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whom to contact, as it prioritized customers, looking at each customer and evaluating whether that customer needed to be contacted. Events were detected by a set of business rules that searched the database nightly or weekly to determine if there had been any signi cant changes in a customer s life. For example, if a customer made a large deposit in a low-interest account, the system alerted a banker to contact the customer and let the customer know that he or she was losing money. Before implementing its event-driven marketing approach, the bank was concerned that customers might be sensitive to the Big Brother effect. Customer feedback from the pilot program showed that the fears were unjusti ed and proved the relevancy of both the offer and the quality of the interactions.
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By using the data warehouse for direct marketing, UBN increased cross sales, measured against controls. The bank computed a payback period on the investment of less than 12 months. Compared to a previous 2 to 5 percent response rate, traditional campaigns using information in the data warehouse produced conversion rates of up to 40 percent. Loyalty programs gave UBN response rates of up to 70 percent. A pilot program for event-based marketing yielded conversion rates of up to 60 percent. The 60 percent sales conversion rate includes the customer history picture provided by the new database. Some of those who were contacted did not express an immediate interest. Their response was recorded in the database. Later, the customer ended up purchasing the additional products from self-generated decisions or as a result of an automated follow-up. The new conversion rate represented an 1100 percent improvement over the bank s previous average close rate for direct marketing campaigns.
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Cellular communications rms have been losing 2.9 percent of their customers each month, or up to 35 percent per year. In addition, companies have to disconnect many customers for nonpayment of
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bills. Disconnecting the wrong customer can translate into signi cant revenue losses, while keeping delinquent customers also affects pro tability. A wireless service provider was formed by combining the customers of 11 different companies within a short space of time. Its rst job was to create a uni ed database covering the more than 20 million customers that had been in 11 different databases. Rather than waiting years for a data warehouse, the provider created an immediate temporary marketing data mart that combined the billing data from each company. This data mart was accessed by SAS products. The data mart allowed managers in 35 different markets to analyze the data, giving each manager the ability to handle customer trends on a local level. Local managers could immediately address churn, instituting local incentives to keep customers or to rework their existing contract. Examples of the tactics used to reduce churn were initiatives for: Rewarding customers by donating to the Special Olympics Reduced rates for particular customers A special deal on a new phone Free movie rentals at Blockbuster Video Price plans developed after analyzing customer behavior Phone trade-in programs
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Overall, the wireless service provider wanted to know why customers were leaving, broken down by Amount of time with the company Minute use Price plan Thirty other speci c market-level and company-level trends
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Armed with these objectives, the wireless service provider reorganized its IT department and hired a dozen SAS programmers to construct a decision support system. When the temporary database was built, it installed several SAS products on its Sun Enterprise servers, including Enterprise Miner and AppDev Studio.
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As a result of the analysis, the company reduced its churn rate signi cantly compared to a number of control groups selected to match each of the the test groups receiving the promotions. Even a 1 percent reduction in churn rates produced several million dollars in additional revenue. Several valuable lessons were learned: Old equipment turned out to be a top reason for churn. As a result, the company strengthened its trade-in program and allowed customers to get a new phone every time they signed a new contract. The database enabled the company to determine which delinquent customers should be kept and which should be dropped based on modeling and measuring the future pro tability of each. The company was now able to make educated predictions about customer behavior, saving the company millions of dollars per year. Decision making no longer relied on educated guesses as to what would draw and hold customers, but was based on hard facts and gures that provided a detailed map for marketing strategies to follow when creating campaigns. In the process of creating the database, the wireless service provider achieved some valuable benchmarks: Reports could now be produced in days instead of weeks, and reports that were previously impossible could now be created. The company could now produce both companywide and marketlevel analysis of customer behavior. New elds had been added to its database. These new elds gave the company the power to make better predictive decisions. Through customer pro ling, the company was able to tailor messages to different customer groups more effectively.
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