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Literature Review

  • Ling LuoEmail author
Chapter
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Part of the Springer Theses book series (Springer Theses)

Abstract

As introduced in Chap.  1, our main research tasks are to understand when customers purchase products, detect customer behaviour patterns, identify different types of customers and compare their responses to factors such as promotions and other marketing strategies. Therefore, we review related work from the following specific areas: 1) temporal modelling of customer behaviour; 2) customer segmentation; 3) factors impacting customer behaviour.

References

  1. 1.
    Chen M-C, Chiu A-L, Chang H-H (2005) Mining changes in customer behavior in retail marketing. Expert Syst Appl 28(4):773–781CrossRefGoogle Scholar
  2. 2.
    Song HS, Kyeong Kim J, Kim SH (2001) Mining the change of customer behavior in an internet shopping mall. Expert Syst Appl 21(3):157–168CrossRefGoogle Scholar
  3. 3.
    Liu B, Hsu W, Han H-S, Xia Y (2000) Mining changes for real-life applications. In: Proceedings of the 2nd international conference on data warehousing and knowledge discovery, vol 1874. Springer, Heidelberg, pp 337–346Google Scholar
  4. 4.
    Huang C-K, Chang T-Y, Narayanan BG (2015) Mining the change of customer behavior in dynamic markets. Inf Technol Manag 16(2):117–138CrossRefGoogle Scholar
  5. 5.
    Wang P, Guo J, Lan Y (2014) Modeling retail transaction data for personalized shopping recommendation. In: Proceedings of the 23rd ACM international conference on information and knowledge management, vol 2662020. ACM, pp 1979–1982Google Scholar
  6. 6.
    Cheung DW, Han J, Ng VT, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the 12th international conference on data engineering. IEEE, pp 106–114Google Scholar
  7. 7.
    Masseglia F, Poncelet P, Teisseire M (2003) Incremental mining of sequential patterns in large databases. Data Knowl Eng 46(1):97–121CrossRefGoogle Scholar
  8. 8.
    Hong T-P, Lin C-W, Yu-Lung W (2008) Incrementally fast updated frequent pattern trees. Expert Syst Appl 34(4):2424–2435CrossRefGoogle Scholar
  9. 9.
    Ding Y, Li X (2005) Time weight collaborative filtering. In: Proceedings of the 14th ACM international conference on information and knowledge management. ACM, pp 485–492Google Scholar
  10. 10.
    Lathia N, Hailes S, Capra L (2009) Temporal collaborative filtering with adaptive neighbourhoods. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. ACM, pp 796–797Google Scholar
  11. 11.
    Xiang L, Yuan Q, Zhao S, Chen L, Zhang X, Yang Q, Sun J (2010) Temporal recommendation on graphs via long- and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, vol 1835896. ACM, pp 723–732Google Scholar
  12. 12.
    Cho YB, Cho YH, Kim SH (2005) Mining changes in customer buying behavior for collaborative recommendations. Expert Syst Appl 28(2):359–369CrossRefGoogle Scholar
  13. 13.
    Koren Y (2010) Collaborative filtering with temporal dynamics. Commun ACM 53(4):89–97CrossRefGoogle Scholar
  14. 14.
    Xiong L, Chen X, Huang T-K, Schneider JG, Carbonell JG (2010) Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: Proceedings of the 2010 SIAM international conference on data mining, vol 10. SIAM, pp 211–222Google Scholar
  15. 15.
    Yin H, Cui B, Chen L, Zhiting H, Zhou X (2015) Dynamic user modeling in social media systems. ACM Trans Inf Syst 33(3):10CrossRefGoogle Scholar
  16. 16.
    Li R, Li B, Jin C, Xue X, Zhu X (2011) Tracking user-preference varying speed in collaborative filtering. In: Proceedings of the 25th AAAI conference on artificial intelligence. AAAI, pp 133–138Google Scholar
  17. 17.
    Ahmed A, Low Y, Aly M, Josifovski V, Smola AJ (2011) Scalable distributed inference of dynamic user interests for behavioral targeting. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 114–122Google Scholar
  18. 18.
    Li B, Zhu X, Li R, Zhang C, Xue X, Wu X (2011) Cross-domain collaborative filtering over time. In: Proceedings of the 22nd international joint conference on artificial intelligence. AAAI Press, pp 2293–2298Google Scholar
  19. 19.
    Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE international conference on data mining. IEEE, pp 263–272Google Scholar
  20. 20.
    Ehrenberg ASC (1959) The pattern of consumer purchases. Appl Stat 26–41Google Scholar
  21. 21.
    Chatfield C, Goodhardt GJ (1973) A consumer purchasing model with Erlang inter-purchase times. J Am Stat Assoc 68(344):828–835Google Scholar
  22. 22.
    Morrison DG, Schmittlein DC (1988) Generalizing the NBD model for customer purchases: what are the implications and is it worth the effort? J Bus Econ Stat 6(2):145–159Google Scholar
  23. 23.
    Couchen W, Chen H-L (2000) Counting your customers: compounding customer’s in-store decisions, interpurchase time and repurchasing behavior. Eur J Oper Res 127(1):109–119zbMATHCrossRefGoogle Scholar
  24. 24.
    Trinh G, Rungie C, Wright M, Driesener C, Dawes J (2014) Predicting future purchases with the Poisson log-normal model. Mark Lett 25(2):219–234CrossRefGoogle Scholar
  25. 25.
    Kim H, Takaya N, Sawada H (2014) Tracking temporal dynamics of purchase decisions via hierarchical time-rescaling model. In: Proceedings of the 23rd ACM international conference on information and knowledge management. ACM, pp 1389–1398Google Scholar
  26. 26.
    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 269–278Google Scholar
  27. 27.
    Pan J, Rao V, Agarwal PK, Gelfand A (2016) Markov-modulated marked poisson processes for check-in data. In: Proceedings of the 33rd international conference on machine learning, pp 2244–2253Google Scholar
  28. 28.
    Teh YW, Blundell C, Elliott L (2011) Modelling genetic variations using fragmentation-coagulation processes. Adv Neural Inf Process Syst 819–827Google Scholar
  29. 29.
    Adams RP, Murray I, MacKay DJC (2009) Tractable nonparametric Bayesian inference in poisson processes with Gaussian process intensities. In: Proceedings of the 26th international conference on machine learning. ACM, pp 9–16Google Scholar
  30. 30.
    Dong D, Kaiser HM (2008) Studying household purchasing and nonpurchasing behaviour for a frequently consumed commodity: two models. Appl Econ 40(15):1941–1951CrossRefGoogle Scholar
  31. 31.
    Taylor A, Wilson F, Hendrie G, Allman-Farinelli M, Noakes M (2015) Feasibility of a healthy trolley index to assess dietary quality of the household food supply. Br J Nutr 114(12):2129–2137CrossRefGoogle Scholar
  32. 32.
    Böttcher M, Spott M, Nauck D, Kruse R (2009) Mining changing customer segments in dynamic markets. Expert Syst Appl 36(1):155–164CrossRefGoogle Scholar
  33. 33.
    Bucklin RE, Gupta S (1992) Brand choice, purchase incidence, and segmentation: an integrated modeling approach. J Mark ResGoogle Scholar
  34. 34.
    Kotler P, Armstrong G (2010) Principles of marketing. Pearson Education, UKGoogle Scholar
  35. 35.
    Bucklin RE, Gupta S, Siddarth S (1998) Determining segmentation in sales response across consumer purchase behaviors. J Mark Res 189–197Google Scholar
  36. 36.
    Allenby GM, Leone RP, Jen L (1999) A dynamic model of purchase timing with application to direct marketing. J Am Stat Assoc 94(446):365–374CrossRefGoogle Scholar
  37. 37.
    Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Mark Sci 27(2):185–204CrossRefGoogle Scholar
  38. 38.
    Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 113–120Google Scholar
  39. 39.
    Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  40. 40.
    Lan D, Buntine W, Jin H, Chen C (2012) Sequential latent dirichlet allocation. Knowl Inf Syst 31(3):475–503CrossRefGoogle Scholar
  41. 41.
    Iwata T, Watanabe S, Yamada T, Ueda N (2009) Topic tracking model for analyzing consumer purchase behavior. In: Proceedings of the 22nd international joint conference on artificial intelligence. AAAI Press, pp 1427–1432Google Scholar
  42. 42.
    Christidis K, Apostolou D, Mentzas G (2010) Exploring customer preferences with probabilistic topic models. In: Proceedings of the joint European conference on machine learning and principles and practice of knowledge discovery in databasesGoogle Scholar
  43. 43.
    Chung J, Kastner K, Dinh L, Goel K, Courville AC, Bengio Y (2015) A recurrent latent variable model for sequential data. In: Advances in neural information processing systems, pp 2980–2988Google Scholar
  44. 44.
    Lloyd C, Gunter T, Nickson T, Osborne MA, Roberts SJ (2016) Latent poisson process allocation. In: Proceedings of the 19th international conference on artificial intelligence and statistics, pp 389–397Google Scholar
  45. 45.
    Ball K, McNaughton SA, Le HND, Gold L, Ni Mhurchu C, Abbott G, Pollard C, Crawford D (2015) Influence of price discounts and skill-building strategies on purchase and consumption of healthy food and beverages: outcomes of the supermarket healthy eating for life randomized controlled trial. Am J Clin Nutr 101(5):1055–1064CrossRefGoogle Scholar
  46. 46.
    Iwata T, Sawada H (2013) Topic model for analyzing purchase data with price information. Data Min Knowl Discov 26(3):559–573zbMATHCrossRefGoogle Scholar
  47. 47.
    Adamopoulos P, Todri V (2015) The effectiveness of marketing strategies in social media: evidence from promotional events. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1641–1650Google Scholar
  48. 48.
    Wan M, Wang D, Goldman M, Taddy M, Rao J, Liu J, Lymberopoulos D, McAuley J (2017) Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs. In: Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 1103–1112Google Scholar
  49. 49.
    Lin Y-C, Huang C-H, Hsieh C-C, Shu Y-C, Chuang K-T (2017) Monetary discount strategies for real-time promotion campaign. In: Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 1123–1132Google Scholar
  50. 50.
    Zhang J, Krishnamurthi L (2004) Customizing promotions in online stores. Mark Sci 23(4):561–578CrossRefGoogle Scholar
  51. 51.
    Zhang J, Wedel M (2009) The effectiveness of customized promotions in online and offline stores. J Mark Res 46(2):190–206CrossRefGoogle Scholar
  52. 52.
    Iwata T, Shah A, Ghahramani Z (2013) Discovering latent influence in online social activities via shared cascade poisson processes. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 266–274Google Scholar
  53. 53.
    Tanaka Y, Kurashima T, Fujiwara Y, Iwata T, Sawada H (2016) Inferring latent triggers of purchases with consideration of social effects and media advertisements. In: Proceedings of the 9th ACM international conference on web search and data mining. ACM, pp 543–552Google Scholar
  54. 54.
    Naik PA, Mantrala MK, Sawyer AG (1998) Planning media schedules in the presence of dynamic advertising quality. Mark Sci 17(3):214–235CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer ScienceThe University of SydneySydneyAustralia

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