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Sales Management 8 Estimating Demand. Time Sales 0 Market Potential Industry Forecast Company...
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Transcript of Sales Management 8 Estimating Demand. Time Sales 0 Market Potential Industry Forecast Company...
Time
Sales
0
Market Potential
Industry Forecast
Company Potential
Company Forecast
(Industry Forecast ≤ Market Potential)
(Company Forecast ≤ Company Potential)
Key Terms
• Market Potential: All possible sales• Industry Forecast: Likely sales, all
companies• Company Potential: All possible sales
for one company• Company Sales Forecast: Likely
company salesNB: All figures are expressed for a period of TIME.
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
Why Forecast Sales?
• Allocate resources
• Control operations
• Project Cash Flow (Important!)
• Capital/operating budgets
• Production schedules & Inventory control
• Hiring; collective bargaining
• Planning marketing and sales plans
Methods of Forecasting
• Subjective– Users’ Expectations
– Sales Force Composite
– Jury of Executive Opinion• Delphi Technique
Users’ Expectations
• Also called Buyers’ Intentions method
• Focus Groups
• Surveys
• Intention ≠ Behavior, but does correlate
Sales Force Composite
• Salespeople are boundary-spanners
• Close to customers, competitors
• Fingers on the pulse of the market
• Survey sales force, and add up estimates
• Good starting point; need adjustment
Jury of Executive Opinion
• Top/key executives, 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
Methods of Forecasting
• Objective– Market Test
– Time Series Analysis• Moving Averages
• Exponential Smoothing
• Decomposition
– Statistical Demand Analysis
Test Market
• Pick “representative” city• Full marketing effort• Extrapolate results to rest of nation
• Expensive• Takes time• Tip your hand• Competitors can skew results
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)
Moving Averages
• Average last n (=2,4, whatever) years sales to predict the coming year
2005: 5,000 units2006: 8,000 units2007: 8,000 units2008: 7,000 units estimated
Decomposition
• Apply to monthly or quarterly data
• Account for:– Trends– Cycles– Seasons– Random occurrences
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.
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
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