Product Recommendation based on Customer Segmentation Engine

Abstract

In today's competitive market, it's critical to comprehend customer behavior and categorize customers according to their demographics and purchasing habits. This is an important part of consumer segmentation since it enables marketers to better customize promotional, marketing, and product development tactics to different audience groups. Customer segmentation is the practice of dividing consumers into groups based on common characteristics, spending habits, and purchasing patterns. Customer segmentation is a technique for determining how to interact with customers in various categories to maximize the value of each client to the company. Customer segmentation can help marketers reach out to each customer in the most effective way possible. Customer segmentation study makes use of a large amount of customer data to properly identify distinct groups of consumers based on behavioural, demographic, and other criteria. K-means clustering is an unsupervised machine learning approach, as opposed to supervised ones. When we have unlabeled data, we employ this method. Unlabeled data is information that hasn't been assigned to any of the available categories or groupings. Different techniques are applied in customer segmentation to determine the optimal number of clusters, but each approach has its own drawbacks, such as the DBSCAN algorithm failing in the scenario of changing density clusters. RFM research is based on historical data rather than future projections. The Hierarchical Clustering technique can never erase what has already been done, and therefore necessitates the use of labeled data. Whereas the K-means method ensures convergence, warm-starts the centroid's locations, and quickly adapts to new and form optimal cluster numbers.