طراحی چارچوب هوش تجاری مبتنی بر دادهکاوی رفتار کاربران در کسبوکارهای الکترونیکی : با تأکید بر بازاریابی 4.0
محورهای موضوعی : انتقال فناوري و تجاريسازي پژوهش
محمدعلی شرفیان
1
*
,
یاسر قاسمی نژاد
2
,
فائزه اسمعیل بگی
3
1 - دانشکده مدیریت، دانشگاه آزاد اسلامی، واحد تهران جنوب، تهران، ایران
2 - دانشکده مدیریت صنعتی, دانشگاه جامع امام حسین، تهران، ایران
3 - دانشکده گردشگری دانشگاه تهران -تهران - ایران
کلید واژه: هوش تجاری, دادهکاوی رفتار کاربران, بازاریابی 4.0, فراترکیب, کسبوکارهای الکترونیکی, تحلیل احساسات,
چکیده مقاله :
در عصر دیجیتال کنونی، کسبوکارهای الکترونیکی با حجم انبوهی از دادههای رفتاری کاربران مواجهاند که تحلیل دقیق و بهموقع آنها میتواند به خلق مزیت رقابتی، تصمیمگیری هوشمندانه و ارتقای تجربه مشتری منجر شود. پژوهش حاضر با هدف طراحی چارچوبی جامع، یکپارچه و نوآورانه برای دادهکاوی رفتار کاربران در بستر بازاریابی 4.0 انجام شده است. این پژوهش از روش مرور نظاممند و رویکرد فراترکیب استفاده کرده است تا مؤلفههای کلیدی مؤثر بر دادهکاوی رفتار کاربران شناسایی و در قالب مدلی مفهومی و کاربردی ترکیب شوند. چارچوب پیشنهادی بر پایه فرآیند استاندارد CRISP-DM و با بهرهگیری از تحلیل احساسات مبتنی بر یادگیری عمیق، مدل پذیرش فناوری (UTAUT) و تحلیل شبکههای اجتماعی توسعه یافته است. یافتهها نشان میدهد که مؤلفههایی نظیر کیفیت داده، تحلیل احساسات، تعاملات اجتماعی، دادههای دموگرافیک، ترجیحات خرید، فناوریهای هوشمند و امنیت داده نقش تعیینکنندهای در درک رفتار کاربران و پیشبینی نیازهای آنان دارند. نوآوری اصلی پژوهش در تلفیق ابعاد فنی و رفتاری دادهکاوی و ارائه ساختاری سهلایه شامل لایه ورودی (منابع داده)، لایه پردازش (تحلیل و مدلسازی) و لایه خروجی (نتایج بازاریابی 4.0) است. این چارچوب، راهنمایی کاربردی برای مدیران بازاریابی و تصمیمگیران دیجیتال فراهم میکند تا با اتکا به تحلیل دادههای کلان و رفتار کاربران، استراتژیهای بازاریابی مؤثرتر، خدمات شخصیسازیشدهتر و تصمیمات مبتنی بر داده اتخاذ کنند. در نتیجه، میتواند به ارتقای هوش تجاری سازمانها، افزایش وفاداری مشتریان و ایجاد مزیت رقابتی پایدار در فضای کسبوکار دیجیتال منجر شود.
In the current digital age, e-businesses are faced with a massive amount of user behavioral data, the accurate and timely analysis of which can lead to creating a competitive advantage, intelligent decision-making, and improving the customer experience. The present study aimed to design a comprehensive, integrated, and innovative framework for user behavioral data mining in the context of Marketing 4.0. This study used a systematic review method and a meta-synthesis approach to identify key components affecting user behavioral data mining and combine them into a conceptual and practical model. The proposed framework is based on the standard CRISP-DM process and is developed using deep learning-based sentiment analysis, technology acceptance model (UTAUT), and social network analysis. The findings show that components such as data quality, sentiment analysis, social interactions, demographic data, purchasing preferences, smart technologies, and data security play a decisive role in understanding user behavior and predicting their needs. The main innovation of the research is in combining the technical and behavioral dimensions of data mining and presenting a three-layer structure including the input layer (data sources), the processing layer (analysis and modeling), and the output layer (Marketing 4.0 results). This framework provides practical guidance for marketing managers and digital decision makers to adopt more effective marketing strategies, more personalized services, and data-driven decisions by relying on big data analysis and user behavior. As a result, it can lead to improving the business intelligence of organizations, increasing customer loyalty, and creating a sustainable competitive advantage in the digital business environment.
[1] سلیمانی حمیدرضا. (2024). بررسی تأثیر داده کاوی و تحلیل داده ها بر بازدهی کمپین های بازاریابی دیجیتالی: ابزارها، روش ها و مطالعات موردی.
[2] کاردگر، وحید و یداله زاده طبری، میثم و بزرگی راد، سید یاسر،1402،نقش هوش مصنوعی و فناوری های نوین داده کاوی در بازاریابی دیجیتالی هوشمند،اولین کنفرانس ملی پژوهش و نوآوری در هوش مصنوعی،کرج،https://civilica.com/doc/2035193
[3] قاسمیان صاحبی، علی و کردحیدری، راحیل و آقایی، محمد،1402،بررسی مدیریت کلان داده ها و تاثیر آن بر رضایت مشتریان و عملکرد سازمانی در صنعت خرده فروشی ایران،https://civilica.com/doc/1877933
[4] Li, L., & Zhang, J. (2021). Research and analysis of an enterprise E-commerce marketing system under the big data environment. Journal of Organizational and End User Computing (JOEUC), 33(6), 1-19.
[5] Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
[6] Kotler, P., Keller, K. L., Brady, M., Goodman, M., & Hansen, T. (2016). Marketing Management 3rd edn PDF eBook. Pearson Higher Ed.
[7] Darwiesh, A., Alghamdi, M. I., El-Baz, A. H., & Elhoseny, M. (2022). Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post‐Pandemic World. Journal of Healthcare Engineering, 2022(1), 6967158.
[8] Mukherjee, S. (2021). A Quantitative Study Using the UTAUT2 Model to Evaluate the Behavioral Intention to Use Telemedicine via IoT-Enabled Devices by Older Adults in the Chicago Tri-State Region (Doctoral dissertation, University of the Cumberlands).
[9] Laudon, K. C., & Laudon, J. P. (2004). Management information systems: Managing the digital firm. Pearson Educación.
[10] O'brien, J. A., & Marakas, G. M. (2006). Management information systems (Vol. 6). New York, NY, USA:: McGraw-Hill Irwin.
[11] Inmon, W. H. (2005). Building the data warehouse. John wiley & sons.
[12] Mining, W. I. D. (2006). Data mining: Concepts and techniques. Morgan Kaufinann, 10(559-569), 4.
[13] Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005, June). Practical machine learning tools and techniques. In Data mining (Vol. 2, No. 4, pp. 403-413). Amsterdam, The Netherlands: Elsevier.
[14] Tan, P. N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education India.
[15] Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.
[16] Schmitt, B. H. (2003). Customer experience management. John Willy & Sons.
[17] Hassenzahl, M. (2010). Experience design: Technology for all the right reasons (Vol. 8). Morgan & Claypool Publishers.
[18] Nielsen, J. (1994). Usability engineering. Morgan Kaufmann.
[19] Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. V. (2013). Digital business strategy: toward a next generation of insights. MIS quarterly, 471-482.
[20] Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of business research, 122, 889-901.
[21] Dara, A. K. (2024). The Role of social media in Shaping Consumer Behavior and Branding Strategies in Iraqi markets. Economic and Administrative Studies Journal, 4(3), 130-145.
[22] Sandelowski, M., & Barroso, J. (2006). Handbook for synthesizing qualitative research. Springer Publishing Company.
[23] Magdum, S. S., & Megha, J. V. (2017, June). Mining online reviews and tweets for predicting sales performance and success of movies. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 334-339). IEEE.
[24] Nithya, R., & Maheswari, D. (2016, January). Correlation of feature score to overall sentiment score for identifying the promising features. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.
[25] Doria, J. B., Galas, E., Faustino, X., Esperon, N., & Buted, D. (2023, May). Assessment on the effectiveness of learning management system using sentiment analysis. In AIP Conference Proceedings (Vol. 2602, No. 1). AIP Publishing.
[26] Thulasi, P. K., & Usha, K. (2016, September). Aspect polarity recognition of movie and product reviews in Malayalam. In 2016 International Conference on Next Generation Intelligent Systems (ICNGIS) (pp. 1-5). IEEE.
[27] Sharma, K., Jain, D., Jain, A., Rathore, Y. S., & Agarwal, S. (2024, March). Sentimental Analysis based on Machine Learning Technique. In 2024 3rd International Conference for Innovation in Technology (INOCON) (pp. 1-7). IEEE.
[28] Kim, Y., & Jeong, S. R. (2015). Opinion-mining methodology for social media analytics. KSII Transactions on Internet and Information Systems (TIIS), 9(1), 391-406.
[29] Abrahams, A. S., Jiao, J., Wang, G. A., & Fan, W. (2012). Vehicle defect discovery from social media. Decision Support Systems, 54(1), 87-97.
[30] Ahmad, I., Ali, T., Nazir, A., & Kamal, S. (2018, October). Social media competitive analysis of shoe brands on customer experiences. In International Conference on Intelligent Technologies and Applications (pp. 205-215). Singapore: Springer Singapore.
[31] Sitaram, D., Murthy, S., Ray, D., Sharma, D., & Dhar, K. (2015, July). Sentiment analysis of mixed language employing Hindi-English code switching. In 2015 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 1, pp. 271-276). IEEE.
[32] Parveen, H., & Pandey, S. (2016, July). Sentiment analysis on Twitter Data-set using Naive Bayes algorithm. In 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT) (pp. 416-419). IEEE.
[33] Boeri, R. (2013). Making the Case for Text and Predictive Analytics. ECONTENT, 36(10), 31-31.
[34] Nazir, M. U., Tharanidharan, S., Mian, M. S., Ahmad, I., Hayat, K., Nazar, S. K. A., ... & Ghumman, M. R. (2019). Social media competitive analysis-a case study in the pizza industry of Pakistan. In Intelligent Technologies and Applications: First International Conference, INTAP 2018, Bahawalpur, Pakistan, October 23-25, 2018, Revised Selected Papers 1 (pp. 313-325). Springer Singapore.
[35] Setiya, K., Ubacht, J., Cunningham, S., & Oruç, S. (2016). Business Intelligence from User Generated Content: online opinion formation in purchasing decisions in high-tech markets. In Social Media: The Good, the Bad, and the Ugly: 15th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2016, Swansea, UK, September 13–15, 2016, Proceedings 15 (pp. 505-521). Springer International Publishing.
[36] Khedkar, S. A., & Shinde, S. K. (2018, December). Customer review analytics for business intelligence. In 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1-5). IEEE.
[37] Abu-Salih, B., Wongthongtham, P., Zhu, D., Chan, K. Y., & Rudra, A. (2021). Social big data analytics. Springer Singapore..
[38] Desai, M., & Mehta, M. A. (2016, April). Techniques for sentiment analysis of Twitter data: A comprehensive survey. In 2016 international conference on computing, communication and automation (ICCCA) (pp. 149-154). IEEE.
[39] Tan, Y. F., Lam, H. S., Azlan, A., & Soo, W. K. (2016). Sentiment analysis for telco popularity on twitter big data using a novel Malaysian dictionary. In Advances in Digital Technologies (pp. 112-125). IOS Press.
[40] Chang, A. C., Trappey, C. V., Trappey, A. J., & Chen, L. W. (2020). Web mining customer perceptions to define product positions and design preferences. International Journal on Semantic Web and Information Systems (IJSWIS), 16(2), 42-58.
[41] Ramos, C. M. (2022). Business intelligence approach and sentiment analysis as a management strategy applied to study customer satisfaction in the hospitality sector. In Advances in Tourism, Technology and Systems: Selected Papers from ICOTTS 2021, Volume 2 (pp. 537-547). Singapore: Springer Nature Singapore.
[42] Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management science, 57(8), 1485-1509.
[43] Deng, S., Sinha, A. P., & Zhao, H. (2017). Resolving ambiguity in sentiment classification: The role of dependency features. ACM Transactions on Management Information Systems (TMIS), 8(2-3), 1-13.
[44] Das, A., & Kannan, A. (2014, August). Discovering topical aspects in microblogs. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (pp. 860-871).
[45] Jindal, S., & Sharma, K. (2019). A review on sentiment classification: natural language understanding. Recent Patents on Engineering, 13(1), 20-27.
[46] Venkateswaran, P. S., Dominic, M. L., Kem, D., Viswanathan, N. S., Kashyap, A. K., Rameshkkumar, S. R., & Sivakani, R. (2025). A Review of Text Mining and Sentiment Analysis for the Purpose of Determining the Veracity of Online Reviews. E-Commerce, Marketing, and Consumer Behavior in the AI Era, 237-258.
[47] Nahili, W., Rezeg, K., & Kazar, O. (2020, June). Sentiment analysis on product reviews data using supervised learning: A comprehensive review of recent techniques. In Proceedings of the 10th International Conference on Information Systems and Technologies (pp. 1-6).
[48] Bahrainian, S. A., & Dengel, A. (2013, November). Sentiment analysis using sentiment features. In 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (Vol. 3, pp. 26-29). IEEE.
[49] Chaudhary, K., & Alam, M. (Eds.). (2023). AI-Based Data Analytics: Applications for Business Management. CRC Press.
[50] Zafar, M. B., Bhattacharya, P., Ganguly, N., Gummadi, K. P., & Ghosh, S. (2015). Sampling content from online social networks: Comparing random vs. expert sampling of the twitter stream. ACM Transactions on the Web (TWEB), 9(3), 1-33.
[51] He, W., Shen, J., Tian, X., Li, Y., Akula, V., Yan, G., & Tao, R. (2015). Gaining competitive intelligence from social media data: Evidence from two largest retail chains in the world. Industrial management & data systems, 115(9), 1622-1636.
[52] Steinbauer, F., & Kröll, M. (2016, June). Sentiment analysis for German Facebook pages. In International Conference on Applications of Natural Language to Information Systems (pp. 427-432). Cham: Springer International Publishing.
[53] Khedkar, S., & Shinde, S. (2020). Ensemble Classifier for Praise or Complaint Classification and Visualization from Big Data. In Internet of Things, Smart Computing and Technology: A Roadmap Ahead (pp. 97-118). Cham: Springer International Publishing.
[54] Berlanga, R., Aramburu, M. J., Llidó, D. M., & García-Moya, L. (2014). Towards a semantic data infrastructure for social business intelligence. In New Trends in Databases and Information Systems: 17th East European Conference on Advances in Databases and Information Systems (pp. 319-327). Springer International Publishing.
[55] Halibas, A. S., Shaffi, A. S., & Mohamed, M. A. K. V. (2018, March). Application of text classification and clustering of Twitter data for business analytics. In 2018 Majan international conference (MIC) (pp. 1-7). IEEE.
[56] Schumaker, R. P., Jarmoszko, A. T., & Labedz Jr, C. S. (2016). Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decision Support Systems, 88, 76-84.
[57] Azeroual, O., Nacheva, R., Nikiforova, A., Störl, U., & Fraisse, A. (2023, June). Predictive Analytics intelligent decision-making framework and testing it through sentiment analysis on Twitter data. In Proceedings of the 24th International Conference on Computer Systems and Technologies (pp. 42-53).
[58] Joshi, P. A., Simon, G., & Murumkar, Y. P. (2018, August). Generation of brand/product reputation using Twitter data. In 2018 International Conference on Information, Communication, Engineering and Technology (ICICET) (pp. 1-4). IEEE.
[59]Casas-Valadez, M. A., Faz-Mendoza, A., Medina-Rodriguez, C. E., Cobo, M. J., Gamboa-Rosales, N. K., & López-Robles, J. R. (2020, December). Research trends in Sentiment Analysis and Opinion Mining from Knowledge Management approach: A science mapping from 2007 to 2020. In 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) (pp. 1-6). IEEE.
[60] Kagan, V., Rossini, E., & Sapounas, D. (2013). Sentiment analysis for PTSD signals. New York: Springer.
[61] Yang, B., Fu, X., Sidiropoulos, N. D., & Hong, M. (2017, July). Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In international conference on machine learning (pp. 3861-3870). PMLR.
[62] Deng, S., Huang, Z., Sinha, A. P., & Zhao, H. (2018). The interaction between microblog sentiment snd stock returns. MIS quarterly, 42(3), 895-A13.
[63] Zarrabeitia-Bilbao, E., Morales-i-Gras, J., Rio-Belver, R. M., & Garechana-Anacabe, G. (2022). Green energy: identifying development trends in society using Twitter data mining to make strategic decisions. Profesional de la Información, 31(1).
[64] Zhan, Y., Han, R., Tse, M., Ali, M. H., & Hu, J. (2021). A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry. Technological Forecasting and Social Change, 163, 120504.
[65] Singh, B., Kushwaha, N., & Vyas, O. P. (2016, November). An interpretation of sentiment analysis for enrichment of Business Intelligence. In 2016 IEEE Region 10 Conference (TENCON) (pp. 18-23). IEEE.
[66] Pang, B., & Lee, L. (2009). Opinion mining and sentiment analysis. Comput. Linguist, 35(2), 311-312.
[67] Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers, 168.
[68] Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business horizons, 52(4), 357-365.
[69] Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177).
[70] Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of marketing, 68(1), 109-127.
[71] Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: the new science of Winning. Harvard business review press, Language, 15(217), 24.
[72] Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 12(4), 5-33.
[73] Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.
[74] Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. pearson.
[75] Davenport, T. H., & Patil, D. J. (2012). Data scientist. Harvard business review, 90(5), 70-76.
[76] Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS quarterly, 17-41.
[77] McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
[78] GDPR. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data.
[79] Rust, R. T., & Lemon, K. N. (2001). E-service and the consumer. International journal of electronic commerce, 5(3), 85-101.
[80] Frow, P. E., & Payne, A. F. (2009). Customer relationship management: a strategic perspective. Journal of business market management, 3, 7-27.
[81] Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of interactive marketing, 12(1), 17-30.
[82] Porter, M. E. (2008). Competitive advantage: Creating and sustaining superior performance. simon and schuster.