Customer Intelligence Hub — Featured Project

2025-10-25

Overview

The Customer Intelligence Hub is a unified analytics framework designed to transform raw customer data into measurable business intelligence. It integrates data engineering, analytics, and visualization through modular components that deliver end-to-end insight into customer behavior, value, and engagement.

The platform bridges strategic and operational analytics by consolidating multiple intelligence layers — retention, segmentation, feedback, and forecasting — into a single, extensible environment.

Core Architecture

The ecosystem is built around four foundational data components and nine analytical modules:

Data Foundation

1. Unified Dataset — Central schema integrating behavioral, transactional, and demographic data.
2. Template Library — Ready-to-use analytical and visualization templates.
3. Schema Guide — Standardized documentation for interoperability and reproducibility.
4. Data Preparation — Automated scripts for cleaning, transformation, and feature engineering.

Analytical Modules

Each module operates as an independent Streamlit application, providing focused analytics and visualizations:

• Retention: Churn prediction, customer lifetime value (CLV), and retention metrics.
• Segmentation: Persona clustering and cohort analysis.
• Feedback: Sentiment and NPS trend insights.
• Location: Geo-spatial distribution and regional performance mapping.
• Affinity: Cross-sell, upsell, and affinity modeling.
• Forecasting: Predictive demand and dynamic pricing analytics.
• Journey: Funnel and conversion path visualization.
• Acquisition: Channel attribution and conversion optimization.
• Loyalty: Scoring system for engagement and advocacy measurement.

Key Features

• Modular Architecture: Each analytical module functions independently yet connects to the unified data foundation.
• Streamlit Front-End: Lightweight, interactive dashboards for rapid exploration and presentation.
• Astro.js Hub: Centralized interface for navigation, dataset management, and module access.
• Data Reproducibility: Version-controlled pipelines ensure consistent analytical outcomes.
• Scalability: Easily extendable to integrate new data domains or ML models without structural changes.

Use Cases

• Marketing Optimization: Identify and target high-value segments with predictive retention modeling.
• Product Insights: Link feature usage to satisfaction and churn outcomes.
• Revenue Forecasting: Align pricing and demand elasticity with real-time customer data.
• Experience Intelligence: Combine NPS, sentiment, and journey analytics to enhance CX design.

Technology Stack

• Frontend: Astro.js, Tailwind, and Streamlit.
• Backend: Python (Pandas, Scikit-learn, Plotly, GeoPandas).
• Data Layer: PostgreSQL + lightweight synthetic demo datasets.
• Version Control & CI/CD: GitHub Actions for modular deployment and testing.

Summary

The Customer Intelligence Hub delivers a complete, reproducible ecosystem for understanding and optimizing customer behavior. By combining modular data analytics with a unified data foundation, it enables scalable intelligence delivery across marketing, product, and strategy teams.

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