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首页> 外文期刊>International Journal Information Theories and Applications >Decision Tree Analysis to Improve e-mail Marketing Campaigns
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Decision Tree Analysis to Improve e-mail Marketing Campaigns

机译：决策树分析可改善电子邮件营销活动

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摘要

The efficiency of e-mail campaigns is a big challenge for any ecommerceventure in terms of the response rate of e-mail campaigns andcustomer segmentation based on loyalty. Decision tree analysis are usefultools to extract customer information related to response rate from e-mailcampaigns data. This study aims at predicting customer loyalty and improvingthe response rate of e-mail campaigns, specifically open rate and click throughrate, using decision tree analysis such as CHAID , CART and QUIST.The methodology used in this study is Cross Industry Standard Process forData Mining (CRISP – DM) methodology. The models are trained and testedusing split sample validation. Furthermore, we used a classification measures tocalculate the accuracy, precision, recall and F1 to evaluate the models. Themodels reported satisfactory results in predicting customer loyalty based onopen rate, click through rate values and on customer demographic variables.The response rates also increase at the preferred moment at which e-mailsshould be send to customers in email campaigns.
机译：就电子邮件活动的响应率和基于忠诚度的客户细分而言，电子邮件活动的效率对任何电子商务企业都是一个巨大的挑战。决策树分析是从电子邮件广告系列数据中提取与响应率相关的客户信息的有用工具。本研究旨在使用CHAID，CART和QUIST等决策树分析来预测客户忠诚度并提高电子邮件活动的响应率，特别是打开率和点击率。本研究中使用的方法是跨行业数据挖掘标准流程CRISP – DM）方法。使用拆分样本验证对模型进行训练和测试。此外，我们使用分类措施来计算准确性，准确性，召回率和F1来评估模型。这些模型在基于开放率，点击率值和客户人口统计变量预测客户忠诚度方面报告了令人满意的结果。在电子邮件活动中将电子邮件发送给客户的首选时间，响应率也会提高。

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