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Content Provider | ACM Digital Library |
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Author | Yeo, Jinyoung Hwang, Seung-won Lipka, Nedim Koh, Eunyee Kim, Sungchul |
Abstract | Generally 2% of shoppers make a purchase on the first visit to an online store while the other 98% enjoys only window-shopping. To bring people back to the store and close the deal, "retargeting" has been a vital online advertising strategy that leads to "conversion" of window-shoppers into buyers. As such retargeting is more effective as a focused tool, in this paper, we study the problem of identifying a conversion rate for a given product and its current customers, 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 level patterns based on the well-studied buying decision process. To evaluate the effectiveness of our method, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs. The evaluation results show that conversion predictions by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment. |
Starting Page | 591 |
Ending Page | 600 |
Page Count | 10 |
File Format | |
ISBN | 9781450346757 |
DOI | 10.1145/3018661.3018715 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2017-02-02 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Retargeting Customer model Sales prediction E-commerce |
Content Type | Text |
Resource Type | Article |
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