https://github.com/ngangawairimu/retention-cohort-churn-analysis

About This project analyzes subscriber retention through cohort analysis, highlighting retention trends and churn patterns over time. It uses SQL for data extraction and Python for visualization and analysis, offering actionable retention strategies.
https://github.com/ngangawairimu/retention-cohort-churn-analysis

cohort-analysis sql subscriber-retention

Last synced: 3 months ago
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About This project analyzes subscriber retention through cohort analysis, highlighting retention trends and churn patterns over time. It uses SQL for data extraction and Python for visualization and analysis, offering actionable retention strategies.

• Host: GitHub
• URL: https://github.com/ngangawairimu/retention-cohort-churn-analysis
• Owner: ngangawairimu
• Created: 2024-10-28T13:55:20.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-12-18T12:34:22.000Z (6 months ago)
• Last Synced: 2025-01-28T14:52:52.168Z (5 months ago)
• Topics: cohort-analysis, sql, subscriber-retention
• Homepage:
• Size: 32.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Retention, Cohorts & Churn Project

#### Summary of Analysis

### Google Sheets: https://docs.google.com/spreadsheets/d/1aHsOk4QBmF7qF9rvLarq5hCeJwUbQA5g/edit?usp=sharing&ouid=112959083755862807381&rtpof=true&sd=true

#### Data Preparation:

The analysis utilized a SQL query to extract subscriber data from the turing_data_analytics. The subscriptions table, focusing on subscriptions, starts between January 1, 2021, and February 7, 2021.

The data was organized into weekly cohorts, allowing for a clear view of subscriber activity over time.

#### Overview

The heatmap shows subscription retention rates across cohorts from November 2020 to January 2021. The percentage indicates how many users from each cohort remain subscribed after subsequent weeks. For earlier cohorts (e.g., November 2020), the retention rates steadily decline, reflecting natural subscriber churn. Later cohorts (e.g., December 2020) maintain higher retention rates in the initial weeks, with some retaining over 90% of users through week 6. However, retention data is incomplete for the most recent cohorts, such as January 2021, showing 0% past week 2, due to the cutoff period (February 7, 2021).

#### Recommendations:

Retention Strategies: Focus on sustaining the higher retention rates seen in December 2020 cohorts by analyzing successful engagement tactics.

Churn Analysis: Investigate why retention stabilizes around 85-88% for earlier cohorts and implement interventions at key drop-off points (e.g., weeks 2-4).

Future Cohorts: Test strategies for new cohorts that resemble the strong performance of December, ensuring timely and relevant engagement across the subscription lifecycle.