Customer segmentation is the process of dividing customers into distinct subsets (segments or clusters) that behave in the same way or have similar needs. Because each segment is fairly homogeneous in their behavior and needs, they are likely to respond similarly to a given marketing strategy. In the marketing literature, market segmentation approaches have often been used to divide customers into groups in order to implement different strategies. It has been long established that customers demonstrate heterogeneity in their product preferences and buying behaviors (Allenby & Rossi 1999) and that the model built on the market in aggregate is often less efficient than models built for individual segments. Much of this research focuses on examining how variables such as demographics, socioeconomic status, personality, and attitudes can be used to predict differences in consumption and brand loyalty. Distance-based clustering techniques, such as k-means, and parametric mixture models, such as Gaussian mixture models, are two main approaches used in segmentation. While both of these approaches have produced good results in various applications, they are not designed to segment customers based on their behavioral patterns. There may exist natural behavioral patterns in different groups of customers or customer transactions (e.g. purchase transactions, Web browsing sessions, etc.). For example, a set of behavioral patterns that distinguish a group of wireless subscribers may be as follows: Their call duration during weekday mornings is short, and these calls are within the same geographical area. They call from outside the home area on weekdays and from the home area on weekends. They have several “data” calls on weekdays. The above set of three behavioral patterns may be representative of a group of consultants who travel frequently and who exhibit a set of common behavioral patterns. This example suggests that there may be natural clusters in data, characterized by a set of typical behavioral patterns. In such cases, appropriate “behavioral pattern-based segmentation” approaches can constitute an intuitive method for grouping customer transactions.
The related work can be categorized into the following groups.
Since the concept emerged in the late 1950s, segmentation has been one of the most researched topics in the marketing literature. There have been two dimensions of segmentation research: segmentation bases and methods. A segmentation basis is defined as a set of variables or characteristics used to assign potential customers to homogenous groups. Research in segmentation bases focuses on identifying effective variables for segmentation, such as socioeconomic status, loyalty, and price elasticity (Frank et al 1972). Cluster analysis has historically been the most well-known method for market segmentation (Gordon 1980). Recently, much of market segmentation literature has focused on the technology of identifying segments from marketing data through the development and application of finite mixture models (see Böhning (1995) for a review). In general model-based clustering (Fraley & Raftery 1998; Fraley & Raftery 2002), the data is viewed as coming from a mixture of probability distributions, each representing a different cluster.