Advances in Geospatial Analytics for Regional Go-to-Market Expansion in Logistics and E-Commerce

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@article{1708470,
      author = {Abiodun Yusuf Onifade, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, Remolekun Enitan Dosumu; Oyeronke Oluwatosin George},
      title = {Advances in Geospatial Analytics for Regional Go-to-Market Expansion in Logistics and E-Commerce},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {3},
      pages = {267-286},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708470.pdf},
      abstract = {Predictive modeling has emerged as a transformative tool in optimizing marketing funnels across both Business-to-Business (B2B) and Business-to-Consumer (B2C) systems. This systematic review investigates the current landscape of predictive modeling techniques applied to marketing funnel optimization, aiming to understand their effectiveness, contextual applications, and methodological advancements. The marketing funnel, which guides potential customers through awareness, consideration, and conversion stages, presents unique optimization challenges and opportunities in B2B and B2C contexts. In B2B systems, longer sales cycles, complex decision-making units, and account-based strategies necessitate sophisticated predictive tools such as lead scoring, customer lifetime value estimation, and sales forecasting. In contrast, B2C systems emphasize real-time personalization, behavioral segmentation, and high-volume data analytics. This review synthesizes findings from peer-reviewed literature published over the past decade, focusing on the application of machine learning algorithms including regression analysis, decision trees, neural networks, clustering, and ensemble methods. It highlights differences in data availability, funnel complexity, and model accuracy between B2B and B2C environments. Furthermore, the review explores how data preprocessing, feature engineering, and algorithm interpretability affect model performance and usability in marketing decision-making. Key findings reveal that while both sectors benefit from predictive analytics, B2C models often achieve higher accuracy due to larger datasets and more homogeneous customer behavior. However, B2B systems are beginning to integrate predictive modeling more effectively through advances in customer relationship management (CRM) platforms and account-based marketing (ABM). Despite these advances, significant research gaps remain, particularly in model transparency, integration of cross-channel data, and application of real-time analytics. This review concludes by offering practical recommendations for marketers and data scientists, emphasizing the need for context-specific modeling approaches and collaboration between technical and strategic teams. The review underscores the importance of predictive modeling as a strategic enabler for optimizing marketing funnels and improving return on investment in both B2B and B2C domains.},
      keywords = {Conceptual framework Integrating, Customer intelligence, Regional Market, Expansion strategies},
      month = {September},
  }