Online Prediction of Aggregated Retailer Consumer Behaviour

Abstract

Predicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.

Customer Behaviour Analysis for Evaluating Buying Patterns Using Svm, Logistic Regression and KNN

“…Additionally, a variety of machine learning models are built, and the accuracy of the best classifier among these is improved even more in order to accurately categorize clients into various product categories. Finally, the system generates segmented customer clusters, which become helpful in understanding the behaviour of customers, allowing them to target customers effectively and increase profitability.The goal of this research work is to create a system that is able to group consumers according to how they often buy across various product categories [1] [5]. Companies can better serve their consumers by gaining more accurate and insightful information about consumer behaviour.…”