Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Cirqueira, DouglasORCID: 0000-0002-1283-0453, Hofer, Markus, Nedbal, DietmarORCID: 0000-0002-7596-0917, Helfert, MarkusORCID: 0000-0001-6546-6408 and Bezbradica, MarijaORCID: 0000-0001-9366-5113
(2020)
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda.
In: International Workshop on New Frontiers in Mining Complex Patterns, 16 Sept 2019, Würzburg, Germany.
ISBN 978-3-030-48860-4
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online.
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Consumer behavior; Purchase prediction; Behavior analytics; Machine learning; Data mining; E-commerce; Digital retail
8th International Workshop on New Frontiers in Mining Complex Patterns Held in Conjunction with ECML-PKDD. Lecture Notes in Computer Science
11948.
Springer. ISBN 978-3-030-48860-4
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 765395, Science Foundation Ireland grant 13/RC/2094
ID Code:
24462
Deposited On:
15 May 2020 13:38 by
Douglas Da Rocha cirqueira
. Last Modified 28 Mar 2022 10:44