The Wheel of Fortune Strategies to Maximize CLV:

Pitching the Right Product, to the Right Customer, at the Right Time:

Companies are constantly involved in predicting customer buying behavior. In such an exercise, the most common method used by companies involves two steps. The first step is to estimate the probability that a customer will choose to purchase a particular product. The second is to estimate the probability that a customer will make a purchase at a particular time. Most firms stop at the first step, which limits their ability to make accurate predictions about the timing of purchases. However, even those companies, which follow the process may not be successful. In a multi-product firm, it is not easy to speculate what product a particular customer is going to buy next. But, from the firm’s point of view, this is a very valuable piece of information because the firm can then decide the message and timing of the customized communication strategy. An ideal contact strategy is one where the firm is able to deliver a sales message that is relevant to the product that is likely to be purchased in the near future by a customer. This could be achieved by accurately predicting the purchase sequence.

Understanding the purchase sequence calls for analyzing past customer purchases and estimating the likelihood of future purchases to design optimal contact strategies. Some questions that need to be answered are (i) in which product category the customer is likely to make a purchase (ii) at what intervals and at time period the customer will make a purchase (iii) how much is the customer likely to spend or in other words, how profitable the customer is likely to be. This strategy describes a model that helps in analyzing and answering the above questions and predicts the purchase sequence of each customer. Once these questions are answered, the next step is to design an optimal allocation strategy that is aimed at efficiently contacting the customers to induce them to make the next purchase. When tested in a B2B setting, 85% of the customers predicted by this model to make a purchase actually went on to do so. In comparison, only 55% of the customers predicted by the traditional model actually made a purchase. When this strategy was implemented in the B2B setting, an increase in ROI of 160% was observed. Thus, this strategy suggests that efficient management of the purchase sequence not only increases revenue by accurately predicting and pre-empting a customer purchase, but also minimizes cost by reducing the frequency of customer contacts.