short-paper

Statistic-based CRM approach via time series segmenting RFM on large scale data

Authors:

Zhonghong OuAuthors Info & Claims

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

Pages 282 - 291

https://doi.org/10.1145/2996890.3007873

Published: 06 December 2016 Publication History

Abstract

CRM (Customer relationship management) plays a crucial role in detecting and gathering valuable users from externality and internality, where externality refers to customer relationship and internality is regarded as customer characteristics. However, conventional approaches based on RFM (recency, frequency and monetary) model have encountered with three challenges. First, traditional approaches that derive from survey data rather than objective large-scale data fail to apply the method in general scenario; Second, since there is several trial to experiment on RFM model changing over time, different segmentation of time leads to different results; Last, analysis of multiple characteristics either on externality or internality is sparse and separate, which betray the exploration purpose for CRM and make results unconvincing. To overcome the three limitations, a multiple statistic-based approach to value users via time series segmenting time interval of RFM on large scale data is proposed in the paper. In the aspect of telecom service data, we experiment on segmenting time interval methodologically for RFM model on data set more than millions of users. Besides, the most significant part there is formal mechanism to apply MCA (multiple corresponding analysis) on multiple characteristics for internality correspondingly with RFM for externality, leading to the deep relationships of users and their characteristics. Subsequently, we improve the traditional RFM model overtime from the different clustering steps on large-scale data.

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Cited By

Ebadi Jalal MElmaghraby A(2024)Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual AnalysisJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1903008119:3(1660-1681)Online publication date: 27-Jun-2024
https://doi.org/10.3390/jtaer19030081
Martínez RCarrasco RSanchez-Figueroa CGavilan D(2021)An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail BusinessMathematics10.3390/math91618369:16(1836)Online publication date: 4-Aug-2021
https://doi.org/10.3390/math9161836
Abbasimehr HBahrini A(2021)An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentationExpert Systems with Applications10.1016/j.eswa.2021.116373(116373)Online publication date: Dec-2021
https://doi.org/10.1016/j.eswa.2021.116373
Show More Cited By

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cover image ACM Other conferences

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

December 2016

549 pages

ISBN:9781450346160

DOI:10.1145/2996890

General Chairs:
Changjun Jiang
Tongji University, China
,
Omer Rana
Cardiff University, UK
,
Nick Antonopoulos
University of Derby, UK

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 December 2016

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UCC '16

UCC '16: 9th International Conference on Utility and Cloud Computing

December 6 - 9, 2016

Shanghai, China

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Cited By

Ebadi Jalal MElmaghraby A(2024)Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual AnalysisJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1903008119:3(1660-1681)Online publication date: 27-Jun-2024
https://doi.org/10.3390/jtaer19030081
Martínez RCarrasco RSanchez-Figueroa CGavilan D(2021)An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail BusinessMathematics10.3390/math91618369:16(1836)Online publication date: 4-Aug-2021
https://doi.org/10.3390/math9161836
Abbasimehr HBahrini A(2021)An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentationExpert Systems with Applications10.1016/j.eswa.2021.116373(116373)Online publication date: Dec-2021
https://doi.org/10.1016/j.eswa.2021.116373
Luo BLin S(2020)Research on The Anonymous Customer Segmentation Model of Telecom2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)10.1109/ITOEC49072.2020.9141572(1026-1031)Online publication date: Jun-2020
https://doi.org/10.1109/ITOEC49072.2020.9141572

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