1. Sales Management 8 Estimating Demand

2. Time Sales 0 Market Potential Industry Forecast Company Potential Company Forecast (Industry Forecast ≤ Market Potential) (Company Forecast ≤ Company Potential)

3. Key Terms Market Potential: All possible ______ Industry Forecast: Likely _________, all companies Company Potential: All possible _________for one company Company Sales Forecast: Likely company _________ NB: All figures are expressed for a period of ___.

4. So how do you estimate demand? Determine WHO will use the product. –Which segment(s)?; Size? Determine their RATE of use. –New/Replacement; Frequency. WHO buys product? –Purchasing agent, parent, lover, uncle WHAT is their motivation to purchase? –Life event, new business, new market, fun

5. Why Forecast Sales? Allocate _________ Control _________ Project Cash Flow (Important!) Capital/operating budgets Production schedules & _________control Hiring; collective _________ Planning marketing and sales plans

6. Methods of Forecasting Subjective –Users’ Expectations –Sales Force Composite –Jury of Executive Opinion Delphi Technique

7. Users’ Expectations Also called Buyers’ _________method __________________ _________ Intention ≠ Behavior, but does correlate

8. Sales Force Composite Salespeople are _________ Close to customers, competitors Fingers on the pulse of the market Survey sales force, and add up estimates Good starting point; need adjustment

9. Jury of Executive Opinion Top/key _________, perhaps outside consultants, give best estimate Not boundary spanners, but see “Big Picture” May need discussion to reach an estimate that everyone can agree on

10. Delphi Technique Similar to Executive Opinion _________Process _________

11. Methods of Forecasting Objective –Market Test –Time Series Analysis Moving Averages Exponential Smoothing Decomposition –Statistical Demand Analysis

12. Test Market Pick “_________” city Full marketing effort _________results to rest of nation Disadvantages: _________

13. Time Series Analysis Use historical (not hysterical) data to predict future Like driving by looking in the rear-view mirror Estimate starts in “ballpark” (not the franks)

14. Moving Averages Average last n (=2,4, whatever) years sales to predict the coming year 2000: 5,000 units 2001: 8,000 units 2002: 6,500 units estimated

15. Decomposition Apply to monthly or quarterly data Account for: –_________

16. Statistical Demand Analysis Use regression or other techniques to determine relationship between sales and predictor factors. Need good data and analytical skills. Example: –Home heating oil demand = Function of temperature, sun, last fill, tank size, & history.

17. What do companies use? They tend to rely more heavily on qualitative than quantitative. Especially sales force composite and jury of executive opinion. Easier, quicker, perhaps accurate enough

18. Sales Territories Design territories –Need a market index to compare Industrial Goods –Standard Industrial Classification –North American Industrial Classification System Consumer goods –Buying Power Index = (5I + 2P + 3R)/10 % disposable personal Income % US Population % total Retail sales