1 / 9

FORECASTING MODELS AND METHODS

FORECASTING MODELS AND METHODS. Adapted from lecture notes by Dr. Michael Geurts. New Products. 1. Buyer intention surveys: Definitely will buy Highly possible As likely as unlikely to buy highly doubtful to buy Definitely will not buy 2. Test markets. New Products.

alton
Download Presentation

FORECASTING MODELS AND METHODS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. FORECASTING MODELS AND METHODS Adapted from lecture notes by Dr. Michael Geurts

  2. New Products • 1. Buyer intention surveys: • Definitely will buy • Highly possible • As likely as unlikely to buy • highly doubtful to buy • Definitely will not buy • 2. Test markets

  3. New Products • 3. Flow through or economic study • 4. Diffusion models = rate of adoption imitators/innovators (Bass, Gopertz) • 5. Product comparable

  4. New Products- con’t • 6. Judgmental models = expert guess, Delphi • 7. New product models like dumps: • Durability • Number of potential Users • Number of Major competitors • Number of Potential customers • Proportion made aware [ MARKET Share ]

  5. New Products- con’t • 8. Conjoint analysis = determine value of product attributes an estimate market share. • 9. Trend/fashion forecasts = Innovators

  6. Existing Product Sales • 10. Business activity = capacity being used • 11. Time series = past data patterns • a. exponential smoothing • Apply exponential decreasing level of observations further in the past • b. Box-Jenkins • ARMA – stationary, but dependence on past • ARIMA – not stationary, need to remove trend/seasonality

  7. Existing Product Sales • 12. Response models = Sales and marketing mix variables (Logit models when dependent = 0 to 1) • 13. Econometric models = Using economic indicators • 14. Salesmen composite = Summation of salesman forecast for her territory • 15. Logistic regression = combination of marketing mix variables and time series forecasts.

  8. Other Forecasting Methods • 15. Technological • 16. Combining forecasts • 17. Partitioned data • 18. Regression • 19. Interest rates • 20. Economic Growth

  9. Role of Data • Bad • Past • Other forecasts • Uses of Forecasts • Budgets • Production quantities • Inventory control • Planning • Bank loans • Identify effect of problems or promotions. If the forecasting has been accurate and the company runs a promotion. The company can measure the effects of the promotion as the difference between forecast and actual.

More Related