Question

. Describe two business applications where cluster analysis can be applied by business owners and explain each cluster grouping in detail?

Answer 1

Clustering is the process of grouping observations of similar kinds into smaller groups within the larger population. It has widespread application in business analytics. One of the questions facing businesses is how to organize the huge amounts of available data into meaningful structures.Or break a large heterogeneous population into smaller homogeneous groups. Cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise.

When to use clustering?

Clustering is primarily used to perform segmentation, be it customer, product or store. We have already talked about customer segmentation using cluster analysis in the example above. Similarly products can be clustered together into hierarchical groups based on their attributes like use, size, brand, flavor etc; stores with similar characteristics – similar sales, size, customer base etc, can be clustered together.

Clustering can also be used for anomaly detection, for example, identifying fraud transactions. Cluster detection methods can be used on a sample containing only good transactions to determine the shape and size of the “normal” cluster. When a transaction comes along that falls outside the cluster for any reason, it is suspect. This approach has been used in medicine to detect the presence of abnormal cells in tissue samples and in telecommunications to detect calling patterns indicative of fraud.

Clustering is often used to break large set of data into smaller groups that are more amenable to other techniques. For example, logistic regression results can be improved by performing it separately on smaller clusters that behave differently and may follow slightly different distributions.

Business application of clustering

A grocer retailer used clustering to segment its 1.3MM loyalty card customers into 5 different groups based on their buying behavior. It then adopted customized marketing strategies for each of these segments in order to target them more effectively.

One of the groups was called ‘Fresh food lovers’. This comprised of customers who purchase a high proportion of organic food, fresh vegetables, salads etc. A marketing campaign that emphasized the freshness of the fruits and vegetables and year-round availability of organic produce in the stores appealed to this customer group.

Another cluster was called ‘Convenience junkies’. This comprised of people who shopped for cooked/semi-cooked, easy-to prepare meals. A marketing campaign focusing on the retailer’s in-house line of frozen meals as well as the speed of the check-out counters at the store worked well with this audience.

In this way the retailer was able to deliver the right message to the right customer and maximize the effectiveness of its marketing.