Buy when? Survival machine learning model comparison for purchase timing
Diego Vallarino
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.