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The influence of sales areas and bargain sales on customer behavior in a grocery store

  • Advances in Intelligent Data Processing and Analysis
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Abstract

Developments in radio frequency identification (RFID) technology have resulted in the availability of data on customers’ movement paths in various stores. In this paper, we propose a customer behavior model in a grocery store by using RFID and point-of-sales data. This model is based on a nonhomogeneous hidden Markov model with covariates and estimates “Stop” and “Pass by” behaviors. The model introduces sales areas and the number of bargain products as covariates and quantifies the effect of these covariates on each behavior. Thus, we can diagnose sales areas and decide the optimal quantity of bargain products. Further, we can rearrange sales areas and reinforce weak sales areas according to the diagnosis results. In addition, information on the optimal quantity of bargain products allows implementation of an effective bargain sales strategy.

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References

  1. Baum LE (1972) An inequality and associated maximization technique in statistical estimation of probabilistic functions of markov processes. Inequalities 3:1–8

    Google Scholar 

  2. Bengio Y, Fransconi P (1995) An input output hmm architecture. Adv Neural Inform Process Syst 7:427–434

    Google Scholar 

  3. Chung H, Walls TA, Park Y (2007) A latent transition model with logistic regression. Psychometrika 72:413–435

    Article  MATH  MathSciNet  Google Scholar 

  4. Dhar R, Huber J, Khan U (2007) The shopping momentum effect. J Market Res 44(3):370–378

    Article  Google Scholar 

  5. Farley JU, Ring LW (1966) A stochastic model of supermarket traffic flow. Oper Res 14:555–567

    Article  Google Scholar 

  6. Gruber A, Weiss Y, Rosen-Zvi M (2007) Hidden topic markov models. In: Proceedings of the conference on artificial intelligence and statistics

  7. Guadagni PM, Little JDC (1983) A logit model of brand choice, calibrated on scanner data. Market Sci 2:203–238

    Article  Google Scholar 

  8. Gupta S (1988) Impact of sales promotions on when, what and how much to buy. J Market Res 25:342–355

    Article  Google Scholar 

  9. Hu P, Huang M, Xu P, Li W, Usadi AK, Zhu X (2011) Generating breakpoint-based timeline overview for news topic retrospection. In: Proceedings of 11th international conference on data mining, pp 260–269

  10. Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Res Res 30:1535–1546

    Article  Google Scholar 

  11. Hui S, Fader P, Bradlow E (2008) The traveling salesman goes shopping: the systematic deviations of grocery paths from tsp optimality. Market Sci 28(3):566–572

    Article  Google Scholar 

  12. Jelinek F (1997) Statistical methods for speech recognition. MIT Press, Cambridge, MA

    Google Scholar 

  13. Kamakura WA, Russell GJ (1989) A probabilistic choice model for market segmentation and elasticity structure. J Market Res 26:379–390

    Article  Google Scholar 

  14. Krogh A, Brown M, Mian IS, Sjölander K, Haussler D (1994) Hidden markov models in computational biology: applications to protein modeling. J Mol Biol 235:1501–1531

    Article  Google Scholar 

  15. Larson JS, Bradlow ET, Fader PS (2005) An exploratory look at super market shopping paths. Int J Res Market 22:395–414

    Article  Google Scholar 

  16. McLachlan GJ, Krishnan T (1997) The EM algorithm and its extensions. Wiley, New Jersey

  17. Montgomery AL, Li S, Srinivasan K, Liechty JC (2004) Predicting online purchase conversion using web path analysis. Market Sci 23:579–595

    Article  Google Scholar 

  18. Netzer O, Lattin JM, Srinivasan V (2008) A hidden markov model of customer relationship dynamics. Market Sci 27:185–204

    Article  Google Scholar 

  19. Sano N, Yada K (2012) The influence of sales areas and bargain sales on customer behavior in the grocery store. In: Proceedings of 16th international conference on knowledge-based and intelligent information and engineering systems, pp 2104–2113

  20. Smith A, Naik PA, Tsai C (2006) Markov-switching model selection using kullback-leibler divergence. J Econom 134:553–577

    Article  MathSciNet  Google Scholar 

  21. Visser I, Speekenbrink M (2010) depmixs4: an r package for hidden markov models. J Stat Softw 36:1–21

    Google Scholar 

  22. Yada K, Sano N (2012) Customer behavior modelling using radio frequency identification data and the hidden markov model. Proc Serv Res Innov Inst Glob Conf 2012:509–514

    Google Scholar 

Download references

Acknowledgments

This research was supported by the Strategic Project to Support the Formation of Research Bases at Private Universities: Matching Fund Subsidy from MEXT (Ministry of Education, Culture, Sports, Science and Technology), 2009–2013.

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Authors and Affiliations

  1. 2641 Yamazaki, Noda, Chiba, Japan

    Natsuki Sano

  2. 3-3-35, Yamate-cho, Suita, Osaka, Japan

    Katsutoshi Yada

Authors
  1. Natsuki Sano
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  2. Katsutoshi Yada
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Correspondence to Natsuki Sano.

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Sano, N., Yada, K. The influence of sales areas and bargain sales on customer behavior in a grocery store. Neural Comput & Applic 26, 355–361 (2015). https://doi.org/10.1007/s00521-014-1619-8

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  • DOI: https://doi.org/10.1007/s00521-014-1619-8

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