How is Vantage different from ChatGPT or Claude?

ChatGPT and Claude are great for one-off generation. Vantage is a full workspace. It connects your tools, tracks dependencies across projects, catches conflicts between products, and surfaces what needs your attention every morning. It also remembers every past decision, so a new PM can query across all projects and get cited answers in seconds.

Do I have to switch away from Notion or Linear?

No. You keep all your tools. Vantage connects to them and builds intelligence on top. When you're ready, you can push tickets to Linear or Jira. But the value starts before you create anything — just by connecting your tools and seeing what the intelligence layer surfaces.

What happens on day one?

You connect your tools — GitHub, Linear, Slack — 2 minutes each. Vantage indexes your existing data overnight. By the next morning, you see your first decision queue: what's stale, what conflicts, what changed. No manual setup. No templates to fill out.

What if I only manage one project?

Vantage works for single projects, but it shines when you manage 3–5 or more. Conflict detection, cross-project memory, and the decision queue are most valuable when you have multiple projects that need to stay in sync. If you manage one project, start with the free tier and see if the cited query engine alone saves you time.

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How does Vantage keep documents and tickets up to date?

When context shifts, you add the new information and Vantage rebuilds every connected deliverable. Documents, prototypes, and tickets all update. You review the changes, accept or reject each one, and approve. Nothing updates silently, and every change is traceable.

How do I get started?

Sign up free at app.vantageos.tech. Connect your tools, and generate your first grounded document within minutes. No credit card required. Upgrade when you need more projects or features.

What is the difference between a cohort and a segment in Amplitude?

A segment is a group of users defined by current properties (e.g., plan = pro). A cohort is a group defined by behavior within a time window (e.g., users who signed up in January, or users who completed 5 sessions in their first week). Cohorts capture a point-in-time grouping that you track over time; segments are dynamic and change as user properties change.

How many weeks of data do I need for meaningful cohort analysis?

You need at least 8-12 weeks of data to see retention curves flatten and draw meaningful conclusions. With fewer weeks, recent cohorts have not matured enough to compare fairly against older ones. For behavioral cohorts, you need enough users in each group (at least 100-200) to avoid sample size issues.

Can I build cohort analyses without Amplitude?

Yes, but it requires more manual work. You can build cohort analyses in SQL using your data warehouse, in Google Sheets with exported data, or in tools like Mixpanel and PostHog that offer similar features. Amplitude's advantage is the no-code cohort builder and built-in visualization that makes the analysis accessible to PMs without SQL skills.

How-To2026-09-0311 min read

How to Build a Cohort Analysis in Amplitude

Aggregate metrics hide the truth about your product's health. An overall 30-day retention rate of 40% might mean every cohort retains at exactly 40%, or it might mean early cohorts retain at 60% while recent cohorts retain at 20% — the same number with completely different implications. Cohort analysis reveals these patterns by grouping users based on when they joined or what they did, then tracking how their behavior evolves over time.

Amplitude's cohort analysis tools let you define cohorts by acquisition date, behavior, user properties, or any combination, then compare how these groups engage, retain, and convert. This guide shows you how to build meaningful cohort analyses that drive product decisions rather than just generating interesting charts.

Step-by-step guide

Define the cohort dimension and the metric you want to compare

Decide what you are comparing and how. The most common cohort dimension is signup week — grouping users by when they joined to see if retention is improving or declining over time. But you can also cohort by behavior (users who completed onboarding vs. those who did not), acquisition source, plan type, or any user property. Pair the cohort dimension with a specific metric: retention rate, feature adoption rate, or conversion rate.

Choose your cohort dimension: time-based (signup week/month) or behavior-based (action completed)
Select your measurement metric: retention, feature adoption, revenue, or conversion
Define the observation window — how many weeks or months you will track each cohort

Create a time-based retention cohort in Amplitude

Navigate to the Retention Analysis chart in Amplitude. Set the 'Starting Event' to signup_completed and the 'Return Event' to any core engagement event. Set the cohort grouping to 'By week' to create weekly signup cohorts. Amplitude will generate a retention table showing the percentage of each weekly cohort that returned in week 1, week 2, week 3, and beyond.

Select signup_completed as the starting event that defines when users enter each cohort
Choose your most meaningful engagement event as the return event — not just login, but a value-delivering action
Set the retention type to 'Unbounded' for overall return rate or 'N-day' for day-specific retention curves

Build a behavioral cohort to compare user segments

Go to the Cohorts section in Amplitude and create a new behavioral cohort. Define the cohort by a specific action — for example, 'users who invited at least one teammate within 7 days of signup.' Create a comparison cohort of users who did not complete that action. Apply both cohorts to a retention chart to see the dramatic difference in retention between the two groups.

Define the behavioral cohort using the cohort builder: performed [event] [N] times in [time window]
Create the complementary cohort: users who did NOT perform the same event in the same window
Apply both cohorts as segments on the same retention or engagement chart for side-by-side comparison

Analyze cohort curves to find retention drivers

Compare the retention curves of your behavioral cohorts. If users who completed onboarding retain at 50% after 8 weeks while those who skipped it retain at 15%, onboarding completion is a strong retention driver. Look for the 'flattening point' in each cohort's curve — the week where retention stabilizes — and compare where it flattens and at what level across segments.

Identify which behavioral cohorts have the highest retention floor (the level where the curve flattens)
Calculate the absolute retention difference between cohorts at key time points (week 1, week 4, week 8)
Rank behavioral cohort differences by magnitude to prioritize which behaviors to drive in onboarding

Create a cohort comparison dashboard

Build a dashboard in Amplitude that shows your key cohort comparisons at a glance. Include a weekly cohort retention table to track retention trends over time, a behavioral cohort comparison chart showing your top retention drivers, and a conversion funnel segmented by your most impactful cohort dimensions. This dashboard becomes the reference for product strategy discussions about retention and engagement.

Add a retention table showing the last 12 weekly signup cohorts with color-coded cells
Include a behavioral cohort comparison chart with your top 3 retention-driving behaviors
Add a trend line showing the week-4 retention rate for each signup cohort over the last 6 months

Act on cohort insights by targeting interventions

Sync your behavioral cohorts to your engagement tools (email, push notifications, in-app messaging) to target interventions at users who have not yet completed the retention-driving behavior. For example, if completing onboarding is the strongest retention driver, sync the 'signed up but did not complete onboarding within 48 hours' cohort to your email tool and trigger a re-engagement campaign automatically.

Sync at-risk cohorts to Braze, Iterable, or your messaging platform via Amplitude's integrations
Create targeted interventions for each retention-driving behavior that at-risk users have not completed
Track whether the intervention improves the behavioral cohort distribution over subsequent weeks

Common mistakes

Comparing cohorts of very different sizes

A cohort of 50 users behaving differently from a cohort of 5,000 might just be noise. Always check cohort sizes and apply minimum sample size thresholds before drawing conclusions. Amplitude shows cohort sizes in the retention table — use them.

Using login as the return event for retention

Login does not equal engagement. A user who logs in to check their billing page and then leaves is counted as retained. Use a value-delivering event (created a report, sent a message, completed an analysis) as your return event to measure meaningful retention.

Ignoring the maturation period of recent cohorts

The most recent weekly cohort always looks bad because users have not had enough time to return. Do not panic about the latest cohort's low numbers — compare cohorts at the same maturity point (e.g., compare all cohorts at their week-4 mark, not their current mark).

Tips

Use Amplitude's Compass feature to automatically identify the events most correlated with retention, then build cohorts around those events

Create a monthly email report from your cohort dashboard and send it to the product leadership team so retention trends stay visible

Combine time-based and behavioral cohorts — for example, track onboarding completion rate for each weekly signup cohort to see if product changes improve activation over time

Export cohort data to a spreadsheet for custom analyses like LTV by cohort or payback period by acquisition source

How Vantage helps

Vantage helps PMs turn cohort analysis insights into product changes. When your Amplitude data reveals that a specific behavior drives retention, Vantage captures that insight as a requirement and generates the implementation plan to make that behavior easier, more discoverable, or part of the default onboarding flow.