Developing Behavioral Analytics Models for Multichannel Customer Conversion Optimization

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@article{1709052,
      author = {Omolola Temitope Kufile, Bisayo Oluwatosin Otokiti, Abiodun Yusuf Onifade, Bisi Ogunwale, Chinelo Harriet Okolo},
      title = {Developing Behavioral Analytics Models for Multichannel Customer Conversion Optimization},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {10},
      pages = {339-354},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709052.pdf},
      abstract = {In the evolving digital marketing ecosystem, businesses increasingly leverage behavioral analytics to optimize customer conversion across multiple channels. This paper presents a comprehensive framework for developing behavioral analytics models that identify, interpret, and act upon cross-channel consumer behavior to enhance conversion rates. Through a hybrid approach combining machine learning, statistical modeling, and psychographic profiling, we provide an integrated model tailored to multichannel environments. A dataset from an omnichannel retailer, spanning web, mobile, email, and social interactions, was analyzed to validate the framework. The results demonstrate significant uplift in conversion metrics, customer engagement, and predictive accuracy. Moreover, the paper addresses critical challenges in multichannel attribution, privacy compliance, and real-time behavioral segmentation. Our findings contribute to advancing customer intelligence capabilities and offer actionable strategies for optimizing digital conversion pipelines.},
      keywords = {Customer behavior, conversion, multichannel, analytics, optimization, segmentation},
      month = {April},
  }