基于数据驱动决策的品牌营销策略研究综述与展望
A Review and Prospect of Brand Marketing Strategies Based on Data-Driven Decision Making
摘要: 随着信息技术的发展和互联网的普及,大量的数据被不断产生和积累。消费行为的数据化、数据技术的进步、竞争压力的增加和个性化需求的兴起等因素推动品牌营销者更加注重数据和分析的应用,以更好地了解市场、消费者和竞争对手,从而制定更有效的品牌营销策略。本文整理了近年来基于数据驱动决策的品牌营销领域的相关理论和实际应用。通过分析已经实践的数据驱动型企业发现,无针对性和被动式的品牌营销方式难以适应现代企业的发展需求,尤其是在基于互联网相关行业,面对新的机遇和挑战,企业应充分利用数据挖掘技术评估和设计品牌的营销策略。
Abstract: With the development of information technology and the widespread adoption of the internet, vast amounts of data are continuously generated and accumulated. Factors such as the datafication of consumer behavior, advancements in data technology, increased competitive pressure, and the rise of personalized demands have driven brand marketers to place greater emphasis on the application of data and analytics. This enables them to better understand the market, consumers, and competitors, thereby formulating more effective brand marketing strategies. This paper reviews the relevant theories and practical applications in the field of data-driven decision-making in brand marketing in recent years. By analyzing data-driven enterprises that have already been implemented, it is evident that non-targeted and passive brand marketing approaches are insufficient to meet the developmental needs of modern enterprises, particularly in internet-related industries. Faced with new opportunities and challenges, enterprises should fully leverage data mining technologies to evaluate and design brand marketing strategies.
文章引用:张学师. 基于数据驱动决策的品牌营销策略研究综述与展望[J]. 电子商务评论, 2025, 14(3): 967-976. https://doi.org/10.12677/ecl.2025.143787

参考文献

[1] Purcarea, T.V. (2021) Prioritization of the Precision-Marketing Efforts and Rigorous Brand Building, Based on Commitment to Authenticity. Romanian Distribution Committee Magazine, 12, 10-14.
[2] Scott, D.M. (2007) The New Rules of Marketing & PR: How to Use Social Media, Online Video, Mobile Applications, Blogs, News Releases, and Viral Marketing to Reach Buyers Directly. John Wiley & Sons Inc.
[3] Aaker, D.A. (1991) Managing Brand Equity, Capitalizing on the Value of a Brand Name. The Free Press.
[4] Gardner, B.B. and Levy, S.J. (1955) The Product and the Brand. Harvard Business Review, 33, 33-39.
[5] Lemon, K.N. and Verhoef, P.C. (2016) Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80, 69-96. [Google Scholar] [CrossRef
[6] Mihailovic, P. (1995) Strategic Brand Management: New Approaches to Creating and Evaluating Brand Equity. Journal of Brand Management, 3, 207-208. [Google Scholar] [CrossRef
[7] Hetet, B., Ackermann, C. and Mathieu, J. (2019) The Role of Brand Innovativeness on Attitudes Towards New Products Marketed by the Brand. Journal of Product & Brand Management, 29, 569-581. [Google Scholar] [CrossRef
[8] Tsai, Y.L., Chung, M.C. and Hsieh, I.J. (2021) The Sustainable Key Strategy for Brand Marketing—The Case of Madou Pomelo Products. International Journal of Agriculture Innovation, Technology and Globalisation, 2, 318-340. [Google Scholar] [CrossRef
[9] 张一兵, 葛新权, 王宗水. 基于文献分析视角的我国品牌营销发展趋势分析[J]. 商业经济研究, 2019(15): 64-67.
[10] Mccarthy, E.J. (1984) Basic Marketing: A Managerial Approach. Richard D. Irwin.
[11] Borden, N.H. (1984) The Concept of the Marketing Mix. Journal of Advertising Research, 2, 7-12.
[12] 李飞, 王高. 4Ps营销组合模型的改进研究[J]. 管理世界, 2006(9): 147-148, 167.
[13] 吴亚军. 互联网背景下企业市场营销创新研究[J]. 环球市场, 2021(5): 172, 176.
[14] Provost, F. and Fawcett, T. (2013) Data Science for Business: What You Need to KNOW about data Mining and Data-Analytic Thinking, O’Reilly Media, Inc.
[15] Aghaei, S. (2012) Evolution of the World Wide Web: From Web 1.0 to Web 4.0. International journal of Web & Semantic Technology, 3, 1-10. [Google Scholar] [CrossRef
[16] Chaffey, D. (2007) E-Business and E-Commerce Management: Strategy, Implementation and Practice. Pearson.
[17] Lioutas, E.D. and Charatsari, C. (2020) Big Data in Agriculture: Does the New Oil Lead to Sustainability? Geoforum, 109, 1-3. [Google Scholar] [CrossRef
[18] Lioutas, E.D., Charatsari, C., La Rocca, G. and De Rosa, M. (2019) Key Questions on the Use of Big Data in Farming: An Activity Theory Approach. NJAS: Wageningen Journal of Life Sciences, 90, 1-12. [Google Scholar] [CrossRef
[19] Pham, X. and Stack, M. (2018) How Data Analytics Is Transforming Agriculture. Business Horizons, 61, 125-133. [Google Scholar] [CrossRef
[20] Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F.X. (2017) A Review on the Practice of Big Data Analysis in Agriculture. Computers and Electronics in Agriculture, 143, 23-37. [Google Scholar] [CrossRef
[21] Dong, J.Q. and Yang, C. (2020) Business Value of Big Data Analytics: A Systems-Theoretic Approach and Empirical Test. Information & Management, 57, Article ID: 103124. [Google Scholar] [CrossRef
[22] Rodriguez, D., de Voil, P., Rufino, M., Odendo, M. and van Wijk, M. (2017) To Mulch or to Munch? Big Modelling of Big Data. Agricultural Systems, 153, 32-42. [Google Scholar] [CrossRef
[23] Castillo, A., Benitez, J., Liorens, J. and Braojos, J. (2021) Impact of Social Media on the Firm’s Knowledge Exploration and Knowledge Exploitation: The Role of Business Analytics Talent. Journal of the Association for Information Systems, 22, 1472-1508. [Google Scholar] [CrossRef
[24] Ross, J.W., Beath, C.M. and Quaadgras, A. (2013) You May Not Need Big Data after All. Harvard Business Review, 91, 90-98.
[25] 张罡, 王宗水, 赵红. 互联网 + 环境下营销模式创新:价值网络重构视角[J]. 管理评论, 2019, 31(3): 94-101.
[26] Kamble, S.S., Gunasekaran, A. and Gawankar, S.A. (2020) Achieving Sustainable Performance in a Data-Driven Agriculture Supply Chain: A Review for Research and Applications. International Journal of Production Economics, 219, 179-194. [Google Scholar] [CrossRef
[27] Visinescu, L.L., Jones, M.C. and Sidorova, A. (2016) Improving Decision Quality: The Role of Business Intelligence. Journal of Computer Information Systems, 57, 58-66. [Google Scholar] [CrossRef
[28] Carolan, M. (2018) Big Data and Food Retail: Nudging Out Citizens by Creating Dependent Consumers. Geoforum, 90, 142-150. [Google Scholar] [CrossRef
[29] Côrte-Real, N., Ruivo, P. and Oliveira, T. (2020) Leveraging Internet of Things and Big Data Analytics Initiatives in European and American Firms: Is Data Quality a Way to Extract Business Value? Information & Management, 57, Article ID: 103141. [Google Scholar] [CrossRef
[30] Durbach, I.N. and Stewart, T.J. (2012) Modeling Uncertainty in Multi-Criteria Decision Analysis. European Journal of Operational Research, 223, 1-14. [Google Scholar] [CrossRef
[31] Lang, M.A.K., Cleophas, C. and Ehmke, J.F. (2021) Multi-Criteria Decision Making in Dynamic Slotting for Attended Home Deliveries. Omega, 102, Article ID: 102305. [Google Scholar] [CrossRef
[32] Zadeh, L.A. (1975) The Concept of a Linguistic Variable and Its Application to Approximate Reasoning—II. Information Sciences, 8, 301-357. [Google Scholar] [CrossRef
[33] Zadeh, L.A. (1996) Fuzzy Logic = Computing with Words. IEEE Transactions on Fuzzy Systems, 4, 103-111. [Google Scholar] [CrossRef
[34] Rickard, J.T. (2011) Perceptual Computing: Aiding People in Making Subjective Judgments (Mendel, J.M. and Wu, D.; 2010) [Book Review]. IEEE Computational Intelligence Magazine, 6, 59-62. [Google Scholar] [CrossRef
[35] Li, C., Dong, Y., Herrera, F., Herrera-Viedma, E. and Martínez, L. (2017) Personalized Individual Semantics in Computing with Words for Supporting Linguistic Group Decision Making. an Application on Consensus Reaching. Information Fusion, 33, 29-40. [Google Scholar] [CrossRef
[36] Dong, Y.C., Xu, Y.F. and Yu, S. (2009) Computing the Numerical Scale of the Linguistic Term Set for the 2-Tuple Fuzzy Linguistic Representation Model. IEEE Transactions on Fuzzy Systems, 17, 1366-1378. [Google Scholar] [CrossRef
[37] Martinez, L. and Herrera, F. (2000) A 2-Tuple Fuzzy Linguistic Representation Model for Computing with Words. IEEE Transactions on Fuzzy Systems, 8, 746-752. [Google Scholar] [CrossRef
[38] Huang, H. and Li, C. (2018) Extended Personalized Individual Semantics with 2-Tuple Linguistic Preference for Supporting Consensus Decision Making. IEICE Transactions on Information and Systems, 101, 387-395. [Google Scholar] [CrossRef
[39] Li, C., Dong, Y., Pedrycz, W. and Herrera, F. (2022) Integrating Continual Personalized Individual Semantics Learning in Consensus Reaching in Linguistic Group Decision Making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 1525-1536. [Google Scholar] [CrossRef
[40] Tang, X., Zhang, Q., Peng, Z., Yang, S. and Pedrycz, W. (2019) Derivation of Personalized Numerical Scales from Distribution Linguistic Preference Relations: An Expected Consistency-Based Goal Programming Approach. Neural Computing and Applications, 31, 8769-8786. [Google Scholar] [CrossRef
[41] Tang, X., Zhang, Q., Peng, Z., Pedrycz, W. and Yang, S. (2020) Distribution Linguistic Preference Relations with Incomplete Symbolic Proportions for Group Decision Making. Applied Soft Computing, 88, Article ID: 106005. [Google Scholar] [CrossRef
[42] Li, C., Gao, Y. and Dong, Y. (2020) Managing Ignorance Elements and Personalized Individual Semantics under Incomplete Linguistic Distribution Context in Group Decision Making. Group Decision and Negotiation, 30, 97-118. [Google Scholar] [CrossRef
[43] Zhang, H., Dong, Y., Xiao, J., Chiclana, F. and Herrera-Viedma, E. (2020) Personalized Individual Semantics-Based Approach for Linguistic Failure Modes and Effects Analysis with Incomplete Preference Information. IISE Transactions, 52, 1275-1296. [Google Scholar] [CrossRef
[44] Yang, X., Li, H., Ni, L. and Li, T. (2021) Application of Artificial Intelligence in Precision Marketing. Journal of Organizational and End User Computing, 33, 209-219. [Google Scholar] [CrossRef
[45] Singh, H.P., Singh, H. and Paul, A.K. (2021) Dynamic ICT Modeling for Handling Student Data Using Big Data Technology and Hybrid Cloud Computing. In: Bhateja, V., Satapathy, S.C., Travieso-Gonzalez, C.M. and Flores-Fuentes, W., Eds., Computer Communication, Networking and IoT, Springer, 9-21. [Google Scholar] [CrossRef
[46] Marchena Sekli, G.F. and De La Vega, I. (2021) Adoption of Big Data Analytics and Its Impact on Organizational Performance in Higher Education Mediated by Knowledge Management. Journal of Open Innovation: Technology, Market, and Complexity, 7, Article 221. [Google Scholar] [CrossRef
[47] 常祺祺. 大数据时代建设银行滨州分行精准营销策略研究[D]: [硕士学位论文]. 咸阳: 西北农林科技大学, 2020.
[48] 朱林鸿. B城商行RFM数据驱动的个人客户精准营销策略研究[D]: [硕士学位论文]. 杭州: 浙江理工大学, 2023.
[49] 李泽平. 基于用户画像的ZJGT智能交通系统集成企业营销方案研究[D]: [硕士学位论文]. 郑州: 河南工业大学, 2021.
[50] Cui, Z., Xu, X., Xue, F., Cai, X., Cao, Y., Zhang, W., et al. (2020) Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios. IEEE Transactions on Services Computing, 13, 685-695. [Google Scholar] [CrossRef
[51] 张梦瑶, 崔晋川. 基于时间序列法的国税月度收入预测模型研究[J]. 系统科学与数学, 2008, 28(11): 1383-1390.
[52] Yoseph, F., Ahamed Hassain Malim, N.H., Heikkilä, M., Brezulianu, A., Geman, O. and Paskhal Rostam, N.A. (2020) The Impact of Big Data Market Segmentation Using Data Mining and Clustering Techniques. Journal of Intelligent & Fuzzy Systems, 38, 6159-6173. [Google Scholar] [CrossRef