Enterprise Workforce Cohort & Retention Analytics Platform

Consolidating multi-enterprise workforce data into accurate cohort, retention, attrition, flow, attendance, and funnel intelligence.

Overview

A Python-based analytics framework for consolidating, cleaning, standardizing, validating, and analyzing monthly workforce and resident operational data across multiple enterprise sources.

The project demonstrates an end-to-end workflow covering data quality, payroll/attendance metrics, cohort analysis, attrition, retention, journey continuity, drop-off, flow analysis, and funnel reporting.

Privacy note: The included dataset is synthetic demonstration data. No confidential company, employee, payroll, or resident records are included.

Business Objectives

• Consolidate fragmented enterprise datasets
• Standardize and validate operational data
• Remove duplicate records and improve consistency
• Track paid and attended days
• Measure attrition and retention
• Identify continuous vs. dropped-off journeys
• Build cohort and flow analysis
• Produce management-ready reporting outputs

Analytics Modules

Data Quality: standardization, validation, deduplication, missing-value handling, attendance-rate calculation

Cohort Analytics: first-observed cohort assignment and monthly active population tracking

Attrition & Retention: month-over-month retention, drop-off identification, enterprise-level attrition

Journey Analytics: single-month, multi-month, continuous and dropped-off journey analysis

Attendance: paid days, attended days and attendance-rate analysis

Flow & Funnel: new entries, continued population, drop-offs and funnel representation

Architecture

Raw Enterprise Data
        ↓
Consolidation & Standardization
        ↓
Validation & Deduplication
        ↓
Cohort / Attrition / Retention
        ↓
Journey / Flow / Funnel Analytics
        ↓
Structured Management Reports

Repository Structure

Enterprise-Workforce-Cohort-Retention-Analytics/
├── data/
│   ├── raw/
│   │   └── sample_workforce_monthly_data.csv
│   └── processed/
├── src/
│   ├── data_cleaning.py
│   ├── data_consolidation.py
│   ├── cohort_analysis.py
│   ├── attrition_analysis.py
│   ├── retention_analysis.py
│   ├── journey_analysis.py
│   ├── funnel_analysis.py
│   └── flow_analysis.py
├── reports/
├── visuals/
├── tests/
│   └── test_analytics.py
├── .env.example
├── .gitignore
├── LICENSE
├── README.md
├── main.py
└── requirements.txt

Technology Stack

Python • Pandas • Data Cleaning • Data Validation • Cohort Analysis • Attrition & Retention Analytics • Funnel Analysis • Flow Analysis • Pytest

Installation

git clone https://github.com/monesh-r/Enterprise-Workforce-Cohort-Retention-Analytics.git
cd Enterprise-Workforce-Cohort-Retention-Analytics
pip install -r requirements.txt

Run

python main.py

Generated outputs are written to data/processed/ and reports/.

Test

pytest

Generated Reports

• cohort_summary.csv
• attrition_by_enterprise.csv
• retention_summary.csv
• journey_summary.csv
• workforce_funnel.csv
• monthly_flow.csv

Real-World Application

The framework is designed to represent operational analytics involving multi-enterprise workforce datasets, payroll and attendance records, contracted accommodation/PG resident records, monthly movement, cohort reporting, attrition and retention monitoring, funnel and flow reporting, data quality management, and executive reporting.

Data Governance

Production implementations should use secure storage, access controls, masking, governance policies, and approved handling procedures for confidential workforce or resident information.

License

MIT License

Author: Monesh R
Focus: Data Analytics | Business Intelligence | Data Quality | Automation | Workforce Analytics