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Your product analytics dashboard, grounded in user behavior.

DAU, MAU, activation rate, feature adoption, and session depth in one view, modeled from your product analytics and CRM, so the engagement numbers tie back to what drives retention and expansion.

See how to build one in Definite

What’s in a product analytics dashboard?

A product analytics dashboard is the single governed view of how users engage with a product: daily and monthly active users, the ratio between them, activation rate, feature adoption, and session depth. The version worth building roadmap decisions on is grounded in the event data from product analytics tools, modeled into governed definitions, so the engagement you measure is the engagement that happened.

Product engagement gets measured differently in every tool. Amplitude counts an active user one way, PostHog another, and a BI query adds a third definition. When DAU, MAU, and activation come from one governed set of definitions modeled on the actual event data, you know what users are doing, and you catch the engagement drop before it becomes a retention problem.

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

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

Built fromAmplitude, PostHog, Pendo, Hubspot

§ 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

I need to see how users are engaging with the product: DAU, MAU, activation, feature adoption, and session depth, tied to the event data.

✦ Fi

Here's your product analytics dashboard, on your Amplitude, PostHog, Pendo and Hubspot 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

4.1K▲ 0.6%

Data ▾

Period	DAU
Jan	2.9K
Feb	3.0K
Mar	3.6K
Apr	2.8K
May	3.4K
Jun	3.1K
Jul	3.2K
Aug	3.2K
Sep	4.1K
Oct	4.2K
Nov	4.1K
Dec	4.1K

MAU

25.8K▲ 11.8%

Data ▾

Period	MAU
Jan	13.0K
Feb	16.7K
Mar	17.4K
Apr	18.2K
May	16.2K
Jun	19.6K
Jul	17.2K
Aug	19.6K
Sep	19.0K
Oct	21.4K
Nov	23.1K
Dec	25.8K

DAU/MAU ratio

15.9%▼ 10.1%

Data ▾

Period	DAU/MAU Ratio
Jan	22.4%
Feb	18.0%
Mar	20.7%
Apr	15.3%
May	21.1%
Jun	15.7%
Jul	18.6%
Aug	16.4%
Sep	21.7%
Oct	19.6%
Nov	17.7%
Dec	15.9%

Activation rate

4835.0%▼ 11.5%

Data ▾

Period	Activation Rate
Jan	4129.1%
Feb	4265.8%
Mar	4936.5%
Apr	4882.5%
May	4319.8%
Jun	4997.1%
Jul	4491.7%
Aug	4518.9%
Sep	4801.0%
Oct	4575.4%
Nov	5463.9%
Dec	4835.0%

MAU over time

Data ▾

Period	MAU
Jan	13.0K
Feb	16.7K
Mar	17.4K
Apr	18.2K
May	16.2K
Jun	19.6K
Jul	17.2K
Aug	19.6K
Sep	19.0K
Oct	21.4K
Nov	23.1K
Dec	25.8K

DAU by segment

Data ▾

Segment	DAU
Enterprise	1.8K
Mid-Market	1.1K
SMB	1.2K

Feature adoption trend

Data ▾

Period	Feature Adoption
Jan	3719.8%
Feb	3631.3%
Mar	3737.0%
Apr	4305.3%
May	3730.1%
Jun	4141.5%
Jul	3786.2%
Aug	4602.8%
Sep	4130.1%
Oct	4922.7%
Nov	4605.8%
Dec	4422.9%

Engagement summary

DAU/MAU Ratio15.9%▼ 10.1%

Activation Rate4835.0%▼ 11.5%

Avg Session Duration14▼ 15.3%

✦ Fi

Anything else I can do for you?

You

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

Amplitude

◆ EventDAUMAUDAU/MAU RatioActivation RateFeature Adoption

◆ SessionDAUMAUDAU/MAU RatioAvg Session Duration

PostHog

◆ EventDAUMAUDAU/MAU RatioActivation RateFeature Adoption

◆ Feature FlagFeature Adoption

Pendo

◆ EventDAUMAUDAU/MAU RatioActivation RateFeature Adoption

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

§ Then do something about it

Have our agent watch for you

A 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

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

Pendo

Google Analytics (GA4)

CRM15

Microsoft Dynamics Business Central

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

§ Get started

Build your 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 each analytics tool defines an 'active user' differently. Amplitude might count any event, PostHog might count only identified users, and a BI query might add session-length thresholds. The reconciliation map above shows which event objects each metric comes from, so there is one definition of DAU and MAU, governed in your warehouse.

Product analytics tools (Amplitude, PostHog, Pendo) for events, sessions, and feature interactions, and your CRM (HubSpot) for account segments so you can break engagement by customer tier. Definite syncs and models all of them.

It depends on the product category. Messaging apps target 50%+, SaaS tools are healthy at 15-25%. What matters more than the benchmark is the trend and whether it varies by segment. This dashboard shows both.

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 your analytics tools and Fi builds the same view from your event data.

Type a prompt like the one above. Fi connects your product analytics tools and CRM, models the engagement metrics, and you refine by asking follow-ups. The first version ties engagement to the event data without a SQL query.