Marketing analytics: Methods, practice, implementation, and links to other fields

被引:49
作者
France, Stephen L. [1 ]
Ghose, Sanjoy [2 ]
机构
[1] Mississippi State Univ, Coll Business, 114 McCool Hall,40 Old Main,POB 5288, Mississippi State, MS 39762 USA
[2] Univ Wisconsin, Sheldon B Lubar Sch Business, POB 742,3202 N Maryland Ave, Milwaukee, WI 53201 USA
关键词
Analytics; Prediction; Marketing; Visualization; Segmentation; Data mining; DECISION-SUPPORT-SYSTEMS; CUSTOMER LIFETIME VALUE; K-MEANS; PARALLEL ALGORITHMS; CLUSTER-ANALYSIS; R-PACKAGE; INDIVIDUAL-DIFFERENCES; PSYCHOMETRIC METHODS; CONJOINT-ANALYSIS; THRESHOLD-MODEL;
D O I
10.1016/j.eswa.2018.11.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, expert systems, statistics, and operations research. This paper provides an integrative review at the boundary of these areas. The aim is to give researchers in the intelligent and expert systems community the opportunity to gain a broad view of the marketing analytics area and provide a starting point for future interdisciplinary collaboration. The topics of visualization, segmentation, and class prediction are featured. Links between the disciplines are emphasized. For each of these topics, a historical overview is given, starting with initial work in the 1960s and carrying through to the present day. Recent innovations for modern, large, and complex "big data" sets are described. Practical implementation advice is given, along with a directory of open source R routines for implementing marketing analytics techniques. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:456 / 475
页数:20
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