IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v17y2023i3d10.1007_s11634-022-00517-3.html
   My bibliography  Save this article

Sequential classification of customer behavior based on sequence-to-sequence learning with gated-attention neural networks

Author

Listed:
  • Licheng Zhao

    (Dalian Maritime University)

  • Yi Zuo

    (Dalian Maritime University
    Kansai University)

  • Katsutoshi Yada

    (Kansai University)

Abstract

During the last decade, an increasing number of supermarkets have begun to use RFID technology to track consumers' in-store movements to collect data on their shopping behavioral. Marketers hope that such new types of RFID data will improve the accuracy of the existing customer segmentation, and provide effective marketing positioning from the customer’s perspective. Therefore, this paper presents an integrated work on combining RFID data with traditional point of sales (POS) data, and proposes a sequential classification-based model to classify and identify consumers’ purchasing behavior. We chose an island area of the supermarket to perform the tracking experiment and collected customer behavioral data for two months. RFID data are used to extract behavior explanatory variables, such as residence time and wandering direction. For these customers, we extracted their purchasing historical data for the past three months from the POS system to define customer background and segmentation. Finally, this paper proposes a novel classification model based on sequence-to-sequence (Seq2seq) learning architecture. The encoder–decoder of Seq2seq uses an attention mechanism to pursue sequential inputs, with gating units in the encoder and decoder adjusting the output weights based on the input variables. The experimental results showed that the proposed model has a higher accuracy and area under curve value for customer classification and recognition compared with other benchmark models. Furthermore, the validity of behavioral description variables among heterogeneous customers was verified by adjusting the attention mechanism.

Suggested Citation

  • Licheng Zhao & Yi Zuo & Katsutoshi Yada, 2023. "Sequential classification of customer behavior based on sequence-to-sequence learning with gated-attention neural networks," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 549-581, September.
    • Handle: RePEc:spr:advdac:v:17:y:2023:i:3:d:10.1007_s11634-022-00517-3
    DOI: 10.1007/s11634-022-00517-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-022-00517-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11634-022-00517-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Toshiki Sato & Yuichi Takano & Takanobu Nakahara, 2019. "Investigating consumers’ store-choice behavior via hierarchical variable selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 621-639, September.
    2. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    3. Kamila Migdał-Najman & Krzysztof Najman & Sylwia Badowska, 2020. "The GNG neural network in analyzing consumer behaviour patterns: empirical research on a purchasing behaviour processes realized by the elderly consumers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 947-982, December.
    4. Takanobu Nakahara & Katsutoshi Yada, 2012. "Analyzing consumers’ shopping behavior using RFID data and pattern mining," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 355-365, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Venkatesh Shankar & Pablo Azar & Matthew Fuller, 2008. "—: A Multicategory Brand Equity Model and Its Application at Allstate," Marketing Science, INFORMS, vol. 27(4), pages 567-584, 07-08.
    2. David R. Bell & Jeongwen Chiang & V. Padmanabhan, 1999. "The Decomposition of Promotional Response: An Empirical Generalization," Marketing Science, INFORMS, vol. 18(4), pages 504-526.
    3. Polo, Yolanda & Sese, F. Javier & Verhoef, Peter C., 2011. "The Effect of Pricing and Advertising on Customer Retention in a Liberalizing Market," Journal of Interactive Marketing, Elsevier, vol. 25(4), pages 201-214.
    4. Yücel, Eda & Karaesmen, Fikri & Salman, F. Sibel & Türkay, Metin, 2009. "Optimizing product assortment under customer-driven demand substitution," European Journal of Operational Research, Elsevier, vol. 199(3), pages 759-768, December.
    5. Yu Ding & Wayne S. DeSarbo & Dominique M. Hanssens & Kamel Jedidi & John G. Lynch & Donald R. Lehmann, 2020. "The past, present, and future of measurement and methods in marketing analysis," Marketing Letters, Springer, vol. 31(2), pages 175-186, September.
    6. Shivaram Subramanian & Hanif Sherali, 2010. "A fractional programming approach for retail category price optimization," Journal of Global Optimization, Springer, vol. 48(2), pages 263-277, October.
    7. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    8. Vishal Gaur & Young-Hoon Park, 2007. "Asymmetric Consumer Learning and Inventory Competition," Management Science, INFORMS, vol. 53(2), pages 227-240, February.
    9. Ravn, Morten O. & Schmitt-Grohe, Stephanie & Uribe, Martín & Uuskula, Lenno, 2010. "Deep habits and the dynamic effects of monetary policy shocks," Journal of the Japanese and International Economies, Elsevier, vol. 24(2), pages 236-258, June.
    10. Richards, Timothy J. & Patterson, Paul M., 1999. "The Impact Of Promotion And Advertising On Choice Of Fruit Category And Apple Variety: A Latent-Class Approach," 1999 Annual meeting, August 8-11, Nashville, TN 21627, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Namwoon Kim & Jin K. Han & Rajendra K. Srivastava, 2002. "A Dynamic IT Adoption Model for the SOHO Market: PC Generational Decisions with Technological Expectations," Management Science, INFORMS, vol. 48(2), pages 222-240, February.
    12. Vanitha Swaminathan & Srinivas Reddy & Sara Dommer, 2012. "Spillover effects of ingredient branded strategies on brand choice: A field study," Marketing Letters, Springer, vol. 23(1), pages 237-251, March.
    13. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.
    14. K. Sudhir, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Marketing Science, INFORMS, vol. 20(3), pages 244-264, October.
    15. Eric T. Anderson & Duncan I. Simester, 2001. "Are Sale Signs Less Effective When More Products Have Them?," Marketing Science, INFORMS, vol. 20(2), pages 121-142, March.
    16. Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
    17. Sha Yang & Yi Zhao & Ravi Dhar, 2010. "Modeling the Underreporting Bias in Panel Survey Data," Marketing Science, INFORMS, vol. 29(3), pages 525-539, 05-06.
    18. Capps, Oral & Seo, Seong-Cheon & Nichols, John P., 1997. "On the Estimation of Advertising Effects for Branded Products: An Application to Spaghetti Sauces," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 29(2), pages 291-302, December.
    19. Aydın Alptekinoğlu & John H. Semple, 2016. "The Exponomial Choice Model: A New Alternative for Assortment and Price Optimization," Operations Research, INFORMS, vol. 64(1), pages 79-93, February.
    20. Shao, Wei & Lye, Ashley & Rundle-Thiele, Sharyn, 2009. "Different strokes for different folks: A method to accommodate decision -making heterogeneity," Journal of Retailing and Consumer Services, Elsevier, vol. 16(6), pages 495-501.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:17:y:2023:i:3:d:10.1007_s11634-022-00517-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.