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
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
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
网络首发:2021-03-10,
纸质出版:2021
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刘洪伟, 詹明君, 高鸿铭, 朱慧, 梁周扬. 基于消费者行为流视域的产品竞争市场结构分析[J]. 广东工业大学学报, 2021, 38(2): 26-33.
Liu Hong-wei, Zhan Ming-jun, Gao Hong-ming, et al. A Product Competitive Market Structure Analysis Based on Consumer Behavioral Stream[J]. 2021, 38(2): 26-33.
刘洪伟, 詹明君, 高鸿铭, 朱慧, 梁周扬. 基于消费者行为流视域的产品竞争市场结构分析[J]. 广东工业大学学报, 2021, 38(2): 26-33. DOI: 10.12052/gdutxb.200143.
Liu Hong-wei, Zhan Ming-jun, Gao Hong-ming, et al. A Product Competitive Market Structure Analysis Based on Consumer Behavioral Stream[J]. 2021, 38(2): 26-33. DOI: 10.12052/gdutxb.200143.
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。
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.
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.
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.
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.
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.
WELLS V K, CHANG S W, OLIVEIRA-CASTRO J, et al . Market segmentation from a behavioral perspective [J ] . Journal of Or ganizational Behavior Management, 2010, 30(2): 176-198.
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.
ROSSI P E, ALLENBY G M. Bayesian statistics and marketing [J]. Marketing Science, 2003, 22(3): 304-328.
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.
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.
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.
FRANCE S L, GHOSE S. An analysis and visualization methodology for identifying and testing market structure [J]. Marketing Science, 2016, 35(1): 182-197.
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.
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.
LEE T Y, BRADLOW E T. Automated marketing research using online customer reviews [J]. Journal of Marketing Research, 2011, 48(5): 881-894.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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