# CHAPTER 7 Estimating Potentials and Forecasting Sales FORECAST

• Slides: 29

CHAPTER 7 Estimating Potentials and Forecasting Sales

FORECAST v. A forecast is a prediction of a future state

IMPORTANCE OF FORECASTS v Determining sales force size v Designing territories v Establishing quotas and budgets v Determining compensation v Evaluating performance

MARKET POTENTIAL v Best possible level of industry sales for a product in a specific market over a stated time period

BUYING POWER INDEX (BPI) v. A composite measure of regional buying power v Composed of area’s percentage of – US disposable income – US retail sales – US population

SALES POTENTIAL v The best possible share of the market potential that a firm can achieve

SALES FORECAST v Level make of sales that a firm expects to

FORECASTING METHODS

TOP-DOWN FORECASTING v Use annual Survey of Buying Power (Sales and Marketing Management) v Example- To get state-by-state sales forecast – 6% of U. S. retail sales come from New York – Shoe mfg. takes U. S. sales forecast and multiplies by. 06 for New York state forecast

DETERMINING MARKET AND SALES FORECASTS v v v v v Market factor methods Regression analysis Surveys of buyer intentions Test markets Executive opinion Delphi Technique Sales Force Composite Projection of Trends Capacity Based Forecasts

MARKET FACTOR METHOD v Market factors are elements that cause demand for a product or are related to the demand of the good

MARKET FACTOR FORECASTING--EXAMPLE A manufacturer of baby playpens estimated that the firm sold 16 playpens for every 1000 births. Using births as a market factor, compute a sales forecast v Estimated births, 1999: 4, 000 v Rate of sales: 16 per 1, 000 v Sales forecast: 64, 000

MARKET FACTOR-ADVANTAGES v High validity v Simplicity v Inexpensive

REGRESSION ANALYSIS v We predict how one variables (sales) is affected by change in other variables (advertising expenditures, number of sales calls, etc. ) v Territory Sales = a + b 1 (factor) + b 2 (factor) + b 3 (factor) v a = constant; b’s = regression coefficients

REGRESSION ANALYSIS v Use of multiple factors provides a high degree of reliability v Many do not understand the concept

SURVEYS OF BUYERS INTENTIONS v Contact customers and question them v High cost v Time consuming v Socially acceptable answers

TEST MARKETS v Accurate v Considerable time and effort

EXECUTIVE OPINION v Simple v Quick v Unscientific v Managers market somewhat removed from

DELPHI TECHNIQUE v Consensus approach

SALES FORCE COMPOSITE v Ask the sales reps v Sales people are poor forecasters

PROJECTION OF TRENDS v Use sales data for the past 10 years v Exponential smoothing

PROJECTION OF TRENDS v Trend Component – General trend due to long-term factors (e. g. , demographic population shifts, lifestyle changes, technological advances etc. ) v Cyclical Component – Patterns lasting more than a year due to cyclical changes in the economy (e. g. , recession, inflation)

PROJECTION OF TRENDS v Seasonal Component – Trends within one-year period from seasonal change (e. g. , lower swimming pool sales in fall and winter) v Irregular Component (Residual) – Random deviations due to unanticipated, nonrecurring factors

CAPACITY BASED FORECASTS v Production limitation capacity becomes

GUIDELINES FORECASTING v Minimize the number of market factors v Use logic v Use more than one method v Recognize limitations v Use max/min analysis v Understand mathematics and statistics

SALES QUOTAS va performance goal assigned to a marketing unit for a specific period of time v is related to the sales forecast

SALES QUOTAS-PURPOSES v Furnish goals and incentives v Control activities of sales personnel v Evaluate productivity v Compensation v Control selling expenses v Evaluate sales contest results

SALES QUOTAS--TYPES v Sales volume v Gross margin or net profit v Expense v Activity v Combination

FROM THE TEXT. . . v Read all of Chapter 7 except: – Chain Ratio Method (page 325) – SIC Method (pages 327 - 328) – Scrappage Method (pages 328 - 329) – Naïve approach / MAPE (pages 335 -336)