Bayes goes big: Distributed MCMC and the drivers of E-commerce conversion

Abstract

This work researches the drivers of e-commerce conversion of one of the largest e-commerce companies in the Netherlands. We focus on product page conversion, i.e., the probability that a customer who visits a specific product page also buys the product. This probability differs between products, which we explain by a variety of factors like pricing, delivery times, reviews, seller type, and the quality of the content. Understanding the drivers of conversion is the first step in increasing it, and therefore an important step in increasing overall sales. We describe the process of transforming Big Data into valuable insights using Bayesian statistics and apply a Bayesian Binomial model to a dataset of 15 million records using a distributed MCMC algorithm. In addition, we apply a simple Binomial GLM on a small sample of these 15 million records for comparison. We find that using the full 15 million records results in a significant reduction in the variance of the estimated parameters. In terms of the actual drivers of e-commerce conversion, we observe that products with competitive pricing, short delivery times, and good content all achieve a higher conversion on average. Furthermore, reviews, average review score, and the number of reviews for a product have a large positive effect on conversion compared to other characteristics. This provides a unique insight into what makes people decide whether or not to purchase a product.