广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (02): 26-33.doi: 10.12052/gdutxb.200143

• 综合研究 • 上一篇    下一篇

基于消费者行为流视域的产品竞争市场结构分析

刘洪伟1, 詹明君1, 高鸿铭1, 朱慧2, 梁周扬1   

  1. 1. 广东工业大学 管理学院, 广东 广州 510520;
    2. 广州大学 管理学院, 广东 广州 510006
  • 收稿日期:2020-10-20 出版日期:2021-03-10 发布日期:2021-01-13
  • 通信作者: 高鸿铭(1993-),男,博士研究生,主要研究方向为数据挖掘和消费者行为,E-mail:gaohongming_gdut@163.com E-mail:gaohongming_gdut@163.com
  • 作者简介:刘洪伟(1962-)，男，教授，博士生导师，主要研究方向为智能商务和管理信息系统
  • 基金资助:
    国家自然科学基金资助项目(71671048)；国家自然科学基金青年项目(71901075)；教育部人文社会科学研究一般项目(19YJCZH278)

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

摘要： 消费者的反馈对市场结构分析至关重要。通过消费者行为流数据追踪消费者对产品的真实外在表现, 利用贝叶斯推断识别决定性反馈行为并构建产品?行为介入矩阵, 采用K-means聚类算法和多维标度分析法对产品市场进行细分和可视化分析。研究发现, 消费者的点击和加入收藏夹行为对产品的市场份额有显著影响, 产品的市场结构揭示了少数产品的支配地位及产品在自身品牌内以及跨品牌间的竞争形势。从消费者出发, 基于其行为反馈进行的市场结构分析有助于管理者更有效地制定和调整新老产品的开发及定位等营销策略, 在提升自身竞争力的同时为消费者提供更优质的产品或服务。

关键词: 市场结构分析, 产品竞争, 贝叶斯推断, 消费者行为流

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

中图分类号: 

  • 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!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发