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<p>With the escalating demand for internet connectivity, operators have initiated marketing campaigns targeting home users for home broadband services. Nevertheless, the prevalent product homogenization in the market and flawed marketing plan designs have posed significant challenges to related marketing efforts. To tackle these issues, this study initially employed the MR fingerprint algorithm to formulate a positioning model to identify household users' positions. Subsequently, by crafting demand recognition indicators and utilizing decision algorithms to build a demand model, user needs and preferences were extracted to create a comprehensive user profile. Ultimately, precise marketing strategies were devised, offering tailored gifts based on the user's location and specific needs. The MR localization model demonstrated an impressive recall rate of 95.2% and an accuracy rate of 96.2%. The on-demand model exhibited a coverage rate of 83.8%, an accuracy rate of 96.3%, and an overall model accuracy of 89.7%. The proposed precision marketing approach could achieve success rates of 13.3% for cross-network customers and 31.3% for marketing, with a lower failure rate than traditional marketing strategies across various reasons for marketing failures. The research design method optimizes resource allocation efficiency and reduces marketing costs through dynamic calibration and multidimensional user profiling technology. It also breaks the product homogenization deadlock with personalized gift recommendations, enhancing user conversion willingness. This method provides communication operators an end-to-end solution that balances accuracy and scalability while offering a new paradigm for precision marketing research driven by big data. In the future, it can be further combined with time series analysis and multi-source data fusion to predict medium-and long-term demand fluctuations and expanded to applications in smart cities, the Internet of Things, and other fields.</p>

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The method of determining the user demand of home broadband services based on data mining and marketing strategies

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Xingchang Yang

摘要

With the escalating demand for internet connectivity, operators have initiated marketing campaigns targeting home users for home broadband services. Nevertheless, the prevalent product homogenization in the market and flawed marketing plan designs have posed significant challenges to related marketing efforts. To tackle these issues, this study initially employed the MR fingerprint algorithm to formulate a positioning model to identify household users' positions. Subsequently, by crafting demand recognition indicators and utilizing decision algorithms to build a demand model, user needs and preferences were extracted to create a comprehensive user profile. Ultimately, precise marketing strategies were devised, offering tailored gifts based on the user's location and specific needs. The MR localization model demonstrated an impressive recall rate of 95.2% and an accuracy rate of 96.2%. The on-demand model exhibited a coverage rate of 83.8%, an accuracy rate of 96.3%, and an overall model accuracy of 89.7%. The proposed precision marketing approach could achieve success rates of 13.3% for cross-network customers and 31.3% for marketing, with a lower failure rate than traditional marketing strategies across various reasons for marketing failures. The research design method optimizes resource allocation efficiency and reduces marketing costs through dynamic calibration and multidimensional user profiling technology. It also breaks the product homogenization deadlock with personalized gift recommendations, enhancing user conversion willingness. This method provides communication operators an end-to-end solution that balances accuracy and scalability while offering a new paradigm for precision marketing research driven by big data. In the future, it can be further combined with time series analysis and multi-source data fusion to predict medium-and long-term demand fluctuations and expanded to applications in smart cities, the Internet of Things, and other fields.