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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 10-18    DOI: 10.11925/infotech.2096-3467.2018.0823
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Predicting Repeat Purchase Intention of New Consumers
Zhang Liyi(), Li Yiran, Wen Xuan
School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper compares the prediction accuracy and efficiency of different machine learning algorithms, aiming to identify new consumers with repeat purchase intentions. It also provides a theoretical framework for customer classification. [Methods] First, we collected the server logs of a dealer on Taobao.com from 2015 to 2018, as well as its orders and consumers’ personal information. And then, we used different algorithms to train the proposed models. [Results] The SMOTE algorithm combined with the random forest algorithm obtained the highest prediction accuracy of 96%. [Limitations] The sample data size needs to be expanded. [Conclusions] The fusion algorithm based on SMOTE and random forest has better performance in predicting repurchase intentions of new consumers.

Key wordsRepeat Purchase      New Consumers      Intention Prediction      SMOTE      Random Forest     
Received: 26 July 2018      Published: 11 December 2018
ZTFLH:  TP391 G35  

Cite this article:

Zhang Liyi,Li Yiran,Wen Xuan. Predicting Repeat Purchase Intention of New Consumers. Data Analysis and Knowledge Discovery, 2018, 2(11): 10-18.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0823     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I11/10

一级属性 二级属性 属性含义
个人基本信息 性别* {男性; 女性}
地址* {一线城市; 二线城市; 三线城市; 四线城市}
买家信誉* {1心; 2心; 3心; 4心; 5心; 1钻; 2钻; 3钻; 4钻; 5钻; 1冠; 2冠; 3冠; 4冠; 5冠}
卖家信誉* {1心; 2心; 3心; 4心; 5心; 1钻; 2钻; 3钻; 4钻; 5钻; 1冠; 2冠; 3冠; 4冠; 5冠}
是否实名认证* {已认证; 未认证}
是否有头像* {是; 否}
服务难度 异常情况 被其他卖家拦截次数 消费者被其他淘宝卖家拦截的次数
是否是云黑名单成员* {是; 否}
服务难度 是否给过其他卖家中差评* {是; 否}
退款次数 消费者的淘宝历史退款次数
退款率 消费者的淘宝历史退款次数/总成交次数
评价信誉 发出好评率 消费者发出的好评数/发出的评价总数
收到好评率 消费者收到的好评数/收到的评价总数
消费水平 购买能力* 消费者在淘宝的购买能力等级(1-10级)
购买积极性* 消费者在淘宝的购买积极性等级(1-10级)
交易数据(近三个月) 历史交易金额 消费者近三个月在淘宝的交易总金额
历史成交次数 消费者近三个月在淘宝的交易成交次数
历史关闭次数 消费者近三个月在淘宝的订单关闭次数
订单支付率 消费者近三个月在淘宝的支付订单数/总订单数
支付积极性* 消费者近三个月在淘宝的支付积极性等级
平均客单价 消费者近三个月在淘宝的交易总金额/成交次数
近三个月浏览本店次数 消费者近三个月浏览本店的次数
交易行为偏好 交易平台偏好 {手机端; 电脑端; 聚划算}
折扣敏感度* {不敏感; 一般敏感; 比较敏感; 非常敏感}
分类模型 Precision Recall F-Score 训练
时长(s)
Bagging_KNN 0.88 0.90 0.86 0.320
SMOTE-Bagging_KNN 0.87 0.87 0.87 1.254
Decision_tree 0.83 0.82 0.82 0.446
SMOTE-Decision_tree 0.89 0.89 0.89 1.188
RF 0.88 0.90 0.86 2.458
SMOTE-RF 0.96 0.95 0.95 5.614
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