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Unformatted text preview: ucts. In an on-line store, however, the product
groups may be quite different, often based on complementing the item under consideration. In
particular, the site can take into account not only the item you’re looking at, but what is in your
shopping cart as well, thus leading to even more customized recommendations.
First, Big Sam’s used clustering to discover which products grouped together naturally. Some of
the clusters were obvious, such as shirts and pants. Others were surprising, such as books about
desert hiking and snakebite kits. They used these groupings to make recommendations whenever
someone looked at a product.
They then built a customer profile to help them identify those customers who would be interested
in the new products they were always adding to their catalog. They found that steering people to
these selected products not only resulted in significant incremental sales, but also solidified their
relationship with the customer. Surveys established that they were viewed as a trusted advisor for
clothing and gear.
To extend their reach further, Big Sam’s started a program through which customers could elect
to receive e-mail about new products that the data mining models predicted would interest them.
While the customers viewed this as another example of proactive customer service, Big Sam’s
found it to be a program of profit improvement.
The effort in personalization paid off for Big Sam’s with significant, measurable increases in
repeat sales, average number of sales per customer, and average size of a sale.
Retaining good customers via data mining
For almost every company, the cost of acquiring a new customer exceeds the cost of keeping
good customers. This was the challenge facing Know Service, an Internet Service Provider (ISP)
whose attrition rate was the industry average, 8% per month. Since they have one million
customers, this means 80,000 customers left each month. The cost to replace these customers is
$200 each, or $16,000,000 – plenty of incentive to start an attrition management program.
The first thing Know Service needed to do was prepare the data for predicting which customers
would leave. They needed to select the variables from their customer database and perhaps
transform them. The bulk of their users were dial-in clients (as opposed to clients who are always
connected through a T1 or DSL line), so they knew how long each user was connected to the
Web. They also knew the volume of data transferred to and from a user’s computer, the number
of e-mail accounts a user had, the number of e-mail messages sent and received, and a customer’s
service and billing history. In addition, they had demographic data that customers provided at
Next they needed to identify who were “good” customers. This is not a data mining question but a
business definition (such as profitability or lifetime value) followed by a calculation. Know
Service built a model to profile their profitable customers and thei...
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This note was uploaded on 11/25/2010 for the course CENG ceng taught by Professor Ceng during the Spring '10 term at Universidad Europea de Madrid.
- Spring '10