Predicting the product purchase patterns of corporate customers

This paper describes TIPPPS (Time Interleaved Product Purchase Prediction System), which analyses billing data of corporate customers in a large telecommunications company in order to predict high value upsell opportunities. The challenges presented by this prediction problem are significant. Firstly, the diversity of products used by corporate telecommunications customers is huge. This, coupled with low product take-up rates, makes this a problem of learning from a very high dimensional feature space with very few minority examples. Further, it is important to give priority specifically to the identification of those new customers who are of high value. These challenges are overcome by introducing a number of modifications to standard data pre-processing and machine learning algorithms, the most important of which are time-interleaving of data and value weighting. Time interleaving is the concatenation of examples from multiple time periods, thus increasing the number of training examples, and hence the number of minority examples. Value weighting assigns importance to minority examples in proportion to the dollar value of take-up, thus biasing the system to identify high value customers. These modifications create a novel algorithm that makes the prediction system practical and usable. Comparison with other techniques designed for similar problems shows that the expected average improvement in ranking accuracy achieved using these modifications is 3.7%. TIPPPS has been in operation for several months and has been successful in identifying many upsell opportunities that were not identified by using the previous manual system.