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.
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