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

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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.

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Notes

  1. https://github.com/soberqian/SITM.

  2. http://www.zamplus.com/.

  3. https://github.com/soberqian/SparseTM.

  4. https://github.com/soberqian/InfluenceTM.

References

  1. Urban, G. L., Johnson, P. L., & Hauser, J. R. (1984). Testing competitive market structures. Marketing Science,3(2), 83–112.

    Google Scholar 

  2. DeSarbo, W. S., Grewal, R., & Wind, J. (2006). Who competes with whom? A demand-based perspective for identifying and representing asymmetric competition. Strategic Management Journal,27(2), 101–129.

    Google Scholar 

  3. Grover, R., & Srinivasan, V. (1987). A simultaneous approach to market segmentation and market structuring. Journal of Marketing Research,24(2), 139–153.

    Google Scholar 

  4. France, S. L., & Ghose, S. (2016). An analysis and visualization methodology for identifying and testing market structure. Marketing Science,35(1), 182–197.

    Google Scholar 

  5. Gabel, S., Guhl, D., & Klapper, D. (2019). P2V-MAP: mapping market structures for large retail assortments. Journal of Marketing Research. https://doi.org/10.1177/0022243719833631.

    Article  Google Scholar 

  6. Cooper, L. G., & Inoue, A. (1996). Building market structures from consumer preferences. Journal of Marketing Research,33(3), 293–306.

    Google Scholar 

  7. Elrod, T., Russell, G. J., Shocker, A. D., Andrews, R. L., Bacon, L., Bayus, B. L., et al. (2002). Inferring market structure from customer response to competing and complementary products. Marketing Letters,13(3), 221–232.

    Google Scholar 

  8. DeSarbo, W. S., & Grewal, R. (2007). An alternative efficient representation of demand-based competitive asymmetry. Strategic Management Journal,28(7), 755–766.

    Google Scholar 

  9. Nedungadi, P. (1990). Recall and consumer consideration sets: Influencing choice without altering brand evaluations. Journal of consumer research,17(3), 263–276.

    Google Scholar 

  10. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science,31(3), 521–543.

    Google Scholar 

  11. Valkanas, G., Lappas, T., & Gunopulos, D. (2017). Mining competitors from large unstructured datasets. IEEE Transactions on Knowledge and Data Engineering,29(9), 1971–1984.

    Google Scholar 

  12. Damangir, S., Du, R. Y., & Hu, Y. (2018). Uncovering patterns of product co-consideration: A case study of online vehicle price quote request data. Journal of Interactive Marketing,42, 1–17.

    Google Scholar 

  13. Wei, Q., Qiao, D., Zhang, J., Chen, G., & Guo, X. (2016). A novel bipartite graph based competitiveness degree analysis from query logs. ACM Transactions on Knowledge Discovery from Data (TKDD),11(2), 21.

    Google Scholar 

  14. Gutt, D., Herrmann, P., & Rahman, M. S. (2019). Crowd-driven competitive intelligence: Understanding the relationship between local market competition and online rating distributions. Information Systems Research,30(3), 980–994.

    Google Scholar 

  15. Nam, H., Joshi, Y. V., & Kannan, P. (2017). Harvesting brand information from social tags. Journal of Marketing,81(4), 88–108.

    Google Scholar 

  16. Bucklin, R. E., & Sismeiro, C. (2009). Click here for Internet insight: Advances in clickstream data analysis in marketing. Journal of Interactive marketing,23(1), 35–48.

    Google Scholar 

  17. Farias, V. F., & Li, A. A. (2019). Learning preferences with side information. Management Science. https://doi.org/10.1287/mnsc.2018.3092.

    Article  Google Scholar 

  18. Chen, Y., & Yao, S. (2016). Sequential search with refinement: Model and application with click-stream data. Management Science,63(12), 4345–4365.

    Google Scholar 

  19. Iwanaga, J., Nishimura, N., Sukegawa, N., & Takano, Y. (2019). Improving collaborative filtering recommendations by estimating user preferences from clickstream data. Electronic Commerce Research and Applications,37, 100877.

    Google Scholar 

  20. Hanson, W. A., & Putler, D. S. (1996). Hits and misses: Herd behavior and online product popularity. Marketing Letters,7(4), 297–305.

    Google Scholar 

  21. Jerath, K., Ma, L., & Park, Y.-H. (2014). Consumer click behavior at a search engine: The role of keyword popularity. Journal of Marketing Research,51(4), 480–486.

    Google Scholar 

  22. Dukas, R. (2004). Causes and consequences of limited attention. Brain, Behavior and Evolution,63(4), 197–210.

    Google Scholar 

  23. Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012). Competition among memes in a world with limited attention. Scientific Reports,2, 335.

    Google Scholar 

  24. Leung, A. C. M., Agarwal, A., Konana, P., & Kumar, A. (2016). Network analysis of search dynamics: The case of stock habitats. Management Science,63(8), 2667–2687.

    Google Scholar 

  25. Li, Q., Maggitti, P. G., Smith, K. G., Tesluk, P. E., & Katila, R. (2013). Top management attention to innovation: The role of search selection and intensity in new product introductions. Academy of Management Journal,56(3), 893–916.

    Google Scholar 

  26. Lin, T., Tian, W., Mei, Q., & Cheng, H. (2014). The dual-sparse topic model: Mining focused topics and focused terms in short text. In Proceedings of the 23rd international conference on world wide web 2014 (pp. 539–550). ACM.

  27. Wang, C., & Blei, D. M. (2009). Decoupling sparsity and smoothness in the discrete hierarchical Dirichlet process. In Advances in neural information processing systems 2009 (pp. 1982–1989).

  28. Kim, J. B., Albuquerque, P., & Bronnenberg, B. J. (2011). Mapping online consumer search. Journal of Marketing Research,48(1), 13–27.

    Google Scholar 

  29. Ringel, D. M., & Skiera, B. (2016). Visualizing asymmetric competition among more than 1,000 products using big search data. Marketing Science,35(3), 511–534.

    Google Scholar 

  30. Srivastava, R. K., Alpert, M. I., & Shocker, A. D. (1984). A customer-oriented approach for determining market structures. Journal of Marketing,48(2), 32–45.

    Google Scholar 

  31. Zenor, M. J., & Srivastava, R. K. (1993). Inferring market structure with aggregate data: A latent segment logit approach. Journal of Marketing Research,30(3), 369–379.

    Google Scholar 

  32. Hansen, K., & Singh, V. (2009). Market structure across retail formats. Marketing Science,28(4), 656–673.

    Google Scholar 

  33. Roberts, J. H., & Lattin, J. M. (1991). Development and testing of a model of consideration set composition. Journal of Marketing Research,28(4), 429–440.

    Google Scholar 

  34. Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research,48(5), 881–894.

    Google Scholar 

  35. Park, Y.-H., & Fader, P. S. (2004). Modeling browsing behavior at multiple websites. Marketing Science,23(3), 280–303.

    Google Scholar 

  36. Moe, W. W. (2006). An empirical two-stage choice model with varying decision rules applied to internet clickstream data. Journal of Marketing Research,43(4), 680–692.

    Google Scholar 

  37. Sambandam, R., & Lord, K. R. (1995). Switching behavior in automobile markets: A consideration-sets model. Journal of the Academy of Marketing Science,23(1), 57–65.

    Google Scholar 

  38. Chakravarti, A., & Janiszewski, C. (2003). The influence of macro-level motives on consideration set composition in novel purchase situations. Journal of Consumer Research,30(2), 244–258.

    Google Scholar 

  39. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research,3(Jan), 993–1022.

    Google Scholar 

  40. Geva, H., Oestreicher-Singer, G., & Saar-Tsechansky, M. (2019). Using retweets when shaping our online persona: Topic modeling approach. MIS Quarterly,43(2), 501–524.

    Google Scholar 

  41. Shi, Z., Lee, G. M., & Whinston, A. B. (2016). Toward a better measure of business proximity: Topic modeling for industry intelligence. MIS Quarterly,40(4), 1035–1056.

    Google Scholar 

  42. Chen, K., Kou, G., Shang, J., & Chen, Y. (2015). Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches. Electronic Commerce Research and Applications,14(1), 58–74.

    Google Scholar 

  43. Zhang, H., Kim, G., & Xing, E. P. (2015). Dynamic topic modeling for monitoring market competition from online text and image data. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining 2015 (pp. 1425–1434). ACM.

  44. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences,101(suppl 1), 5228–5235.

    Google Scholar 

  45. Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2005). Sharing clusters among related groups: Hierarchical Dirichlet processes. Advances in Neural Information Processing Systems,17, 1385–1392.

    Google Scholar 

  46. Mankad, S., Han, H. S., Goh, J., & Gavirneni, S. (2016). Understanding online hotel reviews through automated text analysis. Service Science,8(2), 124–138.

    Google Scholar 

  47. Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS),20(4), 422–446.

    Google Scholar 

  48. Liu, T.-Y. (2009). Learning to rank for information retrieval. Foundations and Trends in Information Retrieval,3(3), 225–331.

    Google Scholar 

  49. Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., & Zhou, T. (2012). Recommender systems. Physics Reports,519(1), 1–49.

    Google Scholar 

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Acknowledgements

This work is supported by the Major Program of the National Natural Science Foundation of China (91846201, 71490725), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (71521001), the National Natural Science Foundation of China (71722010, 91746302, 71872060), The National Key Research and Development Program of China (2017YFB0803303). This work is also sponsered by Zhejiang Lab (NO. 2019KE0AB04).

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, Anhui, People’s Republic of China

    Yang Qian, Yuanchun Jiang, Yanan Du, Jianshan Sun & Yezheng Liu

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, 230009, Anhui, People’s Republic of China

    Yang Qian, Yuanchun Jiang, Jianshan Sun & Yezheng Liu

  3. School of Health Management, Anhui Medical University, Hefei, 230032, Anhui, People’s Republic of China

    Yanan Du

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  1. Yang Qian
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Correspondence to Yuanchun Jiang.

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Qian, Y., Jiang, Y., Du, Y. et al. Segmenting market structure from multi-channel clickstream data: a novel generative model. Electron Commer Res 20, 509–533 (2020). https://doi.org/10.1007/s10660-019-09393-0

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