The Mathematics of Customer Loyalty

There is, in general, a high degree of correlation within a purchase data set. Long-time accounts tend to make more purchases, spend more, and buy a wider selection of products and place orders more frequently. So, the traditional loyalty indices – total amount, number of purchases, number of products, retention (duration of customer relationship), or recency (time since last purchase) – will tend to identify the best and worst customers. These indices will not, however, provide a consistent evaluation of the middle 75 – 80% of a firm’s customers. A tool with a broader scope is needed to evaluate the average customers – those who are neither the best nor the worst but form the great middle-majority. Such a tool can recognize that the customer with a consistent record of small purchases may be more loyal than one who made a single large purchase last quarter, and, hence have greater value to the firm.

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