Abstract
Purchase prediction has an important role for decision-makers in e-commerce to improve consumer experience, provide personalised recommendations and increase revenue. Many works investigated purchase prediction for session logs by analysing users’ behaviour to predict purchase intention after a session has ended. In most cases, e-shoppers prefer to be anonymous while browsing the websites and after a session has ended, identifying users and offering discounts can be challenging. Therefore, after a session ends, predicting purchase intention may not be useful for the e-commerce strategists. In this work, we propose and develop an early purchase prediction framework using advanced machine learning models to investigate how early purchase intention in an ongoing session can be predicted. Since users could be anonymous, this could help to give real-time offers and discounts before the session ends. We use dynamically created session features after each interaction in a session, and propose a utility scoring method to evaluate how early machine learning models can predict the probability of purchase intention. The proposed framework is validated with a real-world dataset. Computational experiments show machine learning models can identify purchase intention early with good performance in terms of Area Under Curve (AUC) score which shows success rate of machine learning models on early purchase prediction.
Highlights
The market share of online/e-commerce sales has been rapidly increasing during the last three decades
We use Decision Tree (DT) classifier to conduct the feature importance analysing following the same method in Dutta et al (2019) and Dou (2020) as we found DT classifier is the best performing model among all machine learning (ML) models we analysed for early purchase intention prediction
We discuss our findings based on the questions asked in Section “Introduction”. These are: (1) Given session data after a user’s first interaction, how helpful can ML models be in predicting the likelihood of a purchase in an ongoing session? (2) What is the most critical session feature for early purchase prediction and how can it be identified? (3) How can ML models be evaluated to measure their performance on early purchase prediction? The findings based on the above questions are discussed (Section “Findings”)
Summary
The market share of online/e-commerce sales has been rapidly increasing during the last three decades. A substantial number of these abandoned sessions are due to lack of purchase intention from the consumers, which means that there is almost no chance for conversion; rendering marketing strategies ineffective. For consumers with strong purchase intention, personalised marketing strategies such as targeted discounts, personalised recommendation, targeted adverts and follow-up emails could be very effective. In addition to the possibility of increasing conversion rate, correctly identifying and targeting consumers with strong purchase intention could lead to an increase in sales. Kim et al (2020) developed a framework for real-time purchase behaviour prediction from the users’ (shopper) physical movement in a store environment. They used camera sensors and object detection algorithms to recognise purchase action. Online purchase prediction models are effective, deployed and integrated with the system (Mokryn et al 2019)
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