Customer Insights & Digital Engagement Analytics Platform

Project Overview

This project builds an end-to-end analytics platform to analyze digital user interaction data and generate actionable business insights.

The system simulates large-scale clickstream activity, processes the data through a Python ETL pipeline, performs customer segmentation using machine learning, and visualizes key business metrics through an interactive Power BI dashboard.

The platform demonstrates how organizations can capture insights from digital engagement channels, measure customer behavior, and optimize marketing initiatives using data analytics.

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Architecture

User Interaction Data ↓ Python Data Generator ↓ AWS S3 Data Lake ↓ AWS Athena SQL Analytics ↓ Customer Segmentation (Machine Learning) ↓ Power BI Business Intelligence Dashboard

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Technologies Used

Programming & Data Processing

• Python
• Pandas
• NumPy

Machine Learning

• Scikit-learn (K-Means clustering)

Cloud & Data Infrastructure

• AWS S3 (Data Lake)
• AWS Athena (SQL Analytics)

Data Analysis & Statistics

• SQL
• SciPy (A/B testing)

Visualization

• Power BI

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Dataset

The project simulates digital clickstream interaction data representing user engagement on a platform.

Dataset Characteristics

• Users: ~10,000 simulated users
• Interaction Events: ~200,000+ digital events
• Tracked Events
  • page_view
  • product_view
  • cart_add
  • purchase

These events represent typical digital channel engagement signals used by analytics teams.

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Key Features

Data Pipeline

A Python-based ETL pipeline generates and processes user interaction data.

Pipeline tasks include:

• Data ingestion
• Data cleaning
• Feature engineering
• Engagement metric aggregation
• Analytics dataset generation

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Cloud Data Architecture

Interaction data is stored in an AWS S3 data lake and analyzed using AWS Athena.

Athena enables scalable SQL queries on large interaction datasets without managing infrastructure.

Analytics queries calculate metrics such as:

• event distribution
• conversion funnel metrics
• user engagement levels
• daily activity trends

Example Athena queries are included in:

aws/athena_queries.sql

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Customer Segmentation (Machine Learning)

Customer behavior is analyzed using K-Means clustering.

Users are grouped into behavioral segments:

Segment	Description
Segment 0	Low engagement users
Segment 1	Casual visitors
Segment 2	Frequent users
Segment 3	High-value customers

This segmentation supports targeted marketing and personalization strategies.

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Marketing Experimentation (A/B Testing)

The project includes a marketing campaign experiment simulation.

Example experiment results:

Campaign	Conversion Rate
Campaign A	~12%
Campaign B	~16%

Analysis shows Campaign B achieves ~33% higher conversion performance, suggesting improved marketing strategy effectiveness.

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Power BI Dashboard

The Power BI dashboard visualizes key platform metrics including:

• Total users
• Total interaction events
• Total purchases
• Event distribution
• Customer segmentation
• Daily engagement trends
• Conversion funnel

Dashboard Preview

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Business Insights

Key insights derived from the analytics pipeline:

• Page views account for ~50% of all platform interactions, indicating strong browsing activity.
• The conversion funnel shows a ~60% drop-off between page view and product view, highlighting opportunities to improve product discovery.
• Customer segmentation reveals high-value users responsible for a significant share of purchases.
• A/B testing results indicate Campaign B delivers ~33% higher conversion rates than Campaign A.
• Users generate an average of ~20 interaction events per session, demonstrating strong digital engagement.

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