📊 User Behavior Analytics & Customer Segmentation Pipeline

📌 Executive Summary

Following the foundational Data Quality Assurance (DQA) established in Project #01, this second project transitions from microdata sanitization to advanced customer behavior modeling, financial aggregation, and strategic cohort segmentation.

In modern E-Commerce environments, customer microdata is often captured in nested, semi-structured formats. Without structured extraction pipelines, raw spend vectors and user behavior cannot be ingested into Customer Relationship Management (CRM) tools or Executive BI Dashboards. This pipeline automates the extraction, cleaning, and segmentation of user purchase histories into actionable business metrics.

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🎯 Strategic Business Objectives

1. Modular Ingestion Framework: Abstract DQA transformation logic into a reusable production function (clean_user) for linear execution.
2. Categorical Vector Normalization: Standardize multi-level user category vectors (fav_categories) into lower-case strings to ensure case-insensitive query processing.
3. Financial Metric Aggregation: Compute enterprise-wide Gross Revenue ($KPI$) and aggregate individual Customer Lifetime Value ($CLV$).
4. Loyalty Status Simulation: Implement a probabilistic transaction loop (while simulation) to evaluate target spend thresholds for VIP program qualification.
5. Multi-Dimensional Cohort Isolation: Filter high-value demographic segments (e.g., Age < 30 with CLV > $1,000).
6. Reusable Analytical Utility: Design a dynamic, multi-parameter extraction engine (get_client_by_cat) for ad-hoc business and marketing inquiries.

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🛠️ Data Pipeline Architecture

Raw Microdata Array (Nested Lists & Mixed Types)

• Step 1: Function Encapsulation ──► clean_user() (String sanitization, Int casting)
• Step 2: Categorical Normalization ──► Lowercase vector mapping across all categories
• Step 3: Financial Aggregation ──► Calculate Global Gross Revenue ($KPI$)
• Step 4: Loyalty Simulation Loop ──► Monte Carlo spend iteration (Target: $1,500)
• Step 5: Cohort Segmentation ──► Filter high-value youth demographic (Age < 30 & CLV > $1k)
• Step 6: Query Engine Utility ──► get_client_by_cat() -> Category-specific CLV profiles

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📊 Analytical Insights & Key Business Metrics

• Total Platform Gross Revenue: $9,189.00 across 10 active customer accounts.
• Target Growth Cohort (Age < 30): Identified key demographics driving engagement (Kate Morgan, Samantha Smith, Emily Brown, Jose Martinez, James Lee).
• High-Value Youth Segment (Age < 30 & CLV > $1,000): Isolated Samantha Smith ($1,063) and James Lee ($1,067) as primary candidates for retention and VIP loyalty campaigns.
• Category Ingestion Accuracy: Standardized category attributes guarantee zero schema ambiguity for downstream DW (PostgreSQL / Snowflake) loading.

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💻 Tech Stack & Key Concepts

• Language: Python 3.12
• Data Structures: Multi-dimensional Nested Lists, Vectors, Sets, Dictionaries
• Core Paradigms: Functional Programming, List Comprenhensions, Control Structures (while, for-in, if-else)
• Analytics Focus: Customer Lifetime Value (CLV), Cohort Analysis, Categorical Vectorization

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📁 Repository Structure

user-behavior-analytics-pipeline/

• user_behavior_analytics_pipeline.ipynb # Main Jupyter Notebook (Cleaned & Annotated)
• README.md # Executive Project Documentation
• .gitignore # Environment exclusion file

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🚀 How to Run

• Clone this repository: git clone [https://github.com/CarlosACrespoS/Advanced-User-Data-Processing-and-Segmentation]

• Navigate to the project directory:

cd user-behavior-analytics-pipeline

• Open the notebook in Visual Studio Insiders or Jupyter Notebook:

code-insiders user_behavior_analytics_pipeline.ipynb

• Execute all cells sequentially to run the end-to-end transformation and segmentation pipeline.

Author: Carlos A. Crespo S.

Data Analyst & Business Analytics Specialist

🔗 GitHub: github.com/CarlosACrespoS