Skip to main content
SpringerLink
Log in

Predicting customer churn for platform businesses: using latent variables of variational autoencoder as consumers’ purchasing behavior

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

A Correction to this article was published on 12 August 2022

This article has been updated

Abstract

Customer churn is considered a critical issue for all businesses, as customer loss leads to a decrease in future profits. Although acquiring new customers can address such losses, this process tends to cost more than retaining existing customers. Therefore, identifying potential churners and then retaining them is important. While churn prediction has been studied widely, current research must analyze more complex business models in response to their increase, such as platform businesses. However, modeling churn prediction for these businesses is challenging because consumer behavior over platforms is more complicated. Past approaches to churn prediction can be improved upon using recent advancements in deep learning that capture the nonlinear relationships behind the data in a data-driven manner, especially for complex business models. This study proposes a method of extracting latent features from purchase histories as explanatory variables for churn prediction using a variational autoencoder with the actual customer distribution as a prior. The proposed method is validated using real purchase data from a platform business and shows a 1.5% improvement in F-measure against the baseline, and a 20% improvement for customers with recent transactions. Subsequently, the variables are examined using several methods for data analysis to interpret the meanings of the extracted features and underlying customers’ purchasing behavior in the group of potential churners. With these analyses, the model provides practical implications for understanding what kind of purchasing behavior may lead to churn and planning effective retention strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

Notes

  1. https://github.com/darleybarreto/vae-pytorch, https://github.com/altosaar/gamma-variational-autoencoder

References

  1. Ngai EW, Xiu L, Chau DC (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2):2592–2602

    Article  Google Scholar 

  2. Bhattacharya CB (1998) When customers are members: customer retention in paid membership contexts. J Acad Mark Sci 26(1):31–44

    Article  Google Scholar 

  3. Buckinx W, Van den Poel D (2005) Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur J Oper Res 164(1):252–268

    Article  MATH  Google Scholar 

  4. Grbovic M, Radosavljevic V, Djuric N, Bhamidipati N, Savla J, Bhagwan V, Sharp D (2015) E-commerce in your inbox: product recommendations at scale. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1809–1818

  5. Shapoval K, Setzer T (2018) Next-purchase prediction using projections of discounted purchasing sequences. Bus Inf Syst Eng 60(2):151–166

    Article  Google Scholar 

  6. Fader PS, Hardie BG, Lee KL (2005) RFM and CLV: using iso-value curves for customer base analysis. J Mark Res 42(4):415–430

    Article  Google Scholar 

  7. Hasumoto K, Kumoi G, Goto M (2019) A prediction of customer lifetime value in a platform business using nonnegative matrix factorization. J Inf Process 60(7):1283–1293

    Google Scholar 

  8. Khajvand M, Zolfaghar K, Ashoori S, Alizadeh S (2011) Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study. Procedia Comput Sci 3:57–63

    Article  Google Scholar 

  9. Voigt S, Hinz O (2016) Making digital freemium business models a success: predicting customers’ lifetime value via initial purchase information. Bus Inf Syst Eng 58(2):107–118

    Article  Google Scholar 

  10. Zhang Y, Bradlow ET, Small DS (2015) Predicting customer value using clumpiness: from RFM to RFMC. Mark Sci 34(2):195–208

    Article  Google Scholar 

  11. Huang B, Kechadi MT, Buckley B (2012) Customer churn prediction in telecommunications. Expert Syst Appl 39(1):1414–1425

    Article  Google Scholar 

  12. Verbeke W, Dejaeger K, Martens D, Hur J, Baesens B (2012) New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur J Oper Res 218(1):211–229

    Article  Google Scholar 

  13. Ahmed M, Afzal H, Siddiqi I, Amjad MF, Khurshid K (2020) Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry. Neural Comput Appl 32(8):3237–3251

    Article  Google Scholar 

  14. Yu R, An X, Jin B, Shi J, Move OA, Liu Y (2018) Particle classification optimization-based BP network for telecommunication customer churn prediction. Neural Comput Appl 29(3):707–720

    Article  Google Scholar 

  15. Xie Y, Li X, Ngai E, Ying W (2009) Customer churn prediction using improved balanced random forests. Expert Syst Appl 36(3):5445–5449

    Article  Google Scholar 

  16. Hopmann J, Thede A (2005) Applicability of Customer Churn Forecasts in a Non-Contractual Setting. In: Baier D, Wernecke KD (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg

    MATH  Google Scholar 

  17. Jahromi AT, Stakhovych S, Ewing M (2014) Managing B2B customer churn, retention and profitability. Ind Mark Manag 43(7):1258–1268

    Article  Google Scholar 

  18. Miguéis VL, Van den Poel D, Camanho AS, Falcãoe Cunha J (2012) Modeling partial customer churn: on the value of first product-category purchase sequences. Expert Syst Appl 39(12):11250–11256

    Article  Google Scholar 

  19. Schmittlein DC, Morrison DG, Colombo R (1987) Counting your customers: who are they and what will they do next? Manag Sci 33(1):1–24

    Article  Google Scholar 

  20. Fader PS, Hardie BG, Lee KL (2005) “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Mark Sci 24(2):275–284

    Article  Google Scholar 

  21. Schweidel DA, Knox G (2013) Incorporating direct marketing activity into latent attrition models. Market Sci 32(3):471–487

    Article  Google Scholar 

  22. Oentaryo RJ, Lim EP, Lo D, Zhu F, Prasetyo PK (2012) Collective churn prediction in social network. In: 2012 IEEE/ACM international conference on advances in social networks analysis and mining, pp. 210–214

  23. Kawale J, Pal A, Srivastava J (2009) Churn prediction in MMORPGs: a social influence based approach. In: 2009 international conference on computational science and engineering, 4: 423–428

  24. Lessmann S, Voß S (2010) Customer-centric decision support. Bus Inf Syst Eng 2(2):79–93

    Article  Google Scholar 

  25. Castanedo F, Valverde G, Zaratiegui J, Vazquez A (2014) Using deep learning to predict customer churn in a mobile telecommunication networks. http://www.wiseathena.com/pdf/wa_dl.pdf. Accessed 13 Feb 2020

  26. Spanoudes P, Nguyen T (2017) Deep learning in customer churn prediction: unsupervised feature learning on abstract company. CoRR. arXiv preprint https://arxiv.org/abs/1703.03869

  27. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  28. Kingma DP, Welling M (2013) Auto-encoding variational bayes. stat. arXiv preprint https://arxiv.org/abs/1312.6114

  29. Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. stat. arXiv preprint https://arxiv.org/abs/1401.4082

  30. Van Alstyne MW, Parker GG, Choudary SP (2016) Pipelines, platforms, and the new rules of strategy. Harvard Bus Rev 94(4):54–62

    Google Scholar 

  31. Boudreau KJ, Hagiu A (2009) Platform rules: multi-Sided platforms as regulators. In: Gawer A (ed) Platforms, Markets and Innovation. Edward Elgar, London, pp 163–191

    Google Scholar 

  32. Eisenmann T, Parker GG, Van Alstyne MW (2006) Strategies for two-sided markets. Harvard Bus Rev 84(10):92

    Google Scholar 

  33. Ascarza E, Netzer O, Hardie BG (2018) Some customers would rather leave without saying goodbye. Market Sci 37(1):54–77

    Article  Google Scholar 

  34. Kim S, Choi D, Lee E, Rhee W (2017) Churn prediction of mobile and online casual games using play log data. Plos One 12(7):e01080735

    Google Scholar 

  35. Wangperawong A, Brun C, Laudy O, Pavasuthipaisit R (2016) Churn analysis using deep convolutional neural networks and autoencoders. stat. arXiv preprint https://arxiv.org/abs/1604.05377

  36. Miguéis VL, Van den Poel D, Camanho AS, Falcãoe Cunha J (2012) Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. Adv Data Anal Classif 6(4):337–353

    Article  MathSciNet  MATH  Google Scholar 

  37. Martínez A, Schmuck C, Pereverzyev S Jr, Pirker C, Haltmeier M (2020) A machine learning framework for customer purchase prediction in the non-contractual setting. Eur J Oper Res 281(3):588–596

    Article  Google Scholar 

  38. Verbeke W, Martens D, Mues C, Baesens B (2011) Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst Appl 38(3):2354–2364

    Article  Google Scholar 

  39. Ascarza E, Neslin SA, Netzer O, Anderson Z, Fader PS, Gupta S, Hardie BG, Lemmens A, Libai B, Neal D, Provost F, Schrift R (2018) In Pursuit of enhanced customer retention management: review, Key Issues, and Future Directions. Cust Need Solut 5:65–81

    Article  Google Scholar 

  40. Lemmens A, Croux C (2006) Bagging and boosting classification trees to predict churn. J Mark Res 43(2):276–286

    Article  Google Scholar 

  41. Chen K, Hu YH, Hsieh YC (2015) Predicting customer churn from valuable B2B customers in the logistics industry: a case study. Inf Syst E-bus Manage 13(3):475–494

    Article  Google Scholar 

  42. Jahromi AT, Sepehri MM, Teimourpour B, Choobdar S (2010) Modeling customer churn in a non-contractual setting: the case of telecommunications service providers. J Strat Mark 18(7):587–598

    Article  Google Scholar 

  43. Zhuang Y (2018) Research on E-commerce customer churn prediction based on improved value model and XG-Boost algorithm. Manag Sci Eng 12(3):51–56

    Google Scholar 

  44. Gupta S, Lehmann DR (2008) Managing customers as investments: the strategic value of customers in the long run. Pearson, Upper Saddle River, NJ

    Google Scholar 

  45. Razavi A, Oord AVD, Vinyals O (2019) Generating diverse high-fidelity images with VQ-VAE-2. CoRR. arXiv preprint https://arxiv.org/abs/1906.00446

  46. Yu Q, Kavitha M, Kurita T (2021) Extensive framework based on novel convolutional and variational autoencoder based on maximization of mutual information for anomaly detection. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06017-3

    Article  Google Scholar 

  47. Dong C, Xue T, Wang C (2018) The feature representation ability of variational AutoEncoder. In: 2018 IEEE Third international conference on data science in cyberspace, pp. 680–684

  48. Mancisidor RA, Kampffmeyer M, Aas K, Jenssen R (2019) Learning latent representations of bank customers with the variational autoencoder. stat. arXiv preprint https://arxiv.org/abs/1903.06580

  49. Lee W, Song K, Moon IC (2017) Augmented variational autoencoders for collaborative filtering with auxiliary information. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1139–1148

  50. Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 world wide web conference on world wide web, pp. 689–698

  51. Naesseth CA, Ruiz FJR, Linderman SW, Blei DM (2016) Reparameterization gradients through acceptance-rejection sampling algorithms. Stat. arXiv preprint https://arxiv.org/abs/1610.05683

  52. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    Article  MathSciNet  MATH  Google Scholar 

  53. Yu X, Guo S, Guo J, Huang X (2011) An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Syst Appl, 38(3): 1425-1430

    Article  Google Scholar 

Download references

Funding

This work was supported by JSPS KAKENHI Grant Number 21H04600.

Author information

Authors and Affiliations

  1. Institute of Data Science, Waseda University, Tokyo, Japan

    Kyosuke Hasumoto

  2. Faculty of Science and Engineering, Waseda University, Tokyo, Japan

    Masayuki Goto

Authors
  1. Kyosuke Hasumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masayuki Goto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyosuke Hasumoto.

Ethics declarations

Conflicts of interest

The authors declare that we know of no conflicts of interest associated with this publication.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1229 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasumoto, K., Goto, M. Predicting customer churn for platform businesses: using latent variables of variational autoencoder as consumers’ purchasing behavior. Neural Comput & Applic 34, 18525–18541 (2022). https://doi.org/10.1007/s00521-022-07418-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07418-8

Keywords

Search

Navigation