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31 lines
1.4 KiB
TeX
31 lines
1.4 KiB
TeX
\section{Conclusion}
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Hanzo Analytics demonstrates that machine learning can extract actionable insights from e-commerce event streams at scale. Our feature engineering pipeline, predictive models, and behavioral segmentation provide merchants with tools to understand and influence customer behavior. Production deployments across 500+ merchants validate the approach, with measurable improvements in campaign ROI (2.3x), churn reduction (6 percentage points), and prediction accuracy.
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Future work includes causal inference for intervention optimization, reinforcement learning for personalized engagement sequences, and federated learning for cross-merchant insights while preserving privacy.
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\bibliographystyle{plain}
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\begin{thebibliography}{9}
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P. Fader, B. Hardie, and K. Lee. Counting your customers the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2):275-284, 2005.
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\bibitem{gupta2006}
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S. Gupta et al. Modeling customer lifetime value. Journal of Service Research, 9(2):139-155, 2006.
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\bibitem{hidasi2016}
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B. Hidasi et al. Session-based recommendations with recurrent neural networks. ICLR, 2016.
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\bibitem{chen2016}
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T. Chen and C. Guestrin. XGBoost: A scalable tree boosting system. KDD, 2016.
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\end{thebibliography}
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