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Your PostHog product analytics dashboard, grounded in every event.

DAU, MAU, activation rate, feature adoption, and session depth modeled from your PostHog events, persons, and cohorts, enriched with Amplitude for cross-tool coverage, so the engagement numbers tie back to the product.

See how to build one in Definite

What’s in a posthog product analytics dashboard?

A product analytics dashboard modeled on PostHog events, persons, cohorts, and feature flags. Each metric — DAU, MAU, DAU/MAU ratio, activation rate, feature adoption — is derived from PostHog's event stream, governed in the warehouse so there is one definition of engagement across your PostHog project.

PostHog gives you insights, trends, and funnels, but the engagement picture fragments across saved insights, cohorts, and feature-flag reports. When DAU, MAU, and activation come from one governed model on the same PostHog event data, you catch the adoption drop in one view instead of clicking between dashboards.

Who it’s forProduct leaders, PMs, and growth teams who use PostHog and own the engagement and adoption numbers.

CadenceRefreshed daily; reviewed in the weekly product review and before roadmap planning.

Built fromPostHog, Amplitude

§ How it works

Describe your dashboard. Fi builds it.

Fi is the AI agent inside Definite. Tell it what you’re trying to understand, and it connects your sources, defines the metrics, and builds the dashboard. One conversation, not a project.

You

Show me product engagement from our PostHog data — DAU, MAU, activation rate, feature adoption, and session depth, tied to the events and persons.

✦ Fi

Here's your posthog product analytics dashboard, on your PostHog and Amplitude data.

Here’s what’s in it

The top row leads with the 4 numbers that matter most: DAU, MAU, DAU/MAU ratio, Activation rate. Each shows a delta versus the prior period so you can see direction at a glance. Below that, 2 trend charts (MAU over time, Feature adoption trend) show how the headline numbers have moved over time. A breakdown (DAU by segment) splits the metric by dimension so you can see what's driving the total. A detail table (Engagement summary) rounds it out with the secondary metrics and their deltas. Every number is computed from the exact formulas shown in the metric table below. Composites are derived from their components, not pasted in, so the KPI tiles, breakdowns, and totals all reconcile to each other.

Illustrative data

DAU

3.6K▼ 2.4%

Data ▾

Period	DAU
Jan	2.8K
Feb	2.3K
Mar	2.6K
Apr	2.7K
May	3.4K
Jun	3.9K
Jul	3.6K
Aug	3.8K
Sep	4.2K
Oct	4.3K
Nov	3.7K
Dec	3.6K

MAU

25.9K▲ 5.1%

Data ▾

Period	MAU
Jan	12.8K
Feb	15.0K
Mar	17.9K
Apr	16.0K
May	18.0K
Jun	17.7K
Jul	18.5K
Aug	18.4K
Sep	20.6K
Oct	22.7K
Nov	24.7K
Dec	25.9K

DAU/MAU ratio

13.9%▼ 7.2%

Data ▾

Period	DAU/MAU Ratio
Jan	22.1%
Feb	15.5%
Mar	14.5%
Apr	17.2%
May	18.6%
Jun	21.8%
Jul	19.3%
Aug	20.5%
Sep	20.2%
Oct	18.8%
Nov	15.0%
Dec	13.9%

Activation rate

5381.4%▼ 5.7%

Data ▾

Period	Activation Rate
Jan	4676.0%
Feb	4459.8%
Mar	4115.6%
Apr	4789.8%
May	4687.2%
Jun	4234.3%
Jul	4722.6%
Aug	4514.7%
Sep	5260.9%
Oct	5048.3%
Nov	5707.0%
Dec	5381.4%

MAU over time

Data ▾

Period	MAU
Jan	12.8K
Feb	15.0K
Mar	17.9K
Apr	16.0K
May	18.0K
Jun	17.7K
Jul	18.5K
Aug	18.4K
Sep	20.6K
Oct	22.7K
Nov	24.7K
Dec	25.9K

DAU by segment

Data ▾

Segment	DAU
Enterprise	1.6K
Mid-Market	751
SMB	1.3K

Feature adoption trend

Data ▾

Period	Feature Adoption
Jan	3389.5%
Feb	3804.8%
Mar	3564.2%
Apr	3686.4%
May	4179.5%
Jun	4411.9%
Jul	4559.7%
Aug	3872.2%
Sep	5121.7%
Oct	5102.3%
Nov	5407.8%
Dec	4989.5%

Engagement summary

DAU/MAU Ratio13.9%▼ 7.2%

Activation Rate5381.4%▼ 5.7%

Avg Session Duration17▲ 23.8%

✦ Fi

Anything else I can do for you?

You

Why did DAU/MAU ratio drop this month?Which feature event has the highest adoption, and which is being ignored?Trace this month's DAU back to the events in PostHog.What is activation rate for the PostHog cohort that churns the most?Break MAU out by plan tier instead of the total.Which session-depth patterns in the first week predict expansion versus churn?Break DAU by PostHog cohort so I can see which user segment is most engaged.Add feature adoption by event name and flag anything under 20%.Show activation rate trend by PostHog cohort over the past six months.

Schedule a demo →Start this conversation with your data →

First version in a dayFree to start, flat pricingNo infrastructure to set up

§ Why the numbers tie out

Every metric traces back to your systems

This is the part a BI tool can’t fake. Each metric is defined once, in your warehouse, from a specific object in a specific source. Change the definition in one place and every tile, report, and answer moves with it. So the number on the screen is the number in the source.

PostHog

◆ EventDAUMAUDAU/MAU RatioActivation RateFeature Adoption

◆ Feature FlagFeature Adoption

Amplitude

◆ EventDAUMAUDAU/MAU RatioActivation RateFeature Adoption

◆ SessionDAUMAUDAU/MAU RatioAvg Session Duration

Metric	What it measures	How it's calculated	Sources
DAU/MAU Ratio	—	DAU ÷ MAU	PostHog, Amplitude

§ Then do something about it

Have our agent watch for you

A posthog product analytics dashboard tells you what happened, and Fi tells you why. The last step is not having to remember to check. Point Definite at the one number you can’t afford to miss, and it watches that number for you off the same definitions as your dashboard. When it moves, you hear about it before the next review instead of during it. One metric, one action, always reversible.

See how autonomous agents work →

Agents for this stack

PostHog agent →

Amplitude agent →

Autonomous agent · watch churn

Watch

A metric you choose

net revenue churn

→

Judge

One condition

> 5% week-over-week

→

Act

One action

alert #revenue + open doc

◄──── then waits · cooldown 24h before it can act again ────

Scoped to a single metric and a single action. You arm it; you can disarm it anytime.

§ The data that powers it

Built from whatever you already run on

Connect the systems you already use. Any source of these types works, and you don’t move data into a warehouse, because Definite is the warehouse.

Product Analytics14

Amplitude

PostHog

Google Analytics (GA4)

No warehouse to stand up or connect. See how the platform models your data →

§ Get started

Build your posthog product analytics dashboard

From signup to a working dashboard in one sitting. No data team required.

Sign up

Free to start. No credit card, no infrastructure to set up.

Create your account →

Connect your sources

Stripe, your CRM, accounting. Definite syncs and models them automatically.

Decide your metrics

Pick the numbers that matter or let Fi propose them from your data. Every metric gets one definition, governed in one place.

Ask Fi to build it

Describe what you need in plain language. Fi builds the dashboard, and you refine by asking follow-ups.

§ FAQ

Common questions

Usually because PostHog defines an active user by identified persons with events, while downstream BI queries may add session-length or property-based thresholds. The reconciliation map above shows which PostHog event and person objects each metric comes from, so there is one governed definition of DAU and MAU in the warehouse.

PostHog for events, persons, cohorts, and feature flags. Amplitude is included as a complement for teams that run both tools. Definite syncs your PostHog project data and models the engagement metrics.

It is a live ECharts dashboard running on a deterministic synthetic dataset, labeled illustrative. DAU/MAU ratio is computed from DAU and MAU by the formula in the metric table. Connect PostHog and Fi builds the same view from your event data.

Type a prompt like the one above. Fi connects your PostHog project, models the engagement metrics from events and persons, and you refine by asking follow-ups. The first version ties engagement to the event data without a SQL query.

Yes. PostHog feature flags sync as dimensions, so you can break DAU, MAU, and activation rate by any flag variant. Ask Fi to add a feature-flag dimension to any tile to see engagement by rollout group.