Estimating Potentials and Forecasting Sales Part I THE BIG PICTURE Sales Management Resources: Estimating Potentials and Forecasting Sales

WHY FORECAST? One of the keys to success in sales is knowing where customers are located and being able to predict how much they will buy. Firms have found that sales potential data are indispensable to developing a sales program, particularly in setting up territories, assigning quotas, developing budgets, and comparing sales performance of individual salespeople. Sales forecasting is so important that more than 50 percent of firms include this topic in their sales manager training programs. Inaccurate demand predictions can have disastrous effects on profitability.

QUALITATIVE SALES FORECASTING Sales forecasting is concerned with predicting future levels of demand. These projections are vital for budgeting and planning purposes. For new products, a few simple routines can be employed. The absence of past sales means that you have to be more creative in coming up with predictions of the future. Sales forecasts for new products are often based on executive judgments, sales force projections, surveys, and market tests. We will begin our discussion of forecasting techniques by focusing on subjective methods that are based on interpretations of business conditions by executives and salespeople.

Sales Force Composite A favorite forecasting technique for new and existing products is the sales force composite method. With this procedure, salespeople project volume for customers in their own territory, and the estimates are aggregated and reviewed at higher management levels. The territory estimate is often derived based on demand estimates for each of the largest customers in the territory, the remainder of the customers as a group, and then for new prospects.

Jury of Executive Opinion This technique involves soliciting the judgment of a group of experienced managers to give sales estimates for proposed and current products. The main advantages of this method are that it is fast and it allows the inclusion of many subjective factors such as competition, economic climate, weather, and union activity.

Leading Indicators Where sales are influenced by basic changes in the economy, leading indicators can be a useful guide in preparing sales forecasts. The idea is to find a factor series that is closely related to company sales, yet for which statistics are available several months in advance. Changes in the factor can then be used to predict sales directly, or the factor can be combined with other variables in a forecasting model. Some of the more useful leading indicators include prices of common stocks, new orders for durable goods, new building permits . Leading indicators are sensitive to changes in the business environment and they often signal turns in the economy months before they actually occur.

When Should Qualitative Forecasting Methods Be Used? Qualitative methods are often used when you have little numerical data to incorporate into your forecasts. New products are a classic example of limited information, and qualitative methods are frequently employed to predict sales revenues for these items. Qualitative methods are also recommended for those situations where managers or the sales force are particularly adept at predicting sales revenues. In addition, qualitative forecasting methods are often utilized when markets have been disrupted by strikes, wars, natural disasters, recessions, or inflation.

When Should Qualitative Forecasting Methods Be Used? Under these conditions, historical data are useless, and judgmental procedures that account for the factors causing market shocks are usually more accurate. Managers should calculate and record the forecasting errors produced by the qualitative techniques they employ so that they will know when these methods are best employed.

Naive Forecasts Time series forecasts rely on past data to provide a basis for making projections about the future. The naive forecast is the simplest numerical forecasting technique and is often used as a standard for comparison with other procedures. This method assumes that nothing is going to change and that the best estimate for the future is the current level of sales. For example, actual sales of 49 units observed in quarter 1 in Table SMR2-4 can be used to predict sales in quarter 2. Naive forecasts for the last three quarters of year 1 would be

Naive Forecasts

Moving Averages With the moving average method, the average revenue achieved in several recent periods is used as a prediction of sales in the next period. The formula takes the form :

MOVING AVERAGE FORECAST CALCULATING A MOVING AVERAGE FORECAST   where Ft+1 = forecast for the next period St = sales in the current period n = number of periods in the moving average

MOVING AVERAGE FORECASTING EXAMPLE 1 2 3 4 Actual sales 49 77 90 79 Quarter 1 2 3 4 Actual sales 49 77 90 79 Two-period moving average 63 83.5

FORECASTING WITH MOVING AVERAGES 1 2 3 4 5 6 Actual sales 49 77 90 79 57 98 Seasonally adjusted sales 67 68 78 81 78 87 Two-period moving average forecast seasonally corrected 78.3 70.1 58.0 89.8 Three-period moving average forecast seasonally corrected 68.9 55.2 89.3 Two-period moving average forecast Three-period moving average forecast F3 = ( S1 + S2 ) x I3 F4 = ( S1 + S2 + S3 ) x I4 2 3 = ( 67 + 68 ) x 1.16 = ( 67 + 68 + 78 ) x 0.97 2 3 = 78.3 = 68.9 Time Periods

Exponential Smoothing An important feature of exponential smoothing is its ability to emphasize recent information and systematically discount old information. A simple exponentially smoothed forecast can be derived using the formula:

CALCULATING AN EXPONENTIAL SMOOTHING FORECAST   where = smoothed sales forecast for period t and the forecast for period t + 1 α = the smoothing constant St = actual sales in period t -1 = smoothed forecast for period t – 1

EXPONENTIAL SMOOTHING FORECASTING EXAMPLE Quarter 1 2 3 4 Actual sales 49 77 90 79 Smoothed forecast 60.2 72.1

When Should Quantitative Forecasting Methods Be Used? Quantitative forecasting techniques are best employed when you have access to historical data. It is also helpful if the time series you are trying to forecast are stable and do not frequently change direction. Quantitative methods have distinct advantages in situations where you must make frequent forecasts for hundreds or thousands of products. Because of the large number of calculations required by quantitative forecasting procedures, analysts need access to computers and appropriate forecasting software..