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who had registered within the previous 2 years. When Terri started to assemble the rst house list using the new database, she assigned a source code to each customer for tracking purposes. The addition of the source code meant that the list and the individual customer cells could be tracked separately. She reviewed the customer records and was able to locate and include a group of customers who had a high lifetime value (LTV) but had not purchased courses for 3 or 4 years. Normally this group of customers would be excluded, since it is too costly to mail calendars to customers who haven t purchased in 3 or 4 years. But by targeting only the customers in this group who had a high LTV, Terri was able to keep the mailing costs manageable while she attempted to reactivate their business. The result was that 8 percent of those who were targeted purchased courses, and their purchasing habits were as high the second time around as they had been originally. The reactivated HV group bought an average of three courses each per year compared to the school s overall average of 1.45. The impact of recency on course purchases is obvious in Figure 8-3. By assigning RFM indicators to the customer base, Terri was able to nd new revenue from lost customers and to gain some valuable insights into the school s best customers. Marketing to customer segments by direct-mail promotions is relatively easy. Going beyond that by appointing segment managers and
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Figure 8-3 Sales Rates for the House List Calendar Mailing
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50% 40% 30% 20% 10% 0% 1mo. <12mo. 12mo. <24mo. <48mo. Cal Req Alumni Test NR NR never registered Reactivated HV Grp
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managing segments on a daily basis is much more complicated and seldom pro table. Let s take a look at one enterprise that succeeded.
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While many companies have failed at managing customer segments on a day-to-day basis, National Australia Bank (NAB) succeeded at this task. The lead franchise in NAB was National, a Melbourne-based bank serving more than 4.5 million customers. With the help of Beth Ann Konves of the Teradata Division of NCR, Fernando Ricardo, head of customer knowledge management at the bank, identi ed six major customer segments composed of individuals and businesses. Each segment was managed as a global business. The system, called National Leads, identi ed leads within the bank s database based on signi cant changes (events) in a customer s transaction pattern, such as balance changes, large transactions, or loan renewals. The system also calculated each customer s propensity to buy a product or to respond to an offer. The bank s database was maintained on an NCR Teradata computer using Teradata s CRM analytical software, which was used for the National Leads system. The National Leads system prioritized events and alerted the appropriate bankers each morning so that they could take appropriate action. The system also served as a communications gatekeeper, managing the frequency, content, and channel used for customer interactions during marketing campaigns. The goal was to ensure that customers were contacted with meaningful opportunities at the right times through the right channels. The six major customer segments used by the bank were common to many other banks and nancial institutions: Custom business Packaged business Agribusiness Private Premium Retail
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Each segment was managed by a vice president. Every National customer was assigned to one of the segments on the basis of a nancial needs analysis and pro tability assessment. Within each of these major segments, further re nements were made based on current pro tability, the bank s share of the customer s wallet potential, and the customer s demonstrated nancial needs. As a result of this analysis, there were more than 200 subsegments that were used for target marketing. The system worked this way: The Teradata warehouse was updated with an average of 2.2 million transactions per day. Every night, queries trawled the warehouse to search for any unusual changes. The queries involved complex logic, scanning hundreds of millions of rows in the database and joining from six to ten tables at a time. Once a month, each customer was scored, using a model that predicted the customer s propensity to purchase various products and propensity to respond to product offers. The best leads were selected and sent to the bankers. Every night 250 communication vectors combining the events detected and the propensity predictions were run. The software recommended the action to be taken via ATM, email, mail, or leads to call centers, branches, or package business bankers, who followed up with a phone call or personal mail. The system captured all feedback and responses, measuring them against the opportunities developed. Each offer was placed in a location in a three-dimensional cube, with the three axes being the segment, the channel, and the offer. The system moved customers from one location in the cube to another based on their response to offers. In a typical scenario, the system might direct a banker to contact a customer with an offer, such as a nancial planning appointment or the renewal of a loan. Armed with this lead, the banker would contact the customer to gather nancial pro le information, using the standard nancial needs assessment process developed by the bank. Bankers were
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