Most market research focuses on understanding customers — their wants and needs, their likes and dislikes. A relatively new tool takes a different approach. Instead of focusing on customers themselves, the buying process model studies the steps that customers go through in making a purchase. Buying process research attempts to identify steps in the process where problems occur, so that marketers can create strategies to overcome roadblocks.

The steps in the process
Most descriptions of the purchase process include five steps:

  1. Problem recognition. The consumer identifies a problem that could be addressed by making a purchase. The problem could be anything from “My feet are killing me” to “I don’t have anything to wear.” In fact, the “problem” might not be a problem at all, just a response to a marketing stimulus, such as a billboard or a television ad.
  2. Information gathering. The consumer collects information about products, including such things as price, availability, options, and features.
  3. Evaluation of alternatives. The consumer weighs the pros and cons of each alternative. Decisions at this stage of the process may be affected by budgets, personal likes or dislikes, and recommendations from family or friends.
  4. Purchase decision. The consumer commits to the purchase.
  5. Post-purchase behavior. The consumer takes some action related to the product. A satisfied customer might tell friends about the product. A dissatisfied customer might experience buyer’s remorse and return the product.

Not every purchase will proceed through all five steps. For example, impulse buys might move directly from problem recognition (“I need that candy bar”) to purchase decision.
Some consumers might decide to drop out of the process at one of the steps. These are the decisions that are especially important for marketers to understand.

Identifying roadblocks
A well-designed survey can identify potential roadblocks at each step in the buying process. Consider this example: You operate a web-based business that sells engineering software. You want to understand the process that customers go through in deciding to purchase your products. You could create a survey with questions like these:

  • What kinds of engineering projects do you typically undertake? (problem recognition)
  • Where do you get most of your information about engineering software? (information gathering)
  • Rank the following features in order of importance to you. (evaluation of alternatives)
  • How would you rate the convenience of purchasing products through our website? (purchase decision)
  • Would you recommend our products to other people in your field? (post-purchase behavior)

By studying the responses to these and other similar questions, you can discover where the “sticking points” are in the buying process, and make the necessary corrections.
As an alternative, if you suspected that most of the roadblocks to purchasing were concentrated in one step, you might focus most of your questions on that step.

Why focus on the process?
The main reason to use the buying process model, as opposed to more traditional types of consumer research, is that it provides a more sophisticated understanding of consumer behavior. The model gives marketers a new way of thinking about the strengths and weaknesses of their campaigns so that they can keep doing what’s working well and change what isn’t. In this respect, it helps provide a greater return on a company’s marketing investment.



Business analytics are turbocharging the engine of business intelligence. Traditionally, business intelligence involved collecting data and using it to answer some simple questions:

  • What’s happening in the business?
  • What’s working well?
  • What problems are we having?

Business analytics go a few steps further. Using statistical business analysis, they answer questions like:

  • Why are these things happening?
  • Where are these trends headed?
  • What are the likely results of our actions?

The answers to these questions can help guide decisions about everything from manufacturing to marketing.

Changing the Odds
The gaming industry offers a good example of how business analytics can turn the odds in a company’s favor. The big casinos collect data about customers from many sources on the casino floor itself. Player cards and slot machines are just a couple of examples. They also conduct surveys and gather data from call centers and online reservation systems. Harrah’s, which operates 26 casinos in 13 states, decided to use business analytics to put their data to work.

First the company used its player card system to discover who its best customers were. It found that 26% of gamblers generated 82% of revenue. It also found that these folks were not the typical “high rollers” that casinos have always courted. Instead they were people who spent modest amounts of money on a regular basis.

Then the company conducted some experiments, using business analytics, to determine what would motivate their best customers to spend more. It found that free chips worked better than free rooms or meals to prompt more spending. This surprised some company executives, who assumed that “comping” meals and rooms was the best way to increase gambling revenue. The company took this experiment one step further, using business analytics to design a three-tiered rewards program based on customer spending.

Harrah’s has had sixteen straight quarters of revenue growth, and company executives believe that business analytics are one of the main reasons for this success.

Business Analytics Become Affordable
At first business analytics were used mainly by large corporations that had the expertise and the budgets to make major investments in these powerful tools. Now, companies like Mineful are making analytical engines widely available to small and medium-size businesses as well.

Basically, analytics allow a business to use its data in new ways. For example, a chain of donut shops could use analytics to predict how opening an hour earlier would affect sales. A hotel chain could use analytics to predict how occupancy rates would be affected by offering a free breakfast. A home improvement store could apply analytics to survey results to determine why sales of appliances have declined.

Effective use of business analytics requires careful planning of the whole process, including:

  • What data to collect.
  • How to collect it.
  • What questions to ask about the data.
  • How to use the answers.

A well-planned effort in business analytics can generate a large return on a relatively small investment.



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