Managing Data For Implementing Customer Retention Management Strategies

Managing Data For Implementing Customer Retention Management Strategies

According to the statistics published by Fredrik Abildtrup, a well established marketing consultant and CEO of TeleFaction, it is 3 to 10 times cheaper for an enterprise to retain old customers than attract new ones. In the prevailing competitive scenario, therefore, customer retention management strategies are crucial for the fate of every business enterprise. How do we evaluate these strategies? The number of active customers at the end of a financial year as a percentage of the total number of active customers at the beginning is normally representative of the customer retention rate. However, one financial year is not always the right timeframe for measurement. In certain cases, the repurchase cycle may be longer. At the core of building customer retention management strategies lies proper storage and management of data. Strategies thrive on facts that are stored in the form of relevant data. The data, if misrepresentative, can grossly mislead you in the strategy building process. Depending on how and where the customer data has been retained, companies may obtain a false impression of whether a customer has defected or been retained. Customer Retention Management: Data Storage There are various methods of storing data depending upon the type of business. Some of the prominent ones include: * Product Silos: This type of data retention is meant for crafting customer retention management strategies specifically for insurance companies. Here, a policy is considered to be the customer and its renewal is recorded as a customer retained. However, if a policy holder waits for the tenure of his policy to expire and then opts for a fresh one, he is considered to be a new customer. One needs to keep such factors in perspective before using such data to formulate strategies. * Channel Silos: Sometimes, a customer may become a part of a buyer group to benefit from economies of scale that accompanies bulk purchase. The moment he stops being an individual customer, there is a false impression that he has defected. * Functional Silos: Often, data stored in functional silos are not integrated properly. For instance, a customer might not have made a purchase for several years and is recorded as defected. But, he might still be active with queries and service related issues. Therefore, the relationship should be considered as active.

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