Customer Segmentation with Constrained Cluster Analysis

Partition techniques used to build clusters of similar market baskets in anonymous transaction datasets have been explained in Section 2.2.3. The question is how to extend these algorithms to build clusters of customers with similar buying behavior. One idea is that the existing partitioning approaches should take the identification numbers (IDs) of every transaction into consideration and use them to build groups of customers with comparable purchasing behavior, as reflected in similar market basket compositions over a specific period of time. Algorithms of constrained clustering can handle the data extension of the customer identification for this purpose (cf. Basu, Banerjee and Mooney 2004, Basu, Davidson and Wagstaff 2008). The need for such algorithms has been known for as long as the problem has existed.

In many real-world situations, additional information about the dataset’s structure is available from the outset and forces the ordering of the entities subject to a specific constraint. For example, in soil science analysts have to construct sets of contiguous terrains characterized by a similar distribution of soil prop-erties. Instead of just grouping the area’s properties (i.e. the items), the segmentation has to consider the location of the mineral resources in a specific terrain (i.e. the constraints) when building the sets (cf.

Aldenderfer and Blashfield 1984, Wagstaff, Cardie, Rogers and Schroedl 2001). The idea is to integrate the background information for clustering purposes. By including constraints conditioning the linkage between particular entities, analysts hope to enhance the output of the segmentation and come closer to the natural structure of the dataset (cf. Gordon 1981). Although verifying the ”real” groups within empir-ical data is usually impossible, the constrained cluster algorithms seem to describe predefined grouping information within synthetic datasets better (cf. Aldenderfer and Blashfield 1984, Wagstaff et al. 2001).

Basu et al. (2008) provide an introduction to constrained clustering and explain a number of available algorithms.

In the MBA context, the collected buying histories provide the background information. The m values define the bundle of transactions which have to belong to one specific customer. Instead of clustering all single, anonymous market baskets, the approaches link the transaction sequences (or buying histories) as background information to the cluster algorithm. In terms of the classification literature, the buying his-tories act as so-called must-link constraints, since all transactions of the whole sequence have to belong to a single group and must not spread onto different segments (Wagstaff et al. 2001). Hence, a result-ing segment includes a number of customers who are characterized by makresult-ing similar compositions of jointly purchased categories during their shopping trips. Since algorithms of K-centroid cluster analy-sis are a popular technique for exploratory MBA, in the next chapter we introduce extensions of these algorithms to deal with constraints and choose an appropriate one for our target marketing approach.

The previous chapter explained the different exploratory MBA techniques. To meet the demands stated in the introduction, we combine some of these techniques to build a target marketing framework. This chap-ter introduces the methodological concept of the framework’s stepwise procedure in detail. The com-bined approach supports retailers in partitioning their customers, understanding the segment-character-istic category correlations and identifying those categories which should be featured in an appropriate target marketing campaign to optimize its profits.

Figure 3.1 illustrates the complete procedure of the framework, the steps of which are described in the subsequent sections. In keeping with the objective of segmenting the households, we first want to build customer groups showing similar category correlations among the items bought less frequently (i.e. LFC). Therefore, we test three modified algorithms of K-centroid cluster analysis dealing with constraints as introduced in Section 2.3.4. The presented algorithms take into account the fact that the individual buying history summarizes the transactions of a certain customer. Instead of dealing only with single transactions, the algorithm has to consider the complete transaction sequence of a customer as must-link constraints when building the customer groups. Since more than one alternative is present to implement the must-link constraints into the iterative procedures of the algorithm, we will compare three relevant variations of KCCA algorithms.

Taking the buying sequences into consideration is of crucial importance for our approach. Due to the lower purchase frequencies of products in the long-tail, there tend to be only a few similar basket compo-sitions, and finding groups according to this feature becomes difficult. Using the additional information that the transactions of a buying sequence all have to belong to the same cluster should enhance the recog-nition of similar customers. For verification purposes, we analyze artificial transactions with a known grouping structure. The predefined structure of the artificial data is comparable to that of real-world transaction datasets. Hence, the algorithm which reveals the structure of the artificial data best might be more likely to find the groups in the dataset provided by the empirical application in Chapter 4.

The next two steps of the framework aim at deriving suitable item recommendations for each cluster of customers. If the cluster analysis identifies customers with similar basket structures, this implies members’ comparable interest in particular product compositions. Recommendations derived from the

S T E P 1 S T E P 2 S T E P 3

Figure 3.1: Stepwise procedure of the framework

found segment-specific category correlations promise to be better attuned to the expectations of the clus-ter members. Consequently, these items will also have a greaclus-ter chance of stimulating cross-selling.

An association rule mining algorithm specifies the category correlations after the partitioning has taken place. Therefore, the combined buying histories of a customer group are pooled into a segment-specific transaction dataset and are explored in the same way as the aggregated transactions. The resulting fre-quent itemsets include segment-specific purchase correlations which occur sufficiently frefre-quently in the corresponding cluster. By combining ARM with partitioning cluster analysis, we balance the deficiency of most ARM algorithms in discovering product associations that only reflect the buying behavior of average clientele (cf. Section 2.2.2).

From the revealed segment-specific frequent itemsets of the foregoing step, the final step of the approach can extract single items used to build a recommendation list for target marketing purposes. To reduce the number of found associations, we select only the FI which contribute most to the retailer’s utility.

As introduced in Section 2.2.2, a filter measure is used to separate the interesting associations from the less-important ones. This reduces the number of considered category correlations and decreases the computational effort of the subsequent steps of the analysis. Moreover, a hierarchical clustering of discovered segment-specific associations can give retailers a better insight into the category-correlations of a specific cluster and makes it possible to group the segment-specific FI according to their similarity.

The frequent itemsets are used as input for an optimization routine. This routine determines a predefined number of single categories which maximize the profit and take categories’ purchase correlations and their gross profit margins into account.

The segmentation objective of the approach is the most complex module since the cluster building pro-cess depends heavily on an appropriate algorithm. If the algorithm is not able to build a useful customer partition for marketing purposes, the remaining steps of the approach will be pointless. In the next section, we discuss the partitioning problem in detail and compare three modified KCCA techniques.

3.1 STEP 1: Identifying the Customer Segments with Constrained