Implementing the Data science Module for Predicting Customer Purchase Propensity and Segmenting Premium Customers

Abstract

In today's data-driven business environment, it is essential to understand customer purchase behaviour to gain competitive advantage. By predicting a customer's likelihood of making a purchase, businesses can use marketing resources more effectively, customize offers, and boost revenue. To evaluate consumer data and predict purchase propensity, this study uses Exploratory Data Analytics (EDA), a data science pipeline. The ability to understand customer behaviour has become an essential skill in a highly competitive market. Using predictive analytics to predict whether a customer will buy will allow businesses to create focused marketing efforts, allocate resources efficiently, and increase return on investment. In this study, a methodical approach is offered to customer data analysis, which includes cleaning and prepping the data, using Exploratory Data Analytics Artificial Intelligence to anticipate purchases, and finally identifying high-value segments for premium offers. In this study, we selected a dataset of 500 customer records with attributes such as age, income, city, marital status, behavioural score, and purchase behaviour, and applied multiple machine learning models to it. Despite weakly predictive features, the results demonstrate the potential of integrating data-driven approaches into marketing decision-making while offering valuable insights into customer behaviour. A study shows how machine learning can be integrated into marketing workflows, by not only predicting but also segmenting and designing targeted campaigns.