Keeping users is more valuable than constantly acquiring new ones. But to improve retention, you need to measure it correctly.

Retention isn’t just “how many users are still here.” It’s a combination of metrics that reveal how users behave, why they stay (or leave), and whether your product is truly valuable over time.

Here are the most important retention health metrics you should be tracking—especially if you’re building an app, SaaS product, or digital platform.

1. 🧮 Customer Retention Rate (CRR)

This is the core retention metric. It shows the percentage of customers who continue using your product over a specific period.

Formula:

mathematicaCopyEdit((E - N) / S) x 100

Where:

• E = End customers
• N = New customers acquired
• S = Starting customers

Why it matters: It gives you a clear view of how many users are sticking around.

2. 📉 Churn Rate

This is the flip side of retention—how many customers are leaving.

Formula:

sqlCopyEdit(Lost Customers / Total Customers at Start of Period) x 100

Track both:

• Customer churn (how many users cancel)
• Revenue churn (how much MRR/ARR is lost)

Why it matters: A high churn rate = a leaky bucket. You can’t scale what you can’t keep.

3. 📆 Time to First Value (TTFV)

This measures how long it takes a new user to reach the first meaningful success with your product.

Examples:

• First project created
• First report exported
• First hour tracked (for time apps)

Why it matters: A faster TTFV = stronger early retention. Delays here often lead to early churn.

4. 🧑‍🤝‍🧑 User Engagement (DAU, WAU, MAU)

Daily/Weekly/Monthly Active Users show how often users interact with your product.

Tip: Track DAU/MAU ratio to determine stickiness (i.e. how often users come back).

Why it matters: Engagement is a strong predictor of retention. If they’re not using it, they’ll leave.

5. 📈 Product Usage Depth

Are users engaging with just one core feature—or are they exploring your full toolset?

Examples:

• of features used
• of sessions per week
• of projects created

Why it matters: Deeper usage = more perceived value = higher retention.

6. 📊 Cohort Retention Analysis

Group users by signup month (or channel) and track their retention over time.

Example: 40% of January signups are still active after 90 days.

Why it matters: Shows you trends over time—are improvements actually increasing long-term retention?

7. 💰 Customer Lifetime Value (CLTV)

CLTV estimates how much revenue an average customer generates before churning.

Formula:

sqlCopyEditAverage Revenue per User (ARPU) x Average Customer Lifespan

Why it matters: Tells you how much you can afford to spend on acquisition—and what segments are most valuable to keep.

8. 📮 Net Promoter Score (NPS)

NPS doesn’t directly measure retention, but it shows user sentiment—a leading indicator of loyalty.

Ask:

“How likely are you to recommend us to a friend or colleague?” (0–10)

Why it matters: Detractors are more likely to churn. Promoters are more likely to stay and advocate.

🧠 Bonus: Activation Rate

How many new users successfully complete the key onboarding actions?

If your activation rate is low, you’re losing people before they ever really start.

✅ Final Thoughts

Retention isn’t a single number—it’s a collection of signals. The best retention strategies are built on:

• Behavioral data (usage patterns)
• Emotional data (feedback & NPS)
• Monetary data (CLTV, churn, revenue)

By tracking the right metrics, you’ll not only reduce churn—you’ll understand what makes users stay, love, and grow with your product.