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The study is dedicated to analyzing the relevance of using Customer Relationship Management (CRM) systems for a wide range of business processes in marketing activities and highlights ways to improve their efficiency through integration with artificial intelligence (AI). The authors present a solution for automating data flow processing and demand forecasting in a selected sales segment by developing a neural network and integrating it with CRM systems that are not AI-driven. A practical example demonstrates the development of an LSTM recurrent neural network model in Python and methods for its implementation. Various applications of other neural network models and future research directions in the field of AI are also discussed.

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Expansion of Customer Relationship Management Functionality Using Recurrent LSTM Networks for Automation Demand Forecasting

  • 会议论文

  • 发表日期：

    2026年

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  • Olena Kopishynska,
  • Yurii Utkin,
  • Igor Sliusar,
  • Mykola Somych,
  • Viktoriia Danylenko,
  • Tetiana Diadyk,
  • Iryna Zahrebelna

摘要

The study is dedicated to analyzing the relevance of using Customer Relationship Management (CRM) systems for a wide range of business processes in marketing activities and highlights ways to improve their efficiency through integration with artificial intelligence (AI). The authors present a solution for automating data flow processing and demand forecasting in a selected sales segment by developing a neural network and integrating it with CRM systems that are not AI-driven. A practical example demonstrates the development of an LSTM recurrent neural network model in Python and methods for its implementation. Various applications of other neural network models and future research directions in the field of AI are also discussed.