A deep hybrid learning model for customer repurchase behavior

被引：28

作者：

Kim, Jina ^{[1
]}

Ji, HongGeun ^{[1
]}

Oh, Soyoung ^{[1
]}

Hwang, Syjung ^{[1
]}

Park, Eunil ^{[1
,2
]}

del Pobil, Angel P. ^{[1
,3
]}

机构：

[1] Sungkyunkwan Univ, Seoul 03063, South Korea

[2] Raon Data, Seoul 03076, South Korea

[3] Univ Jaume 1, Castellon de La Plana 12071, Spain

来源：

JOURNAL OF RETAILING AND CONSUMER SERVICES | 2021年 / 59卷

基金：

新加坡国家研究基金会;

关键词：

Deep learning; Smartphone; Customer repurchase; Online review; INTENTION;

D O I：

10.1016/j.jretconser.2020.102381

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Smartphones have become an integral part of our daily lives, which has led to the rapid growth of the smartphone market. As the global smartphone market tends to remain stable, retaining existing customers has become a challenge for smartphone manufacturers. This study investigates whether a deep hybrid learning approach with various customer-oriented types of data can be useful in exploring customer repurchase behavior of same-brand smartphones. Considering data from more than 74,000 customers, the proposed deep learning approach showed a prediction accuracy higher than 90%. Based on the results of deep hybrid learning models, we aim to provide better understanding on customer behavior, such that it could be used as valuable assets for innovating future marketing strategies.

引用

页数：5

共 40 条

[1] Modelling repurchase intention among smartphones users in Nigeria [J].