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Key Elements of an Information System

Figure 9 shows how marketing researchers and managers use information technology to frame questions that provide answers leading to marketing actions. At the bottom of Figure 9 the marketer queries the databases in the information system with marketing questions needing answers. These questions go through statistical models that analyze the relationships that exist among the data. The databases form the core, or data ware­house, where the ocean of data is collected and stored. After the search of this data warehouse, the models select and link the pertinent data, often presenting them in tables and graphics for easy interpretation. Marketers can also use sensitivity analysis to query the database with “what if’ questions to determine how a hypothetical change in a driver like advertising can affect sales.

FIGURE 9

How marketing researchers and managers use information technology

to turn information into action

 

Data Mining: A New Approach to Searching the Data Ocean

Traditional marketing research typically involves identifying possible drivers and then collecting data: Increasing couponing (the driver) during spring will increase trial by first-time buyers (the result). Marketing researchers then try to collect information to attempt to verify the truth of the relationship.

In contrast, data mining is the extraction of hidden predictive information from large databases. Catalog companies such as Lands’ End, Fingerhut, and Spiegel use data mining to find statistical links that suggest marketing actions. For example, Fingerhut studies about 3,500 variables over the lifetime of a consumer’s relation­ship. It has found that customers who change residences are three times as likely as regular customers to buy tables, fax machines, and decorative products but no more likely to buy jewelry or footwear. So Fingerhut has created a catalog especially tar­geted at consumers who have recently moved.

Some of these purchase patterns are common sense: Peanut butter and grape jelly purchases are linked and might suggest a joint promotion between Skippy peanut butter and Welch’s grape jelly. Other patterns link seemingly unrelated purchases: Supermarkets mined checkout data from scanners and discovered men buying diapers in the evening sometimes buy a six-pack of beer as well. So they placed diapers and beer near each other. Placing potato chips between them increased sales of all three.

Still, the success in data mining ultimately depends on humans - the judgments of the marketing managers and researchers in how to select, analyze, and interpret the information.

 

Text 6

STEP 4: DEVELOP FINDINGS

 

Mark Twain once observed, “Collecting data is like collecting garbage. You’ve got to know what you’re going to do with the stuff before you collect it.” Thus, marketing data and information have little more value than garbage unless they are analyzed carefully and translated into logical findings, step 4 in the marketing research approach.



Analyze the Data

Let’s consider the case of Tony’s Pizza and Tere Carral, the marketing manager responsible for the Tony’s brand. We will use hypothetical data to protect Tony’s proprietary information.

Teré is concerned about the limited growth in the Tony’s brand over the past four years. She hires a consultant to collect and analyze data to explain what’s going on with her brand and to recommend ways to improve its growth. Teré asks the consultant to put together a proposal that includes the answers to two key questions:

1. How are Tony’s sales doing on a household basis? For example, are fewer households buying Tony’s pizzas, or is each household buy­ing fewer Tony’s? Or both?

2. What factors might be contributing to Tony’s very flat sales over the past four years?

Facts uncovered by the consultant are vital. For example, is the aver­age household consuming more or less Tony’s pizza than in previous years? Is Tony’s flat sales performance related to a specific factor? With answers to these questions Teré can identify actions in her marketing plan and implement them over the coming year.

 

Present the Findings

Findings should be clear and understandable from the way the data are presented. Managers are responsible for actions. Often it means deliv­ering the results in clear pictures and, if possible, in a single page.

The consultant gives Teré the answers to her questions using Figure 10, a creative way to present findings graphically.

Let’s look over the shoulders of Teré and the consultant while they interpret these findings:

· Figure 10A, the chart showing Annual Scale. This shows the annual growth of Tony’s Pizza brand is stable but virtually flat from 2001 through 2004.

· Figure 10B, the chart showing Average Sales per Household. Look closely at this grath. At first glance, it may seem like sales in 2004 are half what they were in 2001, right? But be careful to read the numbers on the vertical axis. They show that household purchases of Tony’s have been steadily declining over the past four years, from an average of 3.4 pizzas per household in 2001 to 3.1 pizzas per household in 2004. (Significant, but hardly a 50 percent drop.) Now the question is, if Tony’s annual sales are stable, yet the average individual household is buying fewer Tony’s pizzas, what’s going on? The answer is more households are buying pizzas – it’s just that each household is buying fewer Tony’s pizzas. That households aren’t choosing Tony’s is a genuine source of concern. But again, here’s a classic example of a marketing problem representing a marketing opportunity. The number of households buying pizza is growing, that’s good news for Tony.

· Figure 10C, the chart showing Average Annual Sales per Household, by Household Size. Oh, Oh! This chart starts to show a source of the problem: Even though average sales of pizza to households with only one or two peo­ple is stable, households with three or four people and those with five or more are declining in average annual pizza consumption. Which households tend to have more than two people? Answer: Households with children. Therefore, we should look more closely at the pizza-buying behavior of households with children.

· Figure 1OD, the chart showing Average Annual Sales per Household, by Age of Children in the Household. Oh, oh, oh! The picture is becoming very clear now: The real problem is in the serious decline in average consumption in the households with younger children, especially in households with children in the 6- to 12-year-old age group.

Identifying a sales problem in households with children 6- to 12-years-old is an important discovery, as Tony’s sales are declining in a market segment that is known to be one of the heaviest in buying pizzas.

FIGURE 10

Presenting findings to Tony’s marketing manager

that lead to recommendations and actions

 

Text 7

 

STEP 5: TAKE MARKETING ACTIONS

 

Effective marketing research doesn’t stop with findings and recommendations— someone has to identify the marketing actions, put them into effect, and monitor how the decisions turn out, which is the essence of step 5.

 


Date: 2016-01-14; view: 748


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