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Introduction

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Temporal Modelling of Customer Behaviour

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

Customer behaviour analysis has been recognised as an indispensable component of business intelligence and marketing (Sheth et al. in Consumer behavior and beyond. Dryden Press Fort Worth, TX, 1999 [1]). Understanding customer behaviour is of great interest to marketing researchers and business analysts, as this information can help them communicate better with their customers and develop appropriate strategies.

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References

  1. Sheth JN, Mittal B, Newman BI (1999) Consumer behavior and beyond. Dryden Press Fort Worth, TX

    Google Scholar 

  2. Chen MC, Chiu AL, Chang HH (2005) Mining changes in customer behavior in retail marketing. Expert Syst Appl 28(4):773–781

    Article  Google Scholar 

  3. Huang CK, Chang TY, Narayanan BG (2015) Mining the change of customer behavior in dynamic markets. Inf Technol Manage 16(2):117–138

    Article  Google Scholar 

  4. Wang J, Zhang Y (2013) Opportunity model for e-commerce recommendation: right product; right time. In: Proceedings of the 36th ACM conference on research and development in information retrieval. ACM, pp 303–312

    Google Scholar 

  5. Rossi PE, McCulloch RE, Allenby GM (1996) The value of purchase history data in target marketing. Mark Sci 15(4):321–340

    Article  Google Scholar 

  6. Wagner U, Taudes A (1987) Stochastic models of consumer behaviour. Eur J Oper Res 29(1):1–23

    Article  MathSciNet  Google Scholar 

  7. Kopperschmidt K, Stute W (2009) Purchase timing models in marketing: a review. AStA Adv Stat Anal 93(2):123–149

    Article  MathSciNet  Google Scholar 

  8. Luo L, Li B, Koprinska I, Berkovsky S, Chen F (2016) Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling. In: Pacific Asia conference on knowledge discovery and data mining. Springer, pp 527–539

    Google Scholar 

  9. Luo L, Li B, Koprinska I, Berkovsky S, Chen F (2016) Discovering temporal purchase patterns with different responses to promotions. In: Proceedings of the 25th ACM international conference on information and knowledge management. ACM, pp 2197–2202

    Google Scholar 

  10. Luo L, Li B, Berkovsky S, Koprinska I, Chen F (2017) Online engagement for a healthier you: a case study of web-based supermarket health program. In: Proceedings of the 26th international conference on world wide web companion. International world wide web conferences steering committee, pp 1053–1061

    Google Scholar 

  11. Luo L, Li B, Koprinska I, Berkovsky S, Chen F (2017) Tracking the evolution of customer purchase behavior segmentation via a fragmentation-coagulation process. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 2414–2420

    Google Scholar 

  12. Li L, Zha H (2015) Energy usage behavior modeling in energy disaggregation via marked Hawkes process. In: Proceedings of the 29th AAAI conference on artificial intelligence, pp 672–678

    Google Scholar 

  13. Ferraz Costa A, Yamaguchi Y, Juci Machado Traina A, Traina Jr C, Faloutsos C (2015) RSC: Mining and modeling temporal activity in social media. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 269–278

    Google Scholar 

  14. Baker RS, Yacef K (2009) The state of educational data mining in 2009: a review and future visions. J Educ Data Min 1(1):3–17

    Google Scholar 

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Correspondence to Ling Luo .

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Luo, L. (2020). Introduction. In: Temporal Modelling of Customer Behaviour. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-18289-2_1

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