Lunar Whale https://paragraph.com/@lunarwhale undefined Sat, 18 Apr 2026 13:38:16 GMT https://validator.w3.org/feed/docs/rss2.html https://github.com/jpmonette/feed en Lunar Whale https://storage.googleapis.com/papyrus_images/1a36d3855a6d81dbc44ad32f762ef175af2ccd78690910a77405cd022c0df64d.jpg https://paragraph.com/@lunarwhale All rights reserved <![CDATA[How to Read Cohort Analysis in UA: Breaking Down Metrics, Mistakes, and Insights]]> https://paragraph.com/@lunarwhale/how-to-read-cohort-analysis-in-ua-breaking-down-metrics-mistakes-and-insights pYDj7Iwwzh2K8zwLvEAu Tue, 25 Mar 2025 13:37:24 GMT Cohort analysis is a powerful tool for understanding user behavior among groups who installed your app during the same period. It helps UA managers evaluate the long-term effectiveness of ad campaigns. Today, I’ll explain how to interpret cohort analysis, which metrics matter, and how to avoid common pitfalls.

  1. Key Metrics in Cohort Analysis Here are the core metrics I track in cohort analysis:

Retention:

Percentage of users still active after 1, 7, 14, or 30 days.

Example: "The January 1 cohort had a Day 7 retention of 40%."

LTV (Lifetime Value):

Revenue generated by a user over their lifetime.

Example: "TikTok Ads cohorts showed an LTV of $5 over 30 days."

ROI (Return on Investment):

Revenue vs. ad spend.

Example: "Google UAC cohorts achieved 120% ROI in Month 1."

CPA (Cost Per Action):

Cost to acquire a user who completes a target action (e.g., purchase).

Example: "Facebook Ads cohorts had a $10 CPA."

  1. How to Interpret Cohort Data Cohort analysis isn’t just about numbers—it’s about patterns. Here’s my approach:

Compare Cohorts:

Which cohort has the best retention?

Which traffic sources drive high-LTV users?

Track Trends:

Is retention improving over time?

Is CPA decreasing for newer cohorts?

Identify Red Flags:

A Day 3 retention drop may signal onboarding issues.

Low LTV? Revisit monetization strategies.

  1. Common Cohort Analysis Mistakes Avoid these pitfalls:

Short-Term Focus:

Don’t judge LTV based on just 7 days.

Fix: Analyze 30/60/90-day windows.

Ignoring Context:

Low retention could stem from seasonality or product changes.

Fix: Always cross-check external factors.

Mixing Cohorts:

Blending traffic sources hides insights.

Fix: Segment cohorts by channel/campaign.

  1. Turning Insights into Action How I use cohort data:

Optimize Ad Spend:

Scale budgets for high-retention channels (e.g., TikTok Ads).

Improve Product:

If Day 3 retention drops, revamp onboarding.

Test Hypotheses:

Replicate winning creatives/strategies across campaigns.

  1. Conclusion Cohort analysis is your roadmap to sustainable UA growth. Start using it today to make data-driven decisions and boost ROI.

]]> lunarwhale@newsletter.paragraph.com (Lunar Whale)