IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v20y2020i3d10.1007_s10660-019-09393-0.html
   My bibliography  Save this article

Segmenting market structure from multi-channel clickstream data: a novel generative model

Author

Listed:
  • Yang Qian

    (Hefei University of Technology
    Ministry of Education)

  • Yuanchun Jiang

    (Hefei University of Technology
    Ministry of Education)

  • Yanan Du

    (Hefei University of Technology
    Anhui Medical University)

  • Jianshan Sun

    (Hefei University of Technology
    Ministry of Education)

  • Yezheng Liu

    (Hefei University of Technology
    Ministry of Education)

Abstract

Competitive analysis has long been recognized as the cornerstones of firm’s strategic management and business activities. With the advent of the multi-channel clickstream, this paper studies the competitive market structure by developing a novel generative model. We first aggregate the multi-channel clickstream data to construct a consideration set for each user. Then, a novel sparse influence topic model (SITM) is proposed to segment an overall market into submarkets by leveraging the consideration sets at the individual level. Compared with the current generative models, the proposed SITM model considers the limited interest and the influence of products to generate users’ choice behaviors. Based on the multi-channel clickstream data from 109,081 users on 3779 cars, we empirically analyze the competition structure in China’s automotive market. Experimental results show that the proposed model can obtain deep insights of the competitive market structure and the competition power of each car in the market. It can also help managers understand user’s personalized interesting in the competitive market.

Suggested Citation

  • Yang Qian & Yuanchun Jiang & Yanan Du & Jianshan Sun & Yezheng Liu, 2020. "Segmenting market structure from multi-channel clickstream data: a novel generative model," Electronic Commerce Research, Springer, vol. 20(3), pages 509-533, September.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:3:d:10.1007_s10660-019-09393-0
    DOI: 10.1007/s10660-019-09393-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-019-09393-0
    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/s10660-019-09393-0?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. Nedungadi, Prakash, 1990. "Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(3), pages 263-276, December.
    2. Wayne S. DeSarbo & Rajdeep Grewal, 2007. "An alternative efficient representation of demand‐based competitive asymmetry," Strategic Management Journal, Wiley Blackwell, vol. 28(7), pages 755-766, July.
    3. Bucklin, Randolph E. & Sismeiro, Catarina, 2009. "Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 35-48.
    4. Vivek F. Farias & Andrew A. L, 2019. "Learning Preferences with Side Information," Management Science, INFORMS, vol. 65(7), pages 3131-3149, July.
    5. Shawn Mankad & Hyunjeong Spring Han & Joel Goh & Srinagesh Gavirneni, 2016. "Understanding Online Hotel Reviews Through Automated Text Analysis," Post-Print hal-02311939, HAL.
    6. Stephen L. France & Sanjoy Ghose, 2016. "An Analysis and Visualization Methodology for Identifying and Testing Market Structure," Marketing Science, INFORMS, vol. 35(1), pages 182-197, January.
    7. Dominik Gutt & Philipp Herrmann & Mohammad S. Rahman, 2019. "Crowd-Driven Competitive Intelligence: Understanding the Relationship Between Local Market Competition and Online Rating Distributions," Information Systems Research, INFORMS, vol. 30(3), pages 980-994, September.
    8. Chakravarti, Amitav & Janiszewski, Chris, 2003. "The Influence of Macro-level Motives on Consideration Set Composition in Novel Purchase Situations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(2), pages 244-258, September.
    9. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    10. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    11. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    12. Glen L. Urban & Philip L. Johnson & John R. Hauser, 1984. "Testing Competitive Market Structures," Marketing Science, INFORMS, vol. 3(2), pages 83-112.
    13. Karsten Hansen & Vishal Singh, 2009. "Market Structure Across Retail Formats," Marketing Science, INFORMS, vol. 28(4), pages 656-673, 07-08.
    14. Damangir, Sina & Du, Rex Yuxing & Hu, Ye, 2018. "Uncovering Patterns of Product Co-consideration: A Case Study of Online Vehicle Price Quote Request Data," Journal of Interactive Marketing, Elsevier, vol. 42(C), pages 1-17.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saravanan Thirumuruganathan & Soon-gyo Jung & Dianne Ramirez Robillos & Joni Salminen & Bernard J. Jansen, 2021. "Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?," Electronic Commerce Research, Springer, vol. 21(1), pages 73-100, March.

    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. Maximilian Matthe & Daniel M. Ringel & Bernd Skiera, 2023. "Mapping Market Structure Evolution," Marketing Science, INFORMS, vol. 42(3), pages 589-613, May.
    2. Damangir, Sina & Du, Rex Yuxing & Hu, Ye, 2018. "Uncovering Patterns of Product Co-consideration: A Case Study of Online Vehicle Price Quote Request Data," Journal of Interactive Marketing, Elsevier, vol. 42(C), pages 1-17.
    3. Adam N. Smith & Jim E. Griffin, 2023. "Shrinkage priors for high-dimensional demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 95-146, March.
    4. Saridakis, Charalampos & Katsikeas, Constantine S. & Angelidou, Sofia & Oikonomidou, Maria & Pratikakis, Polyvios, 2023. "Mining Twitter lists to extract brand-related associative information for celebrity endorsement," European Journal of Operational Research, Elsevier, vol. 311(1), pages 316-332.
    5. Zhou, Meihua & Angelopoulos, Spyros & Ou, Carol & Liu, Hongwei & Liang, Zhouyang, 2023. "Optimization of dynamic product offerings on online marketplaces: A network theory perspective," Other publications TiSEM 75d71155-88bf-4ff7-aba1-9, Tilburg University, School of Economics and Management.
    6. Ratchford, Brian & Soysal, Gonca & Zentner, Alejandro & Gauri, Dinesh K., 2022. "Online and offline retailing: What we know and directions for future research," Journal of Retailing, Elsevier, vol. 98(1), pages 152-177.
    7. Torsten J. Gerpott & Jan Berends, 2022. "Competitive pricing on online markets: a literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 596-622, December.
    8. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    9. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.
    10. Alzate, Miriam & Arce-Urriza, Marta & Cebollada, Javier, 2022. "Mining the text of online consumer reviews to analyze brand image and brand positioning," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    11. Xuan Gong & Yunchan Zhu & Rizwan Ali & Ruijin Guo, 2019. "Capturing Associations and Sustainable Competitiveness of Brands from Social Tags," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
    12. Lizhen Xu & Jason A. Duan & Andrew Whinston, 2014. "Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion," Management Science, INFORMS, vol. 60(6), pages 1392-1412, June.
    13. Kfir Eliaz & Ran Spiegler, 2011. "Consideration Sets and Competitive Marketing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(1), pages 235-262.
    14. Bian, Xuemei & Moutinho, Luiz, 2009. "An investigation of determinants of counterfeit purchase consideration," Journal of Business Research, Elsevier, vol. 62(3), pages 368-378, March.
    15. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    16. Desmichel, Perrine & Kocher, Bruno, 2020. "Luxury Single- versus Multi-Brand Stores: The Effect of Consumers’ Hedonic Goals on Brand Comparisons," Journal of Retailing, Elsevier, vol. 96(2), pages 203-219.
    17. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    18. , & ,, 2011. "On the strategic use of attention grabbers," Theoretical Economics, Econometric Society, vol. 6(1), January.
    19. Vishal Bindroo & Xin He & Raj Echambadi, 2016. "Satisfaction—Repurchase Intentions Relationship: Exploring the Contingent Roles of Consideration Set Size and Price Consciousness," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(3), pages 115-125, December.
    20. Paramveer S. Dhillon & Sinan Aral, 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach," Marketing Science, INFORMS, vol. 40(6), pages 1059-1080, November.

    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:elcore:v:20:y:2020:i:3:d:10.1007_s10660-019-09393-0. 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.