  2. Forecasts vs. Potential

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

  4. Deriving Potential Estimates Data Secondary data Calculations Result Past sales data Model/Statistical method Potential estimate Surveys/ Primary data Judgment Secondary sources

  5. Useful Sources for Potential Estimates • Government Sources • Trade Associations • Private Companies • Financial and Industry Analysts • Popular Press • The Internet

  6. 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?

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

  8. Market Potential: Electric Coil

  9. How Are Sales 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

  10. Scenario-Based Forecasts

  11. Summary of Forecasting Methods

  12. Summary of Forecasting Methods (cont.)

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

  14. Graphical Eyeball Forecasting Sales ƍ Range • • Forecast • • • • • • • • Time

  15. 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

  16. Time-Series Forecasting Methods • Moving Averages • Exponential Smoothing • Regression Analysis

  17. Potential Customers by Industry and Size

  18. Sample Data

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

  20. Time-Series Regression Example Input Data Time Sales • 100 • 110 • 105 • 130 • 140 • 120 • 160 • 175 Prediction Ŝ Computer/ Calculator 94.3 105.2 115.0 124.9 134.8 144.7 154.6 164.4 Sales=85.4+9.88(time)

  21. Trial over Time for a New Product Number who try a new product for first time Time

  22. Model-Based Methods • Regression analysis • Leading indicators • Econometric models

  23. Forecasting Method Usage

  24. Use of New Product Forecasting Techniques by All Responding Firms

  25. 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

  26. Cereal Sales Data (Monthly)

  27. Cereal Data

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

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

  30. Format for Reporting a Regression Model Based Forecast

  31. The Impact of Uncertain Predictors on Forecasting

  32. Potential Energy Bar Customers

  33. Power Bar Data

  34. Bass Model: PDA Actual vs. Predicted

  35. Sample Format for Summarizing Forecasts

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