电子商务决策支持中客户流失预测的深度学习

IF 7.4 3区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Maciej Pondel, Maciej Wuczynski, W. Gryncewicz, Lukasz Lysik, Marcin Hernes, Artur Rot, Agata Kozina
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引用次数: 5

摘要

流失预测是一个大数据领域,也是最近最苛刻的用例之一。无论销售规模或渠道如何,这也是衡量企业健康成长的最关键指标之一。本文旨在开发电子商务中客户流失预测的深度学习模型,这是本文的主要贡献。该实验是在真实的电子商务数据上进行的,其中75%的买家是一次性客户。基于这种业务特殊性(许多一次性客户和很少的常规客户)的预测是极具挑战性的,并且在一定程度上必然是不准确的。从另一个角度来看,正确的预测和后续行动导致更高的客户保留率对整体业务绩效非常有吸引力。在这种情况下,准确率为74%,准确率为78%,召回率为68%的预测是非常有希望的。此外,本文填补了研究空白,并通过使用基于客户流失和每个客户交易的完整历史的深度学习工具,为零售业开发客户流失预测方法的现有文献做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning for Customer Churn Prediction in E-Commerce Decision Support
Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.
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来源期刊
Business & Information Systems Engineering
Business & Information Systems Engineering Computer Science-Information Systems
CiteScore
13.60
自引率
7.60%
发文量
44
审稿时长
3 months
期刊介绍: Business & Information Systems Engineering (BISE) is a double-blind peer-reviewed journal with a primary focus on the design and utilization of information systems for social welfare. The journal aims to contribute to the understanding and advancement of information systems in ways that benefit societal well-being.
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