""Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model"

This research extends a Pareto/NBD model of customer-base analysis using a hierarchical Bayesian (HB) framework to suit today's customized marketing. The proposed HB model presumes three tried and tested assumptions of Pareto/NBD models: (1) a Poisson purchase process, (2) a memoryless dropout process (i.e., constant hazard rate), and (3) heterogeneity across customers, while relaxing the independence assumption of the purchase and dropout rates and incorporating customer characteristics as covariates. The model also provides useful output for CRM, such as a customer-specific lifetime and survival rate, as by-products of the MCMC estimation. Using three different types of databases --- music CD for e-commerce, FSP data for a department store and a music CD chain, the HB model is compared against the benchmark Pareto/NBD model. The study demonstrates that recency-frequency data, in conjunction with customer behavior and characteristics, can provide important insights into direct marketing issues, such as the demographic profile of best customers and whether long-life customers spend more.

Using Customer Relationship Trajectories to Segment Customers and Predict Profitability

A central premise of relationship marketing theory is that economic benefits flow fromretaining customers. However, the early research focus on the duration of the relationship may obscure other important aspects of the interactions with the customer that drive profitability. Borrowing from the branding literature, where different types of customer relationships have been described (but not empirically examined), we study the patterns of business customers’ buying behavior, or trajectories that characterize customer-firm relationships over time, and their impact on profitability. We develop a finite mixture model relating customer relationship trajectories to profitability over a three year period. Our analysis yields five segments, or types of customer-firm relationships, for this dataset. We find key determinants of profitability vary across types of customer relationship. Interestingly, in none of these segments does duration predict profitability.marketing ;

Customer-Base Analysis in an Online Search Setting

Consider a major online travel site that presents users with a wide selection of search results when a user enters a query into their system. From October 1st through October 15th 2009, the behavior of all users searching for hotels in four major destinations were collected and compiled. This information included details including the links users were presented, the order in which they were shown, the number of links users were shown, and most importantly, which links users actually clicked. Given this data, management would like to know more about their users to determine how best to display the search results

Counting Your Customers the Easy Way: An Alternative to the Pareto/NBD Model

Today’s managers are very interested in predicting the future purchasing patterns of their customers, which can then serve as an input into “lifetime value” calculations. Among the models that provide such capabilities, the Pareto/NBD “counting your customers” framework proposed by Schmittlein et al. (1987) is highly regarded. However, despite the respect it has earned, it has proven to be a difficult model to implement, particularly because of computational challenges associated with parameter estimation. We develop a new model, the beta-geometric/NBD (BG/NBD), which represents a slight variation in the behavioral “story” associated with the Pareto/NBD but is vastly easier to implement. We show, for instance, how its parameters can be obtained quite easily in Microsoft Excel. The two models yield very similar results in a wide variety of purchasing environments, leading us to suggest that the BG/NBD could be viewed as an attractive alternative to the Pareto/NBD in most applications

Do Private Labels Generate Loyalty? Empirical Evidence for German Frozen Pizza

food retailing, private labels, brand loyalty, panel data, hazard analysis, Consumer/Household Economics, Demand and Price Analysis, Institutional and Behavioral Economics, Marketing,

Ranking customers for marketing actions with a two-stage Bayesian cluster and Pareto/NBD models

Modelling customer behaviour to predict their future purchase frequency and value is crucial when selecting customers for marketing activities. The profitability of a customer and their risk of inactivity are two important factors in this selection process. These indicators can be obtained using the well-known Pareto/NBD model. Here we cluster customers based on their purchase frequency and value over a given period before applying the Pareto/NBD model to each cluster. This initial cluster model provides the customer purchase value and improves the predictive accuracy of the Pareto/NBD parameters by using similar individuals when fitting the data. Finally, taking the outputs from both models, the initial cluster and Pareto/NBD, we present some recommendations to classify customers into interpretable groups and facilitate their prioritisation for marketing activities. To illustrate the methodology, this paper uses a database with sales from a beauty products wholesaler.Peer ReviewedPostprint (published version

"Counting Your Customers": When will they buy next? An empirical validation of probabilistic customer base analysis models based on purchase timing

This research provides a new way to validate and compare buy-till-you-defect [BTYD] models. These models specify a customer’s transaction and defection processes in a non-contractual setting. They are typically used to identify active customers in a com- pany’s customer base and to predict the number of purchases. Surprisingly, the literature shows that models with quite different assumptions tend to have a similar predictive performance. We show that BTYD models can also be used to predict the timing of the next purchase. Such predictions are managerially relevant as they enable managers to choose appropriate promotion strategies to improve revenues. Moreover, the predictive performance on the purchase timing can be more informative on the relative quality of BTYD models. For each of the established models, we discuss the prediction of the purchase timing. Next, we compare these models across three datasets on the predictive performance on the purchase timing as well as purchase frequency. We show that while the Pareto/NBD and its Hierarchical Bayes extension [HB] models perform the best in predicting transaction frequency, the PDO and HB models predict transaction timing more accurately. Furthermore, we find that differences in a model’s predictive performance across datasets can be explained by the correlation between behavioral parameters and the proportion of customers without repeat purchases