papers/hanzo-analytics-ml/sections/01-introduction.tex

\section{Introduction}


E-commerce generates vast quantities of behavioral data: page views, clicks, cart actions, purchases, and returns. This data encodes customer intent, preferences, and lifecycle stage. However, extracting actionable insights requires sophisticated machine learning pipelines capable of processing high-velocity event streams and producing real-time predictions.

Hanzo Analytics addresses three core challenges:

\begin{enumerate}
    \item \textbf{Predictive Analytics}: Forecasting customer actions (purchases, cart abandonment)
    \item \textbf{Customer Segmentation}: Discovering meaningful customer groups for targeted engagement
    \item \textbf{Churn Prediction}: Identifying customers at risk of disengagement before they leave
\end{enumerate}

\subsection{Contributions}

This paper contributes:

\begin{itemize}
    \item A real-time feature engineering pipeline extracting 847 behavioral features
    \item Predictive models achieving state-of-the-art accuracy for commerce applications
    \item A behavioral embedding approach for customer segmentation
    \item Production validation across 500+ merchants processing 50M daily events
\end{itemize}