Sales Management 8

Estimating Demand

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

Group Think

Delphi Technique

• Similar to Executive Opinion

• Iterative/Secret Process

• Convergence

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