1. 6 C H A P T E R Market Potential and Sales Forecasting
Market = Industry (Category)
Think about the product/industry of your choice

2. Major Topics for Ch. 6 Potential versus Forecasting
Estimating Market Potential and Sales Potential
Sales Forecasting & Methods*
Forecasting Method Usage*
What You need: Forecast for (the industry and your firm)

3. Definitions of Key Terms
Potential
Maximum sales (Saturation) attainable under a given set of conditions within a specified period of time
Demand
Customer wants that are backed by buying power
3. Forecast
Amount of sales expected to be achieved under a set of conditions within a specified period of time

4. 1. Potential versus Forecasts
Present Condition
Possibilities
Sales Potential
Firm/Brand
Sales Forecast
Industry
(Category)
Market Forecast
Market Potential

5. Measuring Potential Market Potential - Prosperity Demand
Market Minimum
Marketing Expenditure

6. Market Potential 2. Fixed or Dynamic?*
1. Hard to get it right
2. Fixed or Dynamic?*
3. Major Uses of Market Potential Estimates
To make entry / exit decisions
To make resource-level decisions (firm level)
To make location and other resource allocation decisions (product level)
To set objectives and evaluate performance
As a base for sales forecasting

7. Market Potential (Cont’d)
4. Major Drivers of Market Potential*
Relative Advantage
Compatibility
Risk
Role of Similar Products (caveat)
Coffee: Starbucks
Video game console: Nintendo

8. Deriving Potential Estimates

9. 2. Estimating Market Potential
1. Determine the “potential” buyers or users of the product.  customer analysis
2. Determine how many individual customers are in the potential groups of buyers defined in step 1.
3. Estimate the potential purchasing or usage rate.
4. 2 X 3  Market potential

10. Example: Market Potential for Electric Coil
SIC
Industry
Purchases of Product
Number of Workers
Average Purchase/Worker
National Number of Workers
Estimated Potential
3611
Electrical Measuring
$160
3,200
$.05
34,913
$1,746
3612
Power Transformers
5,015
4,616
1.09
42,587
46,249
3621
Motors and Generators
2,840
10,896
.26
119,330
30,145
3622
Electrical Industry Controls
4,010
4,678
.86
46,805
40,112
$12, $119,252

11. Estimating Area Potential (for Retailing)
Sales and Marketing Management Magazine:
Buying Power Index
: .2 * (percentage of the population of the area)
+ .3 * (percentage of the retail sales of the area)
+ .5 * (percentage of the disposable income)

12. 1. Potential versus Forecasts
Present Condition
Possibilities
Sales Potential
Firm/Brand
Sales Forecast
Industry
(Category)
Market Forecast
Market Potential

13. 3. Sales Forecasting 1. How Are Forecasts Being Used?
To answer “what if” questions
To help set budgets
To provide a basis for a monitoring system
To aid in production planning
By financial analysts to value a company
Four Major Variables to Consider*
Customer Behavior
Past and Planned Product Strategies
Competition
Environment (ex: national economic condition)

14. Four Sales Forecasting Methods**
1. Judgment methods, which rely on pure opinions.
2. Customer-based methods, which use customer data.
3. Sales Extrapolation methods.
4. Association/causal methods, model relating market factors to sales.

15. 1. Four Judgmental Methods
Naïve extrapolation - takes most current sales and adds a judgmentally determined x%.
Sales Force - ask salespeople calling on retail account to forecast sales.
Executive Opinion - marketing manager opinion to predict sales based on experience.*
Delphi Method - a jury of experts sent a questionnaire and estimates sales and justifies the number.

16. 2. Two Customer-based Methods
Market testing - uses primary data collection methods to predict sales.
Market surveys - using purchase intention questions to predict demand.
(especially for B2B products)
Market testing: focus group and in-depth interviews

17. 3. Three Sales Extrapolation Methods
Extrapolation - linearly extrapolates time series data*.
Moving Averages - uses averages of historical sales figures to make a forecast.*
Exponential Smoothing - relies on the historical sales data and is more complicated than the moving average.

18. Time-Series Extrapolation•
• • • •
174.5
s = (time)
Time
Sales

19. Moving Average

20. 4. Four Association/Causal Methods
Correlation. Ex) Soft drink
Regression Analysis* : Time + Other Relevant Explanatory Variables
Leading Indicators.
Econometric Models: Multiple Equations

21. Forecasting Method Usage

22. An Example of Forecasting: Developing Regression Models for Forecasting
Plot Sales Over Time
Consider the Variables that Are Relevant to Predicting Sales
Collect Data
Analyze the Data
Examine the correlations among the independent variables
Develop and Run the regression
Determine the significant predictors

23. Cereal Sales Data (Monthly)

24. Cereal Data

25. Cereal Data Correlation Matrix*
The numbers in each cell are presented as: correlation, (sample size), significant level

26. Regression Results: Cereal Data*
Numbers in ( ) are standard errors

27. Format for Reporting a Regression Model Based Forecast*

28. The Impact of Uncertain Predictors on Forecasting

29. Using Forecasts in Practice
Some points to remember
Do sensitivity analysis
Examine Big Residuals*
You will likely miss turning points
Report Format

30. Sample Format for Summarizing Forecasts

31. What You Need for the Term Project
Get Forecast for
industry sales
your firm sales

32. Four Sales Forecasting Methods**
1. Judgment methods, which rely on pure opinions.
2. Customer-based methods, which use customer data.
3. Sales Extrapolation methods.
4. Association/causal methods, model relating market factors to sales.