Predictive Models for Determining If and When to Display Online Lead Forms

Abstract

This paper will demonstrate a machine learning application for predicting positive lead conversion events on the Edmunds.com website, an American destination for car shopping. A positive conversion event occurs when a user fills out and submits a lead form interstitial. We used machine learning to identify which users might want to fill out lead forms, and where in their sessions to present the interstitials. There are several factors that make these predictions difficult, such as (a) far more negative than positive responses (b) seasonality effects due to car sales events near holidays, which require the model to be easily tunable and (c) the need for computationally fast predictions for real-time decision-making in order to minimize any impact on the website’s usability. Rather than develop a single highly complex model, we used an ensemble of three simple models: Naive Bayes, Markov Chain, and Vowpal Wabbit. The ensemble generated significant lift over random predictions and demonstrated comparable accuracy to an external consulting company’s model.