How to Build an LTV Analysis in Amplitude
Lifetime value is the single most important metric for deciding how much to spend acquiring a customer. If you do not know your LTV, you are guessing on CAC budgets, channel allocation, and pricing strategy. Amplitude gives you the behavioral data to compute LTV not just as a static formula but as a dynamic, segment-aware metric that updates as usage patterns evolve.
Most LTV calculations rely on a spreadsheet average: total revenue divided by total customers over some period. That number is nearly useless because it hides the variance between segments. This guide walks through building an LTV analysis in Amplitude that segments by acquisition channel, plan tier, and behavioral cohort so you can see which customers are actually worth pursuing.
Step-by-step guide
Define and instrument your revenue events
Before building any chart, ensure your Amplitude instrumentation includes revenue events with accurate dollar values. The most common events are subscription_started, subscription_renewed, plan_upgraded, and one-time purchase_completed. Each event should carry properties for revenue amount, currency, plan tier, and billing interval. Without clean revenue data, LTV analysis is unreliable.
- Verify that all revenue events include a revenue property with the dollar amount
- Add plan tier and billing interval as event properties for segmentation
- Backfill historical revenue events if your instrumentation was added recently
Create a Revenue LTV chart using Amplitude's built-in feature
Navigate to Create > Revenue LTV in Amplitude. Select your primary revenue event (typically subscription_renewed or purchase_completed) and set the time range to at least 12 months to capture meaningful lifecycle data. Amplitude will compute the cumulative revenue per user over time, showing you how LTV accrues by day, week, or month since first event.
Segment LTV by acquisition cohort
Add a segment breakdown by the user property that captures acquisition source — this might be utm_source, referral_channel, or a custom first_touch_channel property. The chart will split into separate LTV curves for each channel, revealing which acquisition sources produce customers with the highest long-term value. Organic search customers often have dramatically different LTV curves than paid social.
Build a retention-based LTV model for early prediction
Create a Retention Analysis chart with your core activation event and revenue event. The retention curve shows what percentage of users generate revenue in month 1, month 2, and so on. Multiply each month's retention rate by the average revenue per active user to build a predictive LTV curve that estimates total value before you have 12 months of data for a new cohort.
Identify behavioral predictors of high LTV
Use Amplitude's Compass feature or build a funnel analysis to find the actions that correlate with high lifetime value. Common predictors include completing onboarding within the first session, using a specific power feature in the first week, or inviting a team member within 48 hours. These behavioral signals let you predict LTV early and focus retention efforts on at-risk users.
- Run a Compass analysis targeting 90-day retention as the outcome
- Identify the top 3-5 behavioral correlates within the first 7 days
- Create a behavioral cohort for users who complete those actions
Set up LTV dashboards for ongoing monitoring
Create a dashboard that combines your LTV chart, LTV by acquisition channel, and the behavioral predictor funnels. Add a cohort comparison showing this quarter's users vs. last quarter's so you can spot LTV trends early. Share this dashboard with your growth and finance teams so LTV data is accessible without requiring analysts to pull it on demand.
Common mistakes
Using average LTV without segmentation
A blended LTV number is misleading if your customer base includes very different segments. Enterprise customers might have 10x the LTV of self-serve users, but an average hides that. Always segment LTV by at least plan tier and acquisition channel to get actionable numbers.
Computing LTV from too short a time window
If your product has an annual billing cycle and you compute LTV from 3 months of data, you are dramatically underestimating value. Use the longest time window available and supplement with retention-based projection models for newer cohorts.
Ignoring churn in the LTV calculation
LTV is not just revenue while active — it must account for churn probability. A customer generating $100/month with 10% monthly churn has a very different LTV than one generating $80/month with 3% churn. Factor retention rates into your model.
Not connecting LTV back to acquisition spend
LTV is only useful when compared to CAC. If you build an LTV dashboard but never combine it with acquisition cost data, you cannot make the payback period and ROI decisions that LTV is meant to inform. Add a CAC:LTV ratio chart to your dashboard.
Tips
Use Amplitude's Revenue LTV chart type rather than building a manual formula — it handles the cumulative calculation and cohort alignment automatically.
Create a saved cohort of high-LTV users (top 20% by revenue) and analyze their behavioral patterns to find replicable activation strategies.
Export LTV by cohort data monthly into a spreadsheet to track whether new cohorts are trending above or below historical baselines.
If your product has a free tier, compute LTV from the moment of first paid conversion, not signup, to avoid diluting the metric with free users who never convert.
How Vantage helps
Vantage connects analytics context directly to your product planning. When you build features aimed at improving LTV, Vantage can pull in your Amplitude data during PRD generation so that requirements are grounded in actual user behavior rather than assumptions about what drives retention.