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research-article

Leveraging fine-grained transaction data for customer life event predictions

Authors:

Arno De Caigny,

Kristof Coussement,

Koen W. De BockAuthors Info & Claims

Volume 130, Issue C

https://doi.org/10.1016/j.dss.2019.113232

Published: 01 March 2020 Publication History

Abstract

This real-world study with a large European financial services provider combines aggregated customer data including customer demographics, behavior and contact with the firm, with fine-grained transaction data to predict four different customer life events: moving, birth of a child, new relationship, and end of a relationship. The fine-grained transaction data—approximately 60 million debit transactions involving around 132,000 customers to >1.5 million different counterparties over a one-year period—reveal a pseudo-social network that supports the derivation of behavioral similarity measures. To advance decision support systems literature, this study validates the proposed customer life event prediction model in a real-world setting in the financial services industry; compares models that rely on aggregated data, fine-grained transaction data, and their combination; and extends existing methods to incorporate fine-grained data that preserve recency, frequency, and monetary value information of the transactions. The results show that the proposed model predicts life events significantly better than random guessing, especially with the combination of fine-grained transaction and aggregated data. Incorporating recency, frequency, and monetary value information of fine-grained transaction data also significantly improves performance compared with models based on binary logs. Fine-grained transaction data accounts for the largest part of the total variable importance, for all but one of the life events.

Highlights

•

We predict four different customer life events using customer data.

•

The added value of fine-grained transaction data is explored.

•

To leverage transaction data, we extend a method based on pseudo-social networks.

•

Results demonstrate the feasibility of life event prediction.

•

The added value of our methodological extension is demonstrated.

References

[1]

W. Reinartz, V. Kumar, The mismanagement of customer loyalty, Harvard Business Review 80 (2002) 86–87.

[2]

W. Reinartz, M. Krafft, W.D. Hoyer, The CRM process: its measurement and impact on performance, Journal of Marketing Research 1 (2004) 293–305.

[3]

W. Wells, G. Gubar, Life cycle concept in marketing research, J. Mark. Res. III (1966) 355–363. http://www.jstor.org/stable/3149851.

[4]

M.C. Gilly, B.M. Enis, Recycling the family life cycle: a proposal for redefinition, Advances in Consumer Research 9 (1982) 271–276.

[5]

P.C. Verhoef, B. Donkers, Predicting customer potential value an application in the insurance industry, Decision Support Systems (2001),.

[6]

B. Wheaton, Life transitions, role histories, and mental health, American Sociological Review 55 (1990) 209.

[7]

E. Lee, G.P. Moschis, A. Mathur, A study of life events and changes in patronage preferences, Journal of Business Research 54 (2001) 25–38.

[8]

A. Mathur, G.P. Moschis, E. Lee, A longitudinal study of the effects of life status changes on changes in consumer preferences, Journal of the Academy of Marketing Science 36 (2008) 234–246.

[9]

M. Solomon, G. Bamossy, S. Askegaard, M. Hogg, Consumer Behaviour: A European Perspective, Fourth edition, 2009.

[10]

E.C. Malthouse, Mining for trigger events with survival analysis, Data Min. Knowl. Discov. 15 (2007) 383–402.

[11]

J. Li, A. Ritter, C. Cardie, E. Hovy, Major life event extraction from Twitter based on congratulations/condolences speech acts, Proc. 2014 Conf. Empir. Methods Nat. Lang. Process. (EMNLP 2014), 2014, pp. 1997–2007.

[12]

A. Sen, A.P. Sinha, IT alignment strategies for customer relationship management, Decision Support Systems (2011),.

Digital Library

[13]

P.C. Verhoef, Understanding the effect of customer relationship management efforts on customer retention and customer share development, Journal of Marketing 67 (2003) 30–45.

[14]

J. Peppard, Customer relationship management (CRM) in financial services, European Management Journal 18 (2000) 312–327.

[15]

C. Lehrer, A. Wieneke, J. vom Brocke, R. Jung, S. Seidel, How big data analytics enables service innovation: materiality, affordance, and the individualization of service, Journal of Management Information Systems 35 (2018) 424–460.

[16]

K. Coussement, S. Lessmann, G. Verstraeten, A comparative analysis of data preparation algorithms for customer churn prediction: a case study in the telecommunication industry, Decision Support Systems 95 (2017) 27–36.

Digital Library

[17]

E.W.T. Ngai, L. Xiu, D.C.K. Chau, Application of data mining techniques in customer relationship management: a literature review and classification, Expert Systems with Applications 36 (2009) 2592–2602.

[18]

W. Verbeke, K. Dejaeger, D. Martens, J. Hur, B. Baesens, New insights into churn prediction in the telecommunication sector: a profit driven data mining approach, European Journal of Operational Research 218 (2012) 211–229.

[19]

S. Lessmann, B. Baesens, H.-V. Seow, L.C. Thomas, Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research, European Journal of Operational Research 247 (2015) 124–136.

[20]

K. Coussement, P. Harrigan, D.F. Benoit, Improving direct mail targeting through customer response modeling, Expert Systems with Applications 42 (2015) 8403–8412.

[21]

J. Ganesh, M.J. Arnold, K.E. Reynolds, Understanding the customer base of service providers: an examination of the differences between switchers and stayers, Journal of Marketing 64 (2000) 65–87.

[22]

D. Van den Poel, B. Larivière, Customer attrition analysis for financial services using proportional hazard models, European Journal of Operational Research 157 (2004) 196–217.

[23]

D. Martens, F. Provost, J. Clark, E. Junqué De Fortuny, Mining massive fine-grained behavior data to improve predictive analytics, MIS Quarterly 40 (2016) 869–888.

[24]

A. Knott, A. Hayes, S.A. Neslin, Next-product-to-buy models for cross-selling applications, Journal of Interactive Marketing 16 (2002) 59–75.

[25]

M.J.A. Berry, G.S. Linoff, Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, Second edition, 2004.

[26]

M. Khodabakhsh, M. Kahani, E. Bagheri, Z. Noorian, Detecting life events from twitter based on temporal semantic features, Knowledge-Based Syst. 148 (2018) 1–16.

[27]

M. Kosinski, D. Stillwell, T. Graepel, Private traits and attributes are predictable from digital records of human behavior, Proceedings of the National Academy of Sciences 110 (2013) 5802–5805. (doi:10).

[28]

P.S. Fader, B.G.S. Hardie, K.L. Lee, RFM and CLV: using iso-value curves for customer base analysis, Journal of Marketing Research 42 (2005) 415–430.

[29]

P.C. Verhoef, P.N. Spring, J.C. Hoekstra, P.S.H. Leeflang, The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands, Decision Support Systems (2002),.

Digital Library

[30]

D.L. Olson, B. Chae, Direct marketing decision support through predictive customer response modeling, Decision Support Systems (2012),.

Digital Library

[31]

T. Verbraken, F. Goethals, W. Verbeke, B. Baesens, Predicting online channel acceptance with social network data, Decision Support Systems 63 (2014) 104–114,.

[32]

A. Barfar, B. Padmanabhan, A. Hevner, Applying behavioral economics in predictive analytics for B2B churn: findings from service quality data, Decision Support Systems (2017),.

Digital Library

[33]

S. Moro, P. Cortez, P. Rita, A data-driven approach to predict the success of bank telemarketing, Decision Support Systems (2014),.

[34]

A. McAfee, E. Brynjolfsson, Big data. The management revolution, Harv. Bus. Rev. 90 (2012) 61–68.

[35]

S. Lian, Y. Xu, C. Zhang, Family profile mining in retailing, Decision Support Systems (2019),.

Digital Library

[36]

K. Coussement, D. Van den Poel, Improving customer complaint management by automatic email classification using linguistic style features as predictors, Decision Support Systems 44 (2008) 870–882,.

Digital Library

[37]

A. De Caigny, K. Coussement, K.W. De Bock, S. Lessmann, Incorporating textual information in customer churn prediction models based on a convolutional neural network, International Journal of Forecasting (2019),.

[38]

B. Baesens, V. Van Vlasselaer, W. Verbeke, Social network analysis for fraud detection, in: Fraud Anal. Using Descr. Predict. Soc. Netw. Tech., 2015,.

[39]

J. Moeyersoms, D. Martens, Including high-cardinality attributes in predictive models: a case study in churn prediction in the energy sector, Decision Support Systems 72 (2015) 72–81,.

Digital Library

[40]

K. Coussement, S. Lessmann, G. Verstraeten, A comparative analysis of data preparation algorithms for customer churn prediction: a case study in the telecommunication industry, Decision Support Systems 95 (2016) 27–36.

Digital Library

[41]

M. Meire, M. Ballings, D. Van den Poel, The added value of social media data in B2B customer acquisition systems: a real-life experiment, Decision Support Systems (2017),.

Digital Library

[42]

De Cnudde, S.; Martens, D.; Evgeniou, T.; Provost, F. (2017): A benchmarking study of classification techniques for behavioral data. https://ideas.repec.org/p/ant/wpaper/2017005.html.

[43]

D. Martens, F. Provost, Pseudo-social network targeting from consumer transaction data, New York Univ. - Stern Sch. Bus. Work. Pap. CeDER-11-05, 2011, pp. 1–31.

[44]

K. Coussement, F.A.M.M. Van den Bossche, K.W. De Bock, Data accuracy’s impact on segmentation performance: benchmarking RFM analysis, logistic regression, and decision trees, Journal of Business Research 67 (2014) 2751–2758.

[45]

A. De Caigny, K. Coussement, K.W. De Bock, A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees, European Journal of Operational Research 269 (2018) 760–772. http://www.sciencedirect.com/science/article/pii/S0377221718301243.

[46]

E. Ascarza, P.S. Fader, B.G.S. Hardie, Marketing models for the customer-centric firm, Int. Ser. Oper. Res. Manag. Sci. 1 (2017) 297–329.

[47]

W. Buckinx, D. Van Den Poel, Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting, European Journal of Operational Research 164 (2005) 252–268.

[48]

D.E. Jennings, Outliers and residual distributions in logistic regression, Journal of the American Statistical Association 81 (1986) 987–990.

[49]

J. Freeman, D. Anderson, D. Sweeney, T. Williams, E. Shoesmith, Statistics for Business and Economics, 2nd ed., Cengage, 2010.

[50]

D. Ghosh, A. Vogt, Outliers: an evaluation of methodologies, Jt. Stat. Meet. (2012) 3455–3460.

[51]

D. Pyle, S. Editor, D.D. Cerra, Data Preparation for Data Mining, Morgan Kaufmann Publishers, San Francisco, 1999.

[52]

G.M. Weiss, Mining with rarity: a unifying framework, SIGKDD Explor 6 (2004) 7–19.

[53]

K.W. De Bock, D. Van den Poel, Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models, Expert Systems with Applications 39 (2012) 6816–6826.

[54]

C.X. Ling, C. Li, Data mining for direct marketing: problems and solutions, Proc. 4th Int. Conf. Knowl. Discov. Data Min, 1998, pp. 73–79.

[55]

R.E. Bucklin, S. Gupta, Brand choice, purchase incidence, and segmentation: an integrated modeling approach, Journal of Marketing Research 29 (1992) 201–215.

[56]

S.A. Neslin, S. Gupta, W. Kamakura, J. Lu, C.H. Mason, Defection detection: measuring and understanding the predictive accuracy of customer churn models, J. Mark. Res. XLIII (2006) 204–211.

[57]

D.W.J. Hosmer, S. Lemeshow, Applied Logistic Regression, 2nd ed., John Wiley, New York, 2000.

[58]

A. Lemmens, C. Croux, Bagging and boosting classification trees to predict churn, Journal of Marketing Research 43 (2006) 276–286.

[59]

A.J. Hanley, J.B. McNeil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology 143 (1982) 29–36.

[60]

J. Demsar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research 7 (2006) 1–30.

Digital Library

[61]

F. Wilcoxon, Individual comparisons by ranking methods, Biometrics Bulletin 1 (1945) 80.

[62]

K. Coussement, D. Van den Poel, Integrating the voice of customers through call center emails into a decision support system for churn prediction, Inf. {&} Manag. 45 (2008) 164–174.

[63]

F. Devriendt, D. Moldovan, W. Verbeke, A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: a stepping stone toward the development of prescriptive analytics, Big Data 6 (2018) 13–41. (doi:10).

Cited By

Diaz SCoussement KDe Caigny A(2024)Improved decision-making through life event predictionDecision Support Systems10.1016/j.dss.2024.114342187:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.dss.2024.114342
Coussement KAbedin MKraus MMaldonado STopuz K(2024)Explainable AI for enhanced decision-makingDecision Support Systems10.1016/j.dss.2024.114276184:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.dss.2024.114276
Black SDavern MMaynard SNasser H(2023)Data governance and the secondary use of dataInformation and Organization10.1016/j.infoandorg.2023.10044733:2Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.infoandorg.2023.100447
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Leveraging fine-grained transaction data for customer life event predictions

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Information & Contributors

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Published In

cover image Decision Support Systems

Decision Support Systems Volume 130, Issue C

Mar 2020

119 pages

ISSN:0167-9236

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2020

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Cited By

Diaz SCoussement KDe Caigny A(2024)Improved decision-making through life event predictionDecision Support Systems10.1016/j.dss.2024.114342187:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.dss.2024.114342
Coussement KAbedin MKraus MMaldonado STopuz K(2024)Explainable AI for enhanced decision-makingDecision Support Systems10.1016/j.dss.2024.114276184:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.dss.2024.114276
Black SDavern MMaynard SNasser H(2023)Data governance and the secondary use of dataInformation and Organization10.1016/j.infoandorg.2023.10044733:2Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.infoandorg.2023.100447
Coussement KBenoit D(2022)Interpretable data science for decision makingDecision Support Systems10.1016/j.dss.2021.113664150:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.dss.2021.113664
Zhao L(2021)Event Prediction in the Big Data EraACM Computing Surveys10.1145/345028754:5(1-37)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3450287

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