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.

How do you create user segments in Amplitude?

Go to Cohorts > New Cohort and choose between property-based (filtering by user properties like plan type or role) and behavioral (filtering by events performed with frequency and recency conditions). Combine both types for precision segments like 'Enterprise users who performed core action 5+ times this week.' Save and name each cohort for reuse across all Amplitude reports.

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

In Amplitude, a cohort is the saved definition of a user group that you can apply across reports. A segment is a broader concept — any way of dividing your user base. Cohorts are the mechanism; segments are the concept. You create cohorts to operationalize your segmentation strategy. The terms are often used interchangeably.

How many user segments should a product team track?

Track five to ten primary segments that cover your key dimensions: plan/pricing tier, lifecycle stage (new, activated, mature, churned), and engagement level (power user, regular, casual, dormant). Add compound segments sparingly for specific investigations. More than twenty active segments creates analysis paralysis.

How-To2026-09-0310 min read

How to Set Up User Segmentation in Amplitude

Treating all users as one group is the fastest way to build a product that works for nobody. Power users, casual visitors, enterprise admins, and trial users all have different needs, behaviors, and value to your business. Segmentation is the practice of defining these groups so you can analyze them separately and make targeted product decisions.

Amplitude's segmentation features — behavioral cohorts, user properties, and computed properties — let you define segments based on what users do, not just who they are. This guide covers creating segments from both demographic properties and behavioral patterns, combining them for precision targeting, and using them across Amplitude's analysis tools to drive product decisions.

Step-by-step guide

Audit your user properties and event taxonomy

Before creating segments, review what data you have. In Amplitude, go to Data > User Properties to see all available demographic and firmographic properties (plan type, company size, role, signup date). Then check Events for behavioral data. Good segmentation requires both — properties tell you who users are, events tell you what they do.

Export your user properties list and flag which ones are reliably populated
Identify the five to ten events that best distinguish user types (e.g., 'Report Created' vs 'Report Viewed')
Note any gaps — if you do not track user role, you cannot segment by it until you add the property

Create property-based segments for firmographic analysis

Start with segments defined by user properties: plan type (Free, Pro, Enterprise), company size (1-10, 11-50, 51-200, 200+), user role (admin, member, viewer), and signup cohort (monthly). In Amplitude's Cohorts section, create a cohort for each segment. These are your baseline segments for comparing behavior across user types.

Go to Cohorts > New Cohort > Property-based
Create separate cohorts for each plan tier, company size band, and user role
Name them consistently (e.g., 'Segment: Enterprise Plan', 'Segment: SMB (1-50)')

Build behavioral cohorts based on usage patterns

Create cohorts based on what users do, not just who they are. Define a 'Power User' cohort (performed core action 10+ times in the last 7 days), a 'New Activated' cohort (signed up in the last 30 days AND completed onboarding), and an 'At Risk' cohort (was active 30 days ago but has not returned in 14 days). These behavioral segments capture the user's relationship with your product.

Create a 'Power User' cohort with a frequency filter on your core action event
Create a 'New Activated' cohort combining signup date and onboarding completion event
Create an 'At Risk' cohort using 'did X in past 30 days but not in past 14 days' logic

Combine property and behavioral segments for precision

The most actionable segments combine who and what. Create compound cohorts like 'Enterprise Power Users' (Enterprise plan AND core action 10+/week) and 'Free Trial At Risk' (Free plan AND signed up 10+ days ago AND not activated). These compound segments tell you exactly which user groups need attention and what intervention might work.

Use Amplitude's cohort builder to combine property and behavioral conditions with AND/OR logic
Start with your highest-value compound segments — typically 'best customers at risk' and 'trial users not converting'
Limit compound segments to 10-15 total to keep analysis manageable

Apply segments across Amplitude's analysis tools

Use your segments as filters and comparison groups in Segmentation charts, Funnels, Retention, and Pathfinder. Compare conversion rates between Power Users and casual users. Compare retention curves between Enterprise and SMB. Every analysis should be segmented — aggregate numbers hide the insights that matter most.

Add your cohorts as comparison groups in a Segmentation chart to see usage differences
Build a Funnel segmented by plan type to see where conversion drops for each tier
Run Retention analysis segmented by behavioral cohort to see which user types stick

Set up segment-based alerts and exports

Configure Amplitude alerts for when segment sizes change significantly — a shrinking Power User cohort or a growing At Risk cohort are early warning signals. Export high-value segments to your marketing tools (via Amplitude's integrations or CSV export) for targeted email campaigns, in-app messaging, or personalized onboarding flows.

Set up a weekly alert for changes in your At Risk cohort size
Configure a Slack webhook notification for when Power User count drops below a threshold
Set up a sync to export At Risk users to your CRM or email tool for outreach

Common mistakes

Creating too many segments that overlap and confuse analysis

If you have thirty segments and half of them overlap significantly, your analysis becomes noise. Start with five to ten mutually exclusive segments per dimension (plan type, lifecycle stage, engagement level). Add compound segments only when you have a specific question they answer.

Segmenting only by demographics and ignoring behavior

A segment of 'Enterprise users' tells you who but not what. Two enterprise accounts might have completely different usage patterns. Always layer behavioral conditions on top of demographic ones — 'Enterprise users who have not used the reporting feature' is actionable in a way that 'Enterprise users' is not.

Not updating segment definitions as the product evolves

Your 'Power User' definition from two years ago may not match today's product. If you added a major feature, power users now also use that feature regularly. Review and update cohort definitions quarterly to ensure they still reflect meaningful behavioral distinctions.

Tips

Name segments with a consistent prefix ('Segment:', 'Cohort:') so they are easy to find in Amplitude's cohort picker across all analysis types.

Create a 'Segmentation Map' document that lists every active segment, its definition, its owner, and when it was last validated — this prevents tribal knowledge.

Use Amplitude's 'Personas' feature (if available) to cluster users by behavior automatically and validate your manual segments against the ML-generated clusters.

Export segment membership lists monthly and compare — growing, shrinking, and stable segments each tell a different story about product health.

How Vantage helps

Vantage can ingest your Amplitude segment definitions as product context, so when generating a PRD for a feature targeting a specific user segment, the system understands the behavioral profile, size, and value of that audience. This means generated requirements are scoped to real users, not hypothetical personas.