Chapter 6: Market Potential and Sales Forecasting

1. Advanced Marketing BiMBA 2006 Chapter 6: Market Potential and Sales Forecasting Estimating the top line

2. Advanced Marketing BiMBA 2006 Why estimate market potential? Entry/exit decisions Resource allocations Location decisions Set sales objectives & evaluate performance Set forecast (% of potential)

3. Advanced Marketing BiMBA 2006 Estimating potential for new product Relative advantage over current product Compatibility with current system / norms Risk (monetary, social and psychological) Rate of adoption of comparable products

4. Advanced Marketing BiMBA 2006 Estimating potential for mature product Past experience Recent trends Competition Customers Environment

5. Advanced Marketing BiMBA 2006 Information sources Secondary data Past sales data Primary data

6. Advanced Marketing BiMBA 2006 Methods of estimating potential Potential for Fordham University Population of New York City: 8 million 4% between 18-22 = 320,000 60% high school graduates = 192,000 40% have income > $50,000 = 96,000

7. Advanced Marketing BiMBA 2006 Forecasting: specific product & target Why forecast sales? Compare proposed changes to current results Help set budgets Provide basis for monitoring results Aid in production planning

8. Advanced Marketing BiMBA 2006 Considerations in forecasting Customer behavior (past & future) Competitors� behavior (past & future) Environmental trends Product strategies

9. Advanced Marketing BiMBA 2006 Range of forecasted results Each combination provides one scenario Each scenario has range of possible results Limit to three expected, better and worse than expected

10. Advanced Marketing BiMBA 2006 Methods of forecasting Judgment based Sales extrapolation Customer based Model based

11. Advanced Marketing BiMBA 2006 Judgment-based forecasting: qualitative Jury of expert opinion (most common) Delphi method Na�ve extrapolation / opinion (2nd most common) Sales force composite (3rd most common)

12. Advanced Marketing BiMBA 2006 Sales extrapolation: quantitative Assumes future will follow on past Appropriate for mature, static industry Moving average (most common quantitative method) Average of three period sales over time Average of change in three period sales over time Exponential smoothing Alternative method to smooth data Regression analysis (next most common in U.S.) Forecast sales = a intercept + b slope (time)

13. Advanced Marketing BiMBA 2006 Customer-based forecasting methods Does not assume future will follow on past Appropriate for dynamic markets / new products Market testing Market surveys Can be fed into forecasting model

14. Advanced Marketing BiMBA 2006 Model-based forecasting methods Regression with other factors Sales = a intercept + b (advertising) + c (price) Develop model on half of past data Test model on other half of data

15. Advanced Marketing BiMBA 2006 Forecasting products with new features Show basic product Ask what they would pay This price may be arbitrary Add feature: e.g., a videogame expansion card Ask what they would pay Follow-up prices are coherent Add another feature: e.g., a �Friendstar� device Ask what they would pay Add another feature: e.g., a hard drive Ask what they would pay Add another feature: e.g., a Microsoft office Ask what they would pay

16. Advanced Marketing BiMBA 2006 Forecasting new-to-market products Diffusion model: Bass (1969)

17. Advanced Marketing BiMBA 2006 Forecasting new-to-market products:Diffusion model: Bass (1969)

18. Advanced Marketing BiMBA 2006 Conclusions Forecasting is necessary, but difficult All methods have plusses and minuses All are based on prior experience Will generally miss the turning points Best to come up with different scenarios Have expected, best and worst forecasts for each Be prepared!