Conversion Prediction from Clickstream: Modeling Market Prediction and Customer Predictability

Jinyoung Yeo, Seung Won Hwang, Sungchul Kim, Eunyee Koh, Nedim Lipka

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


As 98 percent of shoppers do not make a purchase on the first visit, we study the problem of predicting whether they would come back for a purchase later (i.e., conversion prediction). This problem is important for strategizing 'retargeting', for example, by sending coupons for customers who are likely to convert. For this goal, we study the following two problems, prediction of market and predictability of customer. First, prediction of market aims at identifying a conversion rate for a given product and its customer behavior modeling, which is an important analytics metric for retargeting process. Compared to existing approaches using either of customer or product-level conversion pattern, we propose a joint modeling of both patterns based on the well-studied buying decision process. Second, we can observe customer-specific behaviors after showing retargeting ads, to predict whether this specific customer follows the market model (high predictability) or not (low predictability). For the former, we apply the market model, and for the latter, we propose a new customer-specific prediction based on dynamic ad behavior features. To evaluate the effectiveness of our methods, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs and retargeting campaign logs. The evaluation results show that conversion predictions and predictability by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment.

Original languageEnglish
Article number8554139
Pages (from-to)246-259
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number2
Publication statusPublished - 2020 Feb 1

Bibliographical note

Publisher Copyright:
© 1989-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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