Skip to content

Towards early purchase intention prediction in online session based retailing systems

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalElectronic Markets
Publication statusAccepted for publication - 6 Nov 2020


  • Electronic_Marketing__Ramazan_Esmeli

    Rights statement: The embargo end date of 2050 is a temporary measure until we know the publication date. Once we know the publication date the full text of this article will be able to view shortly afterwards.

    Accepted author manuscript (Post-print), 350 KB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 1/01/50

Related information

Relations Get citation (various referencing formats)

ID: 23395645