Data AnalyticsRole: Data Analyst2022

Customer Retention Analysis

Cohort analysis, churn pattern modeling, and Power BI visualization for customer lifecycle management.

PythonPower BISQLScikit-learn

At a Glance

Domain
Customer Lifecycle & Retention Analytics
Core Stack
Python, Power BI, SQL, Scikit-learn
Methodology
Cohort Retention Matrices, Churn Modeling, BI Dashboards
Key Focus
User Drop-off Inflection Points & Churn Signals
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Overview

An analytics case study evaluating customer retention, churn signals, and lifetime behavior. Combines Python data modeling with interactive Power BI dashboards to help teams identify early indicators of customer drop-off.

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What I Worked On

  • Built monthly cohort retention matrices tracking drop-offs across onboarding milestones.
  • Engineered engagement features tracking recency, frequency, and activity decay.
  • Developed classification models in Python to predict probability of churn for active customer segments.
  • Built interactive Power BI dashboards communicating retention trends and risk segment distributions to team leaders.
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Key Takeaways

  • Combines Python analytical modeling with business-facing BI dashboard presentation.
  • Practical experience with core growth and retention metrics: cohort retention curves, churn rates, and activity degradation.
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