How can cluster analysis be best used?
The objective of cluster analysis is to assign observations to groups (clusters) so that observations within each group are similar to one another with respect to variables or attributes of interest and the groups themselves stand apart from one another. In other words, the objective is to divide the observations into homogeneous and distinct groups. In contrast to the classification problem where each observation is known to belong to one of a number of groups and the objective is to predict the group to which a new observation belongs, cluster analysis seeks to discover the number and composition of the groups.
Before we do a clustering of the data, we must understand the objectives of the same. Or else at the end of everything we will be left with loads of charts and graphs and nothing valuable to decipher from all that data. So we must understand why we need to cluster information under one head and what values can be added by correlating the different clusters that we make. For example, if we group the features and attributes of a product such as a mobile phone which concerns the product features such as sms, wi-fi, 3g, alarm, clock, radio, music player, torch, etc then we might be able to tell what are the things we should focus on depending on the customer preferences and dislikes. We can tell whether any feature is underutilised in any of the groups of people, if some features are not adding any value to the customer but he has to pay for it, whether a bundling of features can be given to the customers which will make their mobile experience more useful, time saving and convenient.
Uses of cluster analysis in marketing
The primary use of cluster analysis in marketing has been for market segmentation. And now market segmentation has become an important tool for both academic research and applied marketing. Today the marketing world is equipped with hundreds of market segmentation techniques and most of these techniques instead of bringing simplicity, confuses the marketer. These techniques have served to shift discussions of researchers form more substantive issues of meta-research directed at integrating market segmentation research. One of the major areas of future research should be the evaluation of the conditions under which various data analytical techniques are most appropriate.
All segmentation research, regardless of the method used, is designed to identify groups of entities (people, markets, organizations) that share certain common characteristics (attitudes, purchase propensities, media habits, etc.). Stripped of the specific data employed and the details of the purposes of a particular study, segmentation becomes a grouping task.
It has been noticed that researchers tend to select grouping methods largely on the basis of familiarity, availability, and cost rather than on the basis of the methods’ characteristics and appropriateness. These practices can be attributed to the lack of research on similarity measures, grouping (clustering) algorithms, and effects of various data transformations.
A second and equally important use of cluster analysis has been in seeking a better understanding of buyer behaviours by identifying homogeneous groups of buyers. Cluster analysis has been less frequently applied to this type of theory-building problem, possibly because of theorists’ discomfort with a set of procedures which appear ad hoc. Cluster analysis is one means for developing such taxonomies.
Cluster analysis has been employed in the development of potential new product opportunities. By clustering brands/products, competitive sets within the larger market structure can be determined. Thus, a firm can examine its current offerings vis-à-vis those of those of its competitors. The firm can determine the extent to which a current or potential product offering is uniquely positioned or is in a competitive set with other products. Although cluster analysis has not been used frequently in such applications, largely because of the availability of other techniques such as multidimensional scaling, it is not uncommon to find cluster analysis used as an adjunct to these other techniques. Cluster analysis has also been suggested as an alternative to factor discriminant analysis. In such applications, in which case cluster analysis would not be used as a classification technique and the analyst would face a different set of issues from those addressed here.
Cluster analysis has also been employed by several researchers in the problem of test market selection. Such applications are concerned with the identification of relatively homogeneous sets of test markets which may become interchangeable in test market studies. The identification of such homogeneous sets of test markets allows generalization of the results obtained in ine test market to other test markets in the same cluster, thereby reducing the number of test markets required.
Finally the cluster analysis has been used as a general data reduction technique to develop aggregates of data which are more general and more easily managed than individual observations. For e.g. limits on the number of observations that can be used in multidimensional scaling programs often necessitate an initial clustering of observations. Homogeneous clusters then become the unot of analysis for the multidimensional scaling procedure.
The lack of speciality about the method of clustering in some of the marketing studies tells about the problems associated with the use if of cluster analysis. The lack of detailed reporting suggests either an ignorance of or a lack of concern for the important parameters of the clustering method used. Failure to provide specific information about the method also tends to inhibit replication and provides little guidance for the other researchers who might seek an appropriate method of cluster analysis. Use of specific program names rather than the more general algorithm name impedes inter-study comparisons. This situation suggests a need for a sound review of clustering methodology for the market researcher. Though they mention some empirical work on the characteristics of these measures and algorithms, their report is primarily a catalogue of techniques and some marketing applications. Relatively little guidance is provided to the researcher who is seeking to discover the characteristics and limitations of various grouping procedures.
