Buy when? Survival machine learning model comparison for purchase timing

TL;DR

This paper compares survival machine learning models to predict the timing of consumer purchases, highlighting DeepSurv's superior performance and its implications for marketing strategies.

Contribution

It introduces and evaluates survival models like Kernel SVM, DeepSurv, Survival Random Forest, and MTLR for predicting purchase timing, a less-explored area in marketing ML.

Findings

DeepSurv outperformed other models in predicting purchase timing.

Customer demographics and behavior significantly influence purchase timing.

Insights can help marketers optimize timing strategies for increased conversions.

Abstract

The value of raw data is unlocked by converting it into information and knowledge that drives decision-making. Machine Learning (ML) algorithms are capable of analysing large datasets and making accurate predictions. Market segmentation, client lifetime value, and marketing techniques have all made use of machine learning. This article examines marketing machine learning techniques such as Support Vector Machines, Genetic Algorithms, Deep Learning, and K-Means. ML is used to analyse consumer behaviour, propose items, and make other customer choices about whether or not to purchase a product or service, but it is seldom used to predict when a person will buy a product or a basket of products. In this paper, the survival models Kernel SVM, DeepSurv, Survival Random Forest, and MTLR are examined to predict tine-purchase individual decisions. Gender, Income, Location, PurchaseHistory, OnlineBehavior, Interests, PromotionsDiscounts and CustomerExperience all have an influence on purchasing time, according to the analysis. The study shows that the DeepSurv model predicted purchase completion the best. These insights assist marketers in increasing conversion rates.