Consumer Surveys

In the last 25 years, all areas of business—from production to marketing to finance—have become increasingly data driven.1 This is true not only for traditional “bricks and mortar” firms but especially for e-commerce service firms. Today, firms ranging from IBM to Google employ thousands of statisticians and data analysts and pay them more than six-figure salaries. In the e-commerce world, almost everything can be monitored and measured. The detailed behavior of millions of customers can be tracked online. For instance, Google uses scores of statistical techniques to improve its search engine, monitor search behavior, and fine-tune its search rankings of the most popular sites.

Yet, data isn’t synonymous with knowledge. The key is to be able to economically analyze enormous databases in order to extract relevant information such as the firm’s demand curve. Fortunately, there are numerous, powerful statistics and forecasting programs; these are spreadsheet based, userfriendly, and readily available at low cost. This permits a powerful division of labor. Computers are very good at uncovering patterns from huge amounts of data, while humans are good at explaining and exploiting those patterns.

What’s the best advice for a college or postgraduate student preparing for a business career or for life in general? After learning some economics, be sure to learn enough statistics.

This chapter is organized as follows. We begin by examining sources of information that provide data for forecasts. These include consumer interviews and surveys, controlled market studies, and uncontrolled market data. Next, we explore regression analysis, a statistical method widely used in demand estimation. Finally, we consider a number of important forecasting methods.

COLLECTING DATA

Consumer Surveys

A direct way to gather information is to ask people. Whether face to face, by telephone, online, or via direct mail, researchers can ask current and prospective customers a host of questions: How much of the product do you plan to buy this year? What if the price increased by 10 percent? Do price rebates influence your purchase decisions, and, if so, by how much? What features do you value most? Do you know about the current advertising campaign for the product? Do you purchase competing products? If so, what do you like about them?

1The way in which the statistical analysis of data drives business is discussed by S. Lohr, “For Today’s Graduate, Just One Word: Statistics,” The New York Times, August 6, 2009, p. A1; and G. Mankiw, “A Course Load for the Game of Life,” The New York Times, September 5, 2010, p. BU5. For an assessment and survey of computer programs for statistical analysis and forecasting, see J. Swain, “Software Survey: Statistical Analysis,” ORMS Today (February 2011): 42–47, and J. Yurkiwicz, “Software Survey: Forecasting,” ORMS Today (June 2010): 36–43.

Data-Driven Business

Consumer product companies use surveys extensively. In a given year, Campbell Soup Company questions over 100,000 consumers about foods and uses the responses to modify and improve its product offerings and to construct demand equations. Marriott Corporation used this method to design the Courtyard by Marriott hotel chain, asking hundreds of interviewees to compare features and prices. Today, the explosion of online surveys allows firms to collect thousands of responses (often highly detailed) at very low cost.

SURVEY PITFALLS Though useful, surveys have problems and limitations. For example, market researchers may ask the right questions, but of the wrong people. Economists call this sample bias. In some contexts, random sampling protects against sample bias. In other cases, surveys must take care in targeting a representative sample of the relevant market segment.

A second problem is response bias.Respondents might report what they believe the questioner wants to hear. (“Your product is terrific, and I intend to buy it this year if at all possible.”) Alternatively, the customer may attempt to influence decision making. (“If you raise the price, I definitely will stop buying.”) Neither response will likely reflect the potential customer’s true preferences.

A third problem is response accuracy.Even if unbiased and forthright, a potential customer may have difficulty in answering a question accurately. (“I think I might buy it at that price, but when push comes to shove, who knows?”) Potential customers often have little idea of how they will react to a price increase or to an increase in advertising. A final difficulty is cost. Conducting extensive consumer surveys is extremely costly. As in any economic decision, the costs of acquiring additional information must be weighed against the benefits.2

An alternative to consumer surveys is the use of controlled consumer experiments. For example, consumers are given money (real or script) and must make purchasing decisions. Researchers then vary key demand variables (and hold others constant) to determine how the variables affect consumer purchases. Because consumers make actual decisions (instead of simply being asked about their preferences and behavior), their results are likely to be more accurate than those of consumer surveys. Nonetheless, this approach shares some of the same difficulties as surveys. Subjects know they are participating in an experiment, and this may affect their responses. For example, they may react to price much more in an experiment than they do in real life. In addition, controlled experiments are expensive. Consequently, they generally are small (few subjects) and short, and this limits their accuracy. As the following example shows, consumer surveys and experiments do not always accurately foretell actual demand.

2The same point applies to setting the design and size of a given consumer survey. In principle, the number of respondents should be set such that the marginal benefit from adding another respondent in the sample matches its marginal cost.