About the Growbook
In SaaS, product usage is never uniform. Some users adopt critical features quickly. Others get stuck or never activate key functionality.
This Growbook shows you how to uncover the features your best users adopt and how to use that knowledge to build smarter onboarding, trigger personalized nudges, and inform better product roadmap decisions.
Weâll use Otto, a demo project management SaaS platform, as our example.
TL;DR
Track and define core feature usage events (âcreated ml taskâ, âinvite team members', âcreated an objectâ, and âvisited dashboardâ)
Build Insights reports to measure adoption across cohorts
Break down behavior by âsubscriptionâ, âcompany sizeâ, or âregionâ to spot trends
Create real-time segments like âDashboard Adoptersâ or âInactive Usersâ
Launch onboarding journeys tailored to feature usage (or lack of it)
Benefits
Map adoption by cohort: See which features are used by your most engaged and valuable users.
Spot activation bottlenecks: Identify features that are not activated by users that stall or churn.
Personalize onboarding: Trigger nudges based on what users have or havenât tried.
Align teams around real usage: Give product, marketing, and success a shared view of what users actually do â so everyone works from the same source of truth.
How It Works
Step 1: Set up tracking for user activity
Install Intemptâs JavaScript SDK:
Integrate Intemptâs JavaScript SDK into your product to begin capturing behavioral events.
Follow the JavaScript SDK integration guide to ensure proper setup.
Step 2: Define the events you want to track
In GrowthOS, go to Events and verify that the following key feature events are being tracked: âcreated ml taskâ, âinvite team membersâ, âcreated an objectâ, and âvisited dashboardâ.
These represent meaningful actions across different levels of product adoption.
You can also define new events if needed, like âscheduled sprintâ, âjoined workspaceâ, and âexported reportâ.
Step 3: Build your Insights report
Navigate to Analytics - âInsightsâ and create a new report.
Choose a feature usage event to analyze, for example, âvisited dashboardâ
âSelect a metric such as:
Count of users who triggered the event
Average frequency per user
% of active users performing the action
Step 4: Break down usage by user attributes
Use the âbreakdownâ field to compare behavior across:
Plan tier (e.g., Free vs Pro)
Company size
Industry
Apply filters to narrow your scope:
Date range (e.g., last 30 days)
Region (e.g., North America)
Account type (e.g., trial vs paid)
Spot behavior patterns
You might notice:
Dashboard usage is 5x higher among Premium plan users.
Smaller teams rely heavily on âinvite team membersâ, while larger teams donât.
Healthcare companies engage early with ML task creation.
These behavioral patterns help you identify cohorts not based on who they are, but what they actually do.
Step 5: Turn insights into segments
Based on what you discover, create real-time segments like:
âDashboard Adoptersâ: users who visited the dashboard 3+ times in the first 7 days
âHigh Value Onboardersâ: users who triggered 3+ product features in week one
âInactive Usersâ: users who signed up but didnât trigger âexported reportâ
Step 6: Trigger targeted journeys
Use these segments to trigger targeted journeys:
Encourage âHigh Value Onboardersâ to explore automation features
Re-engage âInactive Usersâ with reminders or walkthroughs
Offer additional features to âDashboard Adoptersâ for more activation
Note: for a detailed walkthrough about creating a Journey, check out the Growbook on Personalized Category Upsell
Step 7: Measure and optimize
Continue tracking feature usage over time using updated Insights Reports.
Compare adoption before and after journeys are launched.
Identify which cohorts respond best and evolve your strategy based on what actually works.