BUS203: Principles of Marketing

Forecasts, at their basic level, are simply someone's guess as to what will happen. Each estimate, though, is the product of a process. Several such processes are available to marketing executives, and the final forecast is likely to be a blend of results from more than one process. These processes are judgment techniques and surveys, time series techniques, spending correlates and other models, and market tests.

Judgment and Survey Techniques

At some level, every forecast is ultimately someone's judgment. Some techniques, though, rely more on people's opinions or estimates and are called judgment techniques. Judgment techniques can include customer (or channel member or supplier) surveys, executive or expert opinions, surveys of customers' (or channel members') intentions or estimates, and estimates by salespeople.

Customer and Channel Surveys

In some markets, particularly in business-to-business markets, research companies ask customers how much they plan to spend in the coming year on certain products. Have you ever filled out a survey asking if you intend to buy a car or refrigerator in the coming year? Chances are your answers were part of someone's forecast. Similarly, surveys are done for products sold through distributors. Companies then buy the surveys from the research companies or do their own surveys to use as a starting point for their forecasting. Surveys are better at estimating market potential than sales potential, however, because potential buyers are far more likely to know they will buy something – they just don't know which brand or model. Surveys can also be relatively costly, particularly when they are commissioned for only one company.

Sales Force Composite

A sales force composite is a forecast based on estimates of sales in a given time period gathered from all of a firm's salespeople. Salespeople have a pretty good idea about how much can be sold in the coming period of time (especially if they have bonuses riding on those sales). They've been calling on their customers and know when buying decisions will be made.

Estimating the sales for new products or new promotions and pricing strategies will be harder for salespeople to estimate until they have had some experience selling those products after they have been introduced, promoted, or repriced. Further, management may not want salespeople to know about new products or promotions until these are announced to the general public, so this method is not useful in situations involving new products or promotions. Another limitation reflects salespeople's natural optimism. Salespeople tend to be optimistic about what they think they can sell and may overestimate future sales. Conversely, if the company uses these estimates to set quotas, salespeople are likely to reduce their estimates to make it easier to achieve quota.

Salespeople are more accurate in their near-term sales estimates, as their customers are not likely to share plans too far into the future. Consequently, most companies use sales force composites for shorter-range forecasts in order to more accurately predict their production and inventory requirements. Konica-Minolta, an office equipment manufacturer, has recently placed a heavy emphasis on improving the accuracy of its sales force composites because the cost of being wrong is too great. Underestimated forecasts result in some customers having to wait too long for deliveries for products, and they may turn to competitors who can deliver faster. By contrast, overestimated forecasts result in higher inventory costs.

Executive Opinion

Executive opinion is exactly what the name implies: the best-guess estimates of a company's executives. Each executive submits an estimate of the company's sales, which are then averaged to form the overall sales forecast. The advantages of executive opinions are that they are low cost and fast and have the effect of making executives committed to achieving them. An executive-opinion-based forecast can be a good starting point. However, there are disadvantages to the method, so it should not be used alone. These disadvantages are similar to those of the sales force composites. If the executives' forecast becomes a quota upon which their bonuses are estimated, they will have an incentive to underestimate the forecast so they can meet their targets. Organizational factors also come into play. A junior executive, for example, is not likely to forecast low sales for a product that his or her CEO is pushing, even if low sales are likely to occur.

Expert Opinion

Expert opinion is similar to executive opinion except that the expert is usually someone outside the company. Like executive opinion, expert opinion is a tool best used in conjunction with more quantitative methods. As a sole method of forecasting, however, expert opinions are often very inaccurate. Just consider how preseason college football rankings compare with the final standings. The football experts' predictions are usually not very accurate.

Time Series Techniques

Time series techniques examine sales patterns in the past in order to predict sales in the future. For example, with a trend analysis, the marketing executive identifies the rate at which a company's sales have grown in the past and uses that rate to estimate future sales. For example, if sales have grown 3 percent per year over the past five years, trend analysis would assume a similar 3 percent growth rate next year.

A simple form of analysis such as this can be useful if a market is stable. The problem is that many markets are not stable. A rapid change in any one of a market's dynamics is likely to result in wide swings in growth rates. Just think about auto sales before, during, and after the government's Cash for Clunkers program. What sold the previous month could not account for the effects of the program. Consequently, if an executive were to have estimated auto sales based on the rate of change for the previous period, the estimate would have been way off.

Figure 16.10

The federal government's Cash for Clunkers program resulted in a significant short-term increase in new car sales and filled junkyards with thousands of clunkers!

The Cash for Clunkers program was an unusual situation; many products may have wide variations in demand for other reasons. Trend analysis can still be useful in these situations but adjustments have to be made to account for the swings in rates of change. Two common adjustments are the moving average, whereby the rate of change for the past few periods is averaged, and exponential smoothing, a type of moving average that puts more emphasis on the most recent period.

Correlates and Other Models

A number of more sophisticated models can be useful in forecasting sales. One fairly common method is a correlational analysis, which is a form of trend analysis that estimates sales based on the trends of other variables. For example, furniture-company executives know that new housing starts (the number of new houses that are begun to be built in a period) predict furniture sales in the near future because new houses tend to get filled up with new furniture. Such a correlate is considered a leading indicator, because it leads, or precedes, sales. The Conference Board publishes an Index of Leading Indicators, which is a single number that represents a composite of commonly used leading indicators. Some of these leading indicators are housing starts, wholesale orders, orders for durable goods (items like refrigerators, air conditioning systems, and other long-lasting consumer products), and even consumer sentiment, or how consumers think the economy is doing.

Response Models

Some companies create sophisticated statistical models called response models, which are based on how customers have responded in the past to marketing strategies. JCPenney, for example, takes previous sales data and combines it with customer data gathered from the retailer's Web site. The models help JCPenney see how many customers are price sensitive and only buy products when they are on sale and how many customers are likely to respond to certain offers. The retailer can then estimate the sales for products by market segment based on the offers and promotions directed at those segments.

Market Tests

A market test is an experiment in which the company launches a new offering in a limited market in order to gain real-world knowledge of how the market will react to the product. Since there isn't any historical data on how the product has done, response models and time-series techniques are not effective. A market test provides some measure of sales in response to the marketing plan, so in that regard, it is like a response model, just based on limited data. The demand for the product can then be extrapolated to the full market. However, remember that market tests are visible to your competitors, and they can undertake actions, such as drastic price cuts, to skew your results.

Figure 16.11

HEB uses Waco, Texas, as a test market, combining data from its loyalty program with sales data to see who buys what and at what price.

The grocery chain HEB uses Waco, Texas, as a test site. HEB has a loyalty program that enables it to collect lots of data on its customers. When HEB wants to test market a new product, the firm does it in Waco, where individual customer data can be combined with sales data. Testing in Waco tells HEB who is likely to buy the product and at what price, information that makes extrapolating to their larger market more accurate.