Software That Thinks

Here's a look at how companies can use artificial intelligence to track customers and predict market trends.

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Artificial intelligence is moving out of the lab and into the office. Several programs now track customers' buying habits to predict market trends

KILLER TOOLS
For Plow & Hearth, a $30-million catalog company that sells yard and garden wares, traditional marketing strategies just weren't working. John Popowski, the marketing manager until 1995, and Pete Rice, the circulation manager, tried the recency, frequency, and monetary (RFM) method, which classifies people according to the date of their most recent purchase, the frequency with which they buy, and the amount they usually spend. They'd jam customers into categories, in an attempt to determine which ones would prove most valuable. But too often those categories were completely random. Rice recalls sessions in which a few managers would sit down and say, "OK. Let's divide the monetary categories into ranges of $20." "The whole process of trying to determine which customers were valuable was so arbitrary," he says.

At the time many of Plow & Hearth's bigger competitors were using models built by statisticians to predict consumer spending. But that alternative was cost-prohibitive to a small company. Even hiring statisticians part-time was out of the question. So when Popowski got a call from Advanced Software Applications (ASA) asking if he wanted his company to be a beta test site for a new product that used artificial intelligence to track buying trends, he jumped at the chance. Soon he was predicting customer behavior down to the smallest purchase.

Artificial intelligence-- neural networks, or neural nets in the jargon--can be glamorous. These computer applications were designed to approach problems the way the human brain does: by trying to recognize the patterns that underlie a complex set of data. Neural nets, like people, can be "trained." They start off with wild guesses, but over time they learn to refine their guesswork until they can pick out even the most subtle patterns--in much the same way that you pick out the face of an old friend at a high school reunion. The technology's power lies in its ability to analyze nearly endless combinations of variables very quickly--more quickly, some say, and with greater accuracy than traditional statistical models.

Law-enforcement agencies use neural nets to pick out patterns in travel data (one-day trips to the Cayman Islands on the first of each month, for example), hoping to pinpoint likely drug dealers. Some hospitals are using them to determine a patient's likelihood of contracting cancer or other diseases. But they are also making their way into marketing and other business arenas. According to Bob Barrie of Global Management Technologies, a small software-development and consulting firm in Atlanta, neural nets work particularly well when it comes to analyzing down-to-the-minute trends. Barrie, whose clients include Walt Disney and several large banks, uses a neural net from BioComp Systems to figure out staffing needs based on customer traffic--a task that requires precision analysis, down to the half hour. Increasingly, even businesses a fraction of the size of many of Barrie's clients are firing up neural nets to help them navigate ever-thicker forests of data and make sense of myriad customer traits and buying habits.

Brain in a Box
Plow & Hearth is a case in point. Based in Madison, Va., today the 80-employee company is definitely on the cutting edge of marketing research. But when it started using ASA's ModelMAX, nearly three years ago, most catalog companies three times its size didn't even know the technology existed.

Unlike some neural-net products, which have to be built up by trained programmers to run useful models, ModelMAX is a polished, ready-to-use marketing tool with a simple Windows interface. Users don't have to know how neural nets work (ModelMAX's $25,000 price tag reflects the product's simplicity); they just have to know how their company works and have access to its information on purchasing behavior. Each time Pete Rice decides to do a catalog mailing, for example, he pulls up the information about customers and their buying habits gathered from previous mailings and purchases. He enters all the key variables, for example, the time of year the customer made his or her first purchase, where (by zip code) the customer lives, and which products the customer buys most often. ModelMAX works through the information to determine which combinations of customer characteristics are most important in predicting customer value. Rice has learned, for instance, that customers whose first purchase is clothing tend to stay customers. That fact was surprising, given that Plow & Hearth sells clothes only as a peripheral product. The information has been extremely helpful in targeting which customers to pursue aggressively.

The hard part, explains Rice, is learning how to sort through and organize the company's information (feed for the neural-net models) so that the program runs easily. He was able to use the product to churn out decent reports within a week, he says. But it took about a year before he could use it to its fullest. His efforts have paid off, though. In the past three years, Plow & Hearth's per-catalog sales have gone up 32%, and its hit rates have risen by 19%.

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