Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (02): 26-33.doi: 10.12052/gdutxb.200143

• Comprehensive Studies • Previous Articles     Next Articles

A Product Competitive Market Structure Analysis Based on Consumer Behavioral Stream

Liu Hong-wei1, Zhan Ming-jun1, Gao Hong-ming1, Zhu Hui2, Liang Zhou-yang1   

  1. 1. School of Management, Guangdong University of Technology, Guangzhou 510520, China;
    2. School of Management, Guangzhou University, Guangzhou 510006, China
  • Received:2020-10-20 Online:2021-03-10 Published:2021-01-13

HTML

PDF (PC)

2717

Abstract: Consumer feedback is critical to market structure analysis. The determinate interactive behaviors with Bayesian inference are identified and the product-behavior involvement matrix built by examining the data of consumer behavioral stream which reflects the intention of consumers towards products. A K-means clustering algorithm and multidimensional scaling (MDS) method are utilized to segment and visualize the product market. It is found that Consumers’ Click and Tag into favorite set significantly influences the market share of products. The segmentation result reveals the dominant phenomenon of few products in the whole market, as well as the competition intensity among the products not only across brands, but within the same brand. Considering the influence of consumers, analyzing market structure with real-time behavioral feedback enables managers to adjust their market strategies on development or positioning of new and old products.

Key words: product market structure analysis, product competition, Bayesian inference, consumer behavioral stream

CLC Number: 

  • TP391.4F713.8
[1] 刘洪伟, 梁周扬, 左妹华, 等. 利用消费者浏览行为识别品牌竞争关系研究[J]. 广东工业大学学报, 2019, 36(5): 1-6.
LIU H W, LIANG Z Y, ZUO M H, et al. Research on identifying brand competition relationshipswith consumer browsing behavior [J]. Journal of Guangdong University of Technology, 2019, 36(5): 1-6.
[2] 范梦婷, 刘洪伟, 高鸿铭, 等. 电子商务平台下的竞争产品市场结构研究[J]. 广东工业大学学报, 2019, 36(6): 32-37.
FAN M T, LIU H W, GAO H M, et al. A research on competitive product market structure of e-commerce platform [J]. Journal of Guangdong University of Technology, 2019, 36(6): 32-37.
[3] SRIVASTAVA R K, LEONE R P, SHOCKER A D. Market structure analysis: hierarchical clustering of products based on substitution-in-use [J]. Journal of Marketing, 1981, 45(3): 38-48.
[4] MIHIĆ M, KURSAN I. Assessing the situational factors and impulsive buying behavior: market segmentation approach [J]. Management: Journal of Contemporary Management Issues, 2010, 15(2): 47-66.
[5] WELLS V K, CHANG S W, OLIVEIRA-CASTRO J, et al. Market segmentation from a behavioral perspective [J]. Journal of Organizational Behavior Management, 2010, 30(2): 176-198.
[6] ELROD T, RUSSELL G J, SHOCKER A D, et al. Inferring market structure from customer response to competing and complementary products [J]. Marketing Letters, 2002, 13(3): 221-232.
[7] ROSSI P E, ALLENBY G M. Bayesian statistics and marketing [J]. Marketing Science, 2003, 22(3): 304-328.
[8] GABEL S, GUHL D, KLAPPER D. P2V-MAP: mapping market structures for large retail assortments [J]. Journal of Marketing Research, 2019, 56(4): 557-580.
[9] MANTRALA M K, LEVY M, KAHN B E, et al. Why is assortment planning so difficult for retailers? a framework and research agenda [J]. Journal of Retailing, 2009, 85(1): 71-83.
[10] RINGEL D M, SKIERA B. Visualizing asymmetric competition among more than 1, 000 products using big search data [J]. Marketing Science, 2016, 35(3): 511-534.
[11] FRANCE S L, GHOSE S. An analysis and visualization methodology for identifying and testing market structure [J]. Marketing Science, 2016, 35(1): 182-197.
[12] NETZER O, FELDMAN R, GOLDENBERG J, et al. Mine your own business: market-structure surveillance through text mining [J]. Marketing Science, 2012, 31(3): 521-543.
[13] URBAN G L, HAUSER J R. “Listening in” to find and explore new combinations of customer needs [J]. Journal of Marketing, 2004, 68(2): 72-87.
[14] LEE T Y, BRADLOW E T. Automated marketing research using online customer reviews [J]. Journal of Marketing Research, 2011, 48(5): 881-894.
[15] CHEN K, KOU G, SHANG J, et al. Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches [J]. Electronic Commerce Research and Applications, 2015, 14(1): 58-74.
[16] 莫赞, 罗敏瑶. 在线评论对消费者购买决策的影响研究——基于评论可信度和信任倾向的中介、调节作用[J]. 广东工业大学学报, 2019, 36(2): 54-61.
MO Z, LUO M Y. A research of the influence of online reviews on consumer purchase decision-based on mediation and adjustment of reliability comments and trust tendency [J]. Journal of Guangdong University of Technology, 2019, 36(2): 54-61.
[17] BUCKLIN R E, SISMEIRO C. Click here for Internet insight: advances in clickstream data analysis in marketing [J]. Journal of Interactive Marketing, 2009, 23(1): 35-48.
[18] MONTGOMERY A L, LI S, SRINIVASAN K, et al. Modeling online browsing and path analysis using clickstream data [J]. Marketing Science, 2004, 23(4): 579-595.
[19] 刘洪伟, 高鸿铭, 陈丽, 等. 基于用户浏览行为的兴趣识别管理模型[J]. 数据分析与知识发现, 2018, 2(2): 74-85.
LIU H W, GAO H M, CHEN L, et al. Identifying user interests based on browsing behaviors [J]. Data Analysis and Knowledge Discovery, 2018, 2(2): 74-85.
[20] MOE W W. Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream [J]. Journal of Consumer Psychology, 2003, 13(1-2): 29-39.
[21] SU Q, CHEN L. A method for discovering clusters of e-commerce interest patterns using click-stream data [J]. Electronic Commerce Research and Applications, 2015, 14(1): 1-13.
[22] DING A W, LI S, CHATTERJEE P. Learning user real-time intent for optimal dynamic web page transformation [J]. Information Systems Research, 2015, 26(2): 339-359.
[23] KOLLMANN T, LOMBERG C, PESCHL A. Web 1.0, web 2.0, and web 3.0: the development of e-business[M]. Hershey, PA: IGI Global, 2016: 1139-1148.
[24] 高鸿铭, 刘洪伟, 詹明君, 等. 在线评论与产品介入对虚拟购物车选择决策的影响研究——基于消费者介入理论[J/OL]. 中国管理科学: 1-12[2020-11-12]. https://doi.org/10.16381/j.cnki.issn1003-207x.2018.1727.
GAO H M, LIU H W, ZHAN M J, et al. Research on the impact of online reviews and product involvement on virtual shopping-cart decision-making based on consumer involvement[J/OL]. Chinese Journal of Management Science: 1-12[2020-11-12]. https://doi.org/10.16381/j.cnki.issn1003-207x.2018.1727.
[25] MCCARTHY D M, WINER R S. The Pareto rule in marketing revisited: is it 80/20 or 70/20? [J]. Marketing Letters, 2019, 30(2): 139-150.
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © 2017 Journal of Guangdong University of Technology
Add：729 East Dongfeng RD., Guangzhou PRC. Postcode: 510090
Tel：020-37626653,020-37626139,020-37626396
Email：xbzrb@gdut.edu.cn
Supported:Beijing Magtech