APPLICATION OF CLUSTERING METHODS IN THE SEGMENTATION OF CLIENTS TO BOOST MARKETING STRATEGIES AND HEIGHTEN CLIENTS’ EXPERIENCE

Clustering just like the word describes is the process of grouping similar objects into clusters. It is an unsupervised machine learning method. It is like classification but in this case, we do not have an already predefined dependent variable that we take as the class. Here, data is grouped according to their similarity and not already existing groups. Clustering algorithms group similar items together in a way that the distance between each item in the cluster are closer to themselves than another cluster.

Some areas of application of clustering methods are Marketing (knowing what customers react in like manner will help tailor a marketing strategy for each cluster), Streaming service (clustering methods can be used to identify the different groups of viewers which in turn can be used for advertisement and the likes), and Retail (grouping of households/individuals on their retail experiences. Information like income, family size, occupation, etc can be used for creating the clusters).

Clustering in data mining is very beneficial in the sense that businesses can investigate data thoroughly and use clustering methods to study the data and then gain insights that will help in the growth of their businesses.

It is highly beneficial for a company to know the types of customers they have. Just like we mentioned earlier, knowing the behavioural pattern of each cluster would help a company tailor goods and services that are particular for each cluster and this in turn would heighten the experience of the client. In this study, we will be using a transactional dataset based on client’s annual spendings on different produces produced by a wholesale distributor to create clusters that will be beneficial to the distributor’s decision-making process and their marketing strategies.

The aim is to apply two clustering methods namely K-Means algorithm and DBSCAN algorithm in creating clusters. The elbow method is used to determine the optimal K for the K-Means algorithm. In this report, outliers are removed for K-Means and not removed for DBSCAN to compare the results. There would be interpretation and visualisation of the results gotten from both methods. Using PCA to determine what variable is the most important.