Volume 13, Number 4/5

When Should They Buy? Surviving Machine Learning Models for Purchase Timing

  Authors

Diego Vallarino, Independent Researcher, Spain

  Abstract

Unlocking the value of raw data requires transforming it into knowledge and information that drives decision-making. Algorithms for Machine Learning (ML) are capable of analyzing big datasets and delivering accurate predictions. Using machine learning, market segmentation, customer lifetime value, and marketing approaches have been used. This article explores machine learning approaches for marketing, including Support Vector Machines, Genetic Algorithms, Deep Learning, and K-Means. It is rare for ML to be used to forecast when a person will purchase a product or a basket of goods. The survival models Kernel SVM, DeepSurv, Survival Random Forest, and MTLR are evaluated to predict individual tine-purchase choices in this research. Gender, Revenue, Location, Purchase History, Online Behaviour, Interests, and Promotions According to the research, both discounts and CustomerExperience have an impact on the amount of time spent making a purchase. The research indicates that the DeepSurv model best predicted purchase completion. These insights aid in raising conversion rates for marketers.

  Keywords

Survival Analysis, Machine Learning for survival, marketing analytics.