Profiting from Consumer Behavior: Data Mining Helps Businesses Make More Money by Predicting the Spending Habits of Their Customers

By Donaldson, Sonia A. | Black Enterprise, March 2007 | Go to article overview

Profiting from Consumer Behavior: Data Mining Helps Businesses Make More Money by Predicting the Spending Habits of Their Customers


Donaldson, Sonia A., Black Enterprise


RICHARD MOUSER WAS WATCHING HIS company go slowly down the drain. Mr. Waterfilter (www.mrwaterfilter.com), his 1-year-old, Houston-based dot-com, would soon go bust unless he acted fast.

"I was generating traffic and making sales, but my ratio of ad costs to sales was abysmal," says Mouser, the company president, who spent $85 for every sale he made. He readily admits that although he is a techie with a computer science degree and was running one of the numerous software tools on the market to help improve Web sales results, he had only limited success with it.

Having opted for the do-it-yourself approach, and achieving lackluster results, Mouser was stuck. But at a technology conference in the spring of 2006, he happened to listen in on the tail end of a seminar that David Bullock (www.davidbullock.com) was teaching. Mouser says this chance encounter helped revolutionize his business.

Bullock is managing director of White Bullock Group in Carson City, Nevada, a consulting firm that specializes in a range of business optimization tools and services, including data mining: a market response analysis that helps small companies and large corporations increase their revenues and reduce costs.

By analyzing the spending patterns of a company's existing customers, data miners like Bullock make predictions about consumer choices, thereby improving the efficiency and effectiveness of the way a company sells its products. A better understanding of consumer behavior gives company leaders the clear, actionable information they need to grow their business.

In his seminar, Bullock talked about a data mining technique called the Taguchi method, a testing process that analyzes a few key variables in the sales process that help to accurately pinpoint the best way to achieve a goal. Mouser approached Bullock and asked him for help with his online advertising problems.

Bullock trained Mouser on the Taguchi and multivariate testing applications, and Mouser ran his Website through its paces. He sent visitors to several test pages for the Mr. Water-filter site and tracked their responses. He tested headers, subheads, site copy, and several other elements--and determined which landing page was most effective in attracting customers and, ultimately, in making a sale.

Seven months after using Bullock's consulting services, Mouser's ad costs per sale were an astounding 1/10th of what they had been. Now, instead of paying $85 to make a sale, he spends just over $8--and his pay-per-click costs continue to decrease. "My sales numbers have also gone up dramatically," he adds.

Bullock is part of a small but in-demand group of technologists using data mining. Using applications created by SAS, IBM, Oracle, Computer Associates, and Microsoft (which plans to unveil data mining add-ins to its Office 2007 suite of programs this year)--or by designing their own, as Bullock did--these number crunchers track and explain significant patterns, trends, facts, and exceptions that might otherwise go unnoticed.

Data mining, or knowledge discovery, an estimated $1.85 billion industry, requires the computer-assisted process of digging through and analyzing enormous amounts of data and then extracting the meaning of that data. But for business owners, data mining can be a powerful tool to take their company from zero to profitability--in relatively short order. Specific uses of data mining include:

* Market segmentation--identifying the common characteristics of customers. Market segmentation allows a company's marketing or sales arm to focus on prospects that are "most likely" to buy products.

* Customer chum--predicting the factors that cause customers to leave a business and go to a competitor. Understanding these factors helps companies take action to reduce their churn rate.

* Fraud detection--identifying which transactions are most likely to be fraudulent.

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