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

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)

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

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

Measuring Potential Market Potential - Prosperity Demand Market Minimum Marketing Expenditure

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

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

Deriving Potential Estimates

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

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 $12,025 $119,252

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)

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

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)

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.

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.

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

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.

Time-Series Extrapolation • • • • • 174.5 s = 85.4 + 9.88 (time) Time Sales

Moving Average

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

Forecasting Method Usage

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

Cereal Sales Data (Monthly)

Cereal Data

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

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

Format for Reporting a Regression Model Based Forecast*

The Impact of Uncertain Predictors on Forecasting

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

Sample Format for Summarizing Forecasts

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

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.