Dynamic Tariff Modeling as a Predictive Tool for Enhancing Telecom Network Utilization and Customer Experience

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@article{1710815,
      author = {Omorinsola Bibire Seyi-Lande, Adesola Abdul-Gafar Arowogbadamu, Stanley Tochukwu Oziri},
      title = {Dynamic Tariff Modeling as a Predictive Tool for Enhancing Telecom Network Utilization and Customer Experience},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {12},
      pages = {436-450},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710815.pdf},
      abstract = {Telecommunications networks are increasingly challenged by rising data demand, fluctuating traffic patterns, and heightened customer expectations for affordability and service quality. Traditional static tariff models, while simple to administer, are often inflexible and ill-suited to address the dual challenges of optimizing network utilization and enhancing customer experience. This paper proposes dynamic tariff modeling as a predictive tool capable of addressing these limitations by integrating real-time data analytics, demand forecasting, and adaptive pricing mechanisms. Dynamic tariff modeling operates on the principle of aligning pricing structures with network conditions and customer behaviors. By leveraging predictive analytics and machine learning, telecom providers can anticipate traffic surges, simulate alternative scenarios, and adjust tariffs dynamically to encourage off-peak utilization. This approach reduces network congestion, balances resource allocation, and improves the overall efficiency of bandwidth use. At the same time, personalization of tariffs through segmentation and behavioral insights ensures that pricing remains fair, transparent, and responsive to customer needs. Such personalization not only enhances affordability but also strengthens customer satisfaction, retention, and long-term loyalty. The proposed framework emphasizes four key dimensions: data infrastructure for capturing usage patterns, predictive analytics for demand management, dynamic pricing mechanisms for real-time responsiveness, and customer engagement strategies to ensure trust and acceptance. While challenges such as data privacy concerns, regulatory constraints, and implementation costs remain, these can be mitigated through compliance frameworks, incremental adoption, and customer-centric design principles. Ultimately, dynamic tariff modeling represents a transformative approach that unites network optimization with customer-centric value creation. By embedding predictive intelligence into pricing strategies, telecom operators can achieve greater operational efficiency, improve customer experience, and establish a competitive edge in increasingly dynamic and resource-intensive digital markets.},
      keywords = {Dynamic Tariff Modeling, Predictive Analytics, Telecom Network Utilization, Customer Experience, Pricing Optimization, Demand Forecasting, Usage-Based Pricing, Real-Time Adjustments, Revenue Management, Data-Driven Strategy, Network Efficiency, Customer Segmentation},
      month = {June},
  }