In the narrow sense, data mining is a collection of tools and techniques. It is one of several technologies required to support a customer-centric enterprise. In a broader sense, data mining is an attitude that business actions should be based on learning, that informed decisions are better than uninformed decisions, and that measuring results is beneficial to the business. Data mining is also a process and a methodology for applying the tools and techniques. For data mining to be effective, the other requirements for analytic CRM must also be in place. In order to form a learning relationship with its customers, a firm must be able to:

Notice what its customers are doing

Remember what it and its customers have done over time

Learn from what it has remembered

Act on what it has learned to make customers more profitable

Although the focus of this book is on the third bullet-learning from what has happened in the past-that learning cannot take place in a vacuum. There must be transaction processing systems to capture customer interactions, data warehouses to store historical customer behavior information, data mining to translate history into plans for future action, and a customer relationship strategy to put those plans into practice.

The Role of Transaction Processing Systems

A small business builds relationships with its customers by noticing their needs, remembering their preferences, and learning from past interactions how to serve them better in the future. How can a large enterprise accomplish something similar when most company employees may never interact personally with customers? Even where there is customer interaction, it is likely to be with a different sales clerk or anonymous call-center employee each time, so how can the enterprise notice, remember, and learn from these interactions? What can replace the creative intuition of the sole proprietor who recognizes customers by name, face, and voice, and remembers their habits and preferences?

In a word, nothing. But that does not mean that we cannot try. Through the clever application of information technology, even the largest enterprise can come surprisingly close. In large commercial enterprises, the first step-noticing what the customer does-has already largely been automated. Transaction processing systems are everywhere, collecting data on seemingly everything. The records generated by automatic teller machines, telephone switches, Web servers, point-of-sale scanners, and the like are the raw material for data mining.

These days, we all go through life generating a constant stream of transaction records. When you pick up the phone to order a canoe paddle from L.L.

Bean or a satin bra from Victorias Secret, a call detail record is generated at the local phone company showing, among other things, the time of your call, the number you dialed, and the long-distance company to which you have been connected. At the long-distance company, similar records are generated recording the duration of your call and the exact routing it takes through the switching system. This data will be combined with other records that store your billing plan, name, and address in order to generate a bill. At the catalog company, your call is logged again along with information about the particular catalog from which you ordered and any special promotions you are responding to. When the customer service representative that answered your call asks for your credit card number and expiration date, the information is immediately relayed to a credit card verification system to approve the transaction; this too creates a record. All too soon, the transaction reaches the bank that issued your credit card, where it appears on your next monthly statement. When your order, with its item number, size, and color, goes into the cata-logers order entry system, it spawns still more records in the billing system and the inventory control system. Within hours, your order is also generating transaction records in a computer system at UPS or FedEx where it is scanned about a dozen times between the warehouse and your home, allowing you to check the shippers Web site to track its progress.

These transaction records are not generated with data mining in mind; they are created to meet the operational needs of the company. Yet all contain valuable information about customers and all can be mined successfully. Phone companies have used call detail records to discover residential phone numbers whose calling patterns resemble those of a business in order to market special services to people operating businesses from their homes. Catalog companies have used order histories to decide which customers should be included in which future mailings-and, in the case of Victorias secret, which models produce the most sales. Federal Express used the change in its customers shipping patterns during a strike at UPS in order to calculate their share of their customers package delivery business. Supermarkets have used point-of-sale data in order to decide what coupons to print for which customers. Web retailers have used past purchases in order to determine what to display when customers return to the site.

These transaction systems are the customer touch points where information about customer behavior first enters the enterprise. As such, they are the eyes and ears (and perhaps the nose, tongue, and fingers) of the enterprise.

The Role of Data Warehousing

The customer-focused enterprise regards every record of an interaction with a client or prospect-each call to customer support, each point-of-sale transaction, each catalog order, each visit to a company Web site-as a learning opportunity. But learning requires more than simply gathering data. In fact,

many companies gather hundreds of gigabytes or terabytes of data from and about their customers without learning anything! Data is gathered because it is needed for some operational purpose, such as inventory control or billing. And, once it has served that purpose, it languishes on disk or tape or is discarded.

For learning to take place, data from many sources-billing records, scanner data, registration forms, applications, call records, coupon redemptions, surveys-must first be gathered together and organized in a consistent and useful way. This is called data warehousing. Data warehousing allows the enterprise to remember what it has noticed about its customers.

Customer patterns become evident over time. Data warehouses need to support accurate historical data so that data mining can pick up these critical trends.

One of the most important aspects of the data warehouse is the capability to track customer behavior over time. Many of the patterns of interest for customer relationship management only become apparent over time. Is usage trending up or down? How frequently does the customer return? Which channels does the customer prefer? Which promotions does the customer respond to?

A number of years ago, a large catalog retailer discovered the importance of retaining historical customer behavior data when they first started keeping more than a years worth of history on their catalog mailings and the responses they generated from customers. What they discovered was a segment of customers that only ordered from the catalog at Christmas time. With knowledge of that segment, they had choices as to what to do. They could try to come up with a way to stimulate interest in placing orders the rest of the year. They could improve their overall response rate by not mailing to this segment the rest of the year. Without some further experimentation, it is not clear what the right answer is, but without historical data, they would never have known to ask the question.

A good data warehouse provides access to the information gleaned from transactional data in a format that is much friendlier than the way it is stored in the operational systems where the data originated. Ideally, data in the warehouse has been gathered from many sources, cleaned, merged, tied to particular customers, and summarized in various useful ways. Reality often falls short of this ideal, but the corporate data warehouse is still the most important source of data for analytic customer relationship management.

The Role of Data Mining

The data warehouse provides the enterprise with a memory. But, memory is of little use without intelligence. Intelligence allows us to comb through our memories, noticing patterns, devising rules, coming up with new ideas, figuring out