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Prior to implementing Life Matters, the bank s database marketing campaigns had primarily been focused on a single product. It was possible for a customer to be repeatedly contacted with several product-centered offers that were sometimes confusing and frustrating. The new system allowed the bank to communicate with customers in a more customerfocused manner, while at the same time reducing the costs associated with running many single-product campaigns. Bundling the offers allowed the database marketing group to balance customer focus with the objectives of various product lines and channel capacity constraints for lead follow-up. As a result of the lessons learned through the successive waves, the bank was able to develop complex campaigns approximately 25 to 40 percent faster than by using the previous SAS-based approach. Annual marketing program cost savings of approximately Can$3.5 million were

Analyses of Mean Profit/Loss 47.0000 Devlatlon 974.4681 6091.1028 088.4787 1.0868 0.1392 0.2120 1.4301 0.1572 16.0000 0.3404 0.5318 0.1702

achieved through eliminating separate product campaign mail and branch contact costs. The cost and time savings enabled the database marketing team to execute campaigns for other divisions, such as Wealth Management, Insurance, and Small Business. In total, 120 percent more marketing campaigns were run than had been run in the previous 12-month period, resulting in almost 9 million customer contacts an increase of 180 percent. Life Matters campaigns yielded response rates of over 20 percent and produced an average return on investment of over 100 percent.

Expected SD of Mean T Statislic (PiL > 0) Probability (Siiniflcance) Serial CorrelaIion (lag=l) Associated T Statistic Probability (Significance) Number Of Wlns

The Union Bank of Norway (UBN) had 1 million customers and $25 billion in assets. During the last 20 years, as the bank had grown and become more automated, it had become clear that bank employees were not interacting directly with customers as often as they had been, which meant that the bank was losing touch with its customers needs. The bank was still conducting traditional direct marketing programs with some success, its highest response rate being about 10 percent. Since it operated multiple computer platforms with data scattered among the 175 branches, the bank could not easily gather and use customer-speci c information. Getting a full view of a group of customers took days. Bank management wanted to store and access customer-speci c information in a responsive, easy-to-use format. The goal was to improve revenue and margins through cross-selling and increasing customer use of its most pro table products. To solve these problems, the bank turned to NCR s Teradata Division, installing a Teradata data warehouse in 1995 to capture and store all information about customers, including transactions going back 4 years and about 40 data elements from customer survey responses. In 1999, UBN added the Teradata CRM solution, a relationship optimization and campaign management application, for its events-based marketing initiative. The Teradata system became the foundation for UBN s loyalty program and event-based marketing initiative. It helped the bank determine

In my opinion, the apple is a very special fruit. It s loaded with fiber, vitamins, minerals, and other beneficial phytochemicals (such as phenolic compounds, flavonoids, terpenoids, and D-glucarate), all of which combine to aid all aspects of detoxification. There is definitely something to the old saying An apple a day, keeps the doctor away.

(Microsoft s Excel spreadsheet provides a function to obtain probabilities based on the t-distribution. Numen cal Recipes in C provides the incomplete beta function, which is very easily used to calculate probabilities based on a variety of distributions, including Student s t.) The cumulative t-distribution calculation yields a figure that represents the probability that the results obtained from the trading system were due to chance. Since this figure was small, it is unlikely that the results were due to capitalization on random features of the sample. The smaller the number, the more likely the system performed the way it did for reasons other than chance. In this instance, the probability was 0.1392; i.e., if a system with a true (population) profit