The ability to retain existing customers is a major concern for many businesses. However retention is not the only dimension of interest; the revenue stream associated with each customer is another key factor influencing customer profitability.

In most contractual situations the exact revenue that will be generated per customer is uncertain at the beginning of the contract period; customer revenue is determined by how much of the service each individual consumes. While a number of researchers have explored the problem of modeling retention in a contractual setting, the literature has been surprisingly silent on how to forecast customers' usage (and therefore future revenue) in contractual situations.

We propose a dynamic latent trait model in which usage and renewal behavior are modeled simultaneously by assuming that both behaviors are driven by the same (individual-level) underlying process that evolves over time. We capture the dynamics in the underlying latent variable (which we label “commitment”) using a hidden Markov model, and then incorporate unobserved heterogeneity in the usage process.

The model parameters are estimated using hierarchical Bayesian methods. We validate the model using data from a so-called Friends scheme run by a performing arts organization. First we show how the proposed model outperforms benchmark models on both the usage and retention dimensions. In contrast to most churn models, this dynamic model is able to identify changes in behavior before the contract is close to expiring, thus providing early predictions of churn. A novel aspect of the model is that it allows us to model two processes (churn and usage) that are observed on different time scales (e.g., annual and quarterly, respectively). Moreover, the model provides additional insights into the behavior of the customer base that are of interest to managers.

Firms in a number of service sectors are either complementing or replacing their two-part tariff pricing schemes with three-part tariff plans. While previous research has contributed to our understanding of customer behavior under either two-part or three-part tariffs, nothing has been said about the transition between these two pricing mechanisms. We analyze a data set from a mobile phone operator in South Asia that contains 12 months of tariff choice and monthly usage for 5,381 post-paid customers. The interesting aspect of these data lies in the fact that the focal firm introduced a three-part tariff scheme while retaining the existing two-part tariffs. After the launch of the three-part tariff scheme all consumers were free to switch to the new three-part tariff plans or remain on their existing (two-part) plans. The introduction of the new pricing scheme during the observation period provides us with a natural experiment in which we observe the same set of customers under two different pricing regimes.

We find empirically that those customers who switched to a three-part tariff significantly “over-use” after the switch. In other words, they attain a level of consumption on a three-part tariff that cannot be explained by a simple shift in the budget constraint. Based on existing work in the marketing literature, we postulate that it is the “free” attribute of the inclusive consumption that makes customers increase their consumption up to levels that standard economic theory does not predict.

We estimate a structural econometric model of tariff choice and usage decisions that includes customers' valuation of the “free” attribute. Over time, customers learn about their valuation of the new attribute. The proposed model reflects usage behavior significantly better than a model that only adjusts for a change in the budget constraint. Our results show that more than 88% of the switchers have a positive valuation for the new attribute. In other words, customers value “free” minutes more than minutes at zero marginal cost. If quantifying the effect of this new attribute in our sample, this translates into a 29% increase in consumption and a 12% increase in total revenue. Furthermore, we perform policy simulations to investigate how the firm could increase its revenues by encouraging switching behavior and by setting optimal three-part tariffs.