Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
When I started working with B2B SaaS clients as a freelance consultant, everyone was obsessed with the same activation metrics. Daily active users, feature adoption rates, trial-to-paid conversion - the usual suspects that every SaaS dashboard displays.
But here's what I discovered after diving deep into multiple client projects: most teams are measuring the wrong things. They're tracking metrics that look impressive in board meetings but don't actually predict which users will stick around and pay.
The breakthrough came when I was working with a B2B SaaS client whose activation strategy looked solid on paper. Multiple channels, decent traffic, trial signups coming in. But something was fundamentally broken in their conversion funnel - and it wasn't what the traditional metrics were showing.
Here's what you'll learn from my experience:
Why traditional activation metrics often mislead product teams
The counterintuitive approach that actually predicts user retention
My specific framework for measuring activation that drives revenue
How to spot the difference between engaged users and tire-kickers
The one metric that changed how my clients think about onboarding optimization
Industry Reality
What every product team tracks (but shouldn't)
Walk into any SaaS company, and you'll find product teams religiously tracking the same set of activation metrics. It's like a standardized playbook that everyone follows without questioning.
The Standard Activation Metrics Everyone Uses:
Time to First Value - How quickly users complete key actions
Feature Adoption Rate - Percentage of users who try core features
Onboarding Completion Rate - Users who finish the setup flow
Daily/Weekly Active Users - Login frequency and session duration
Trial-to-Paid Conversion - The holy grail percentage
This conventional wisdom exists because it's easy to measure and fits nicely into analytics dashboards. Product managers love metrics they can track, visualize, and present to stakeholders. These numbers give the illusion of control and progress.
But here's where it falls apart: These metrics measure activity, not commitment. They tell you what users are doing, but not whether they're actually getting value from your product. A user can complete your onboarding, try multiple features, and still churn after their trial expires.
The real problem is that these metrics are lagging indicators. By the time you see poor activation numbers, users have already decided your product isn't worth paying for. You're measuring the symptoms, not diagnosing the disease.
Most teams end up optimizing for vanity metrics that make dashboards look good but don't actually correlate with long-term retention or revenue growth.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
This insight hit me hard during a project with a B2B SaaS client who was struggling with conversions. On the surface, their metrics looked decent - they had reasonable trial signup rates, users were completing onboarding at industry-standard rates, and feature adoption seemed healthy.
But when I dug deeper into their analytics, I found a classic case of misleading data. The dashboard showed tons of "direct" conversions with no clear attribution. Users were engaging with multiple features during their trial period, but something was still broken in the conversion funnel.
Here's what made this situation unique: The company had strong personal branding efforts from the founder on LinkedIn. While everyone was focused on optimizing in-app metrics, the real activation was happening outside the product entirely.
After analyzing user behavior patterns more carefully, I noticed a critical distinction:
Cold users (from ads and SEO) typically used the service only on their first day, then abandoned it - despite "activating" according to traditional metrics
Warm leads (from LinkedIn personal branding) showed much stronger engagement patterns and higher conversion rates
This was my "aha" moment: We were treating SaaS activation like an e-commerce conversion when it's actually a trust-based service adoption. You're not selling a one-time purchase; you're asking someone to integrate your solution into their daily workflow.
The traditional activation metrics completely missed this trust component. A user could check every activation box and still not trust the product enough to pay for it.
Here's my playbook
What I ended up doing and the results.
Based on this revelation, I developed a completely different approach to measuring activation that focuses on commitment signals rather than activity metrics.
The Trust-Based Activation Framework:
1. Investment Depth Over Feature Breadth
Instead of tracking how many features users try, I measure how deeply they invest in core workflows. Are they just browsing, or are they actually setting up processes that matter to their business?
Key metrics:
Data input volume (how much information they're willing to enter)
Configuration complexity (advanced settings they customize)
Integration attempts (connecting to their existing tools)
2. Behavioral Consistency Patterns
Rather than measuring daily active users, I track return intent signals - actions that indicate a user plans to come back because they've found value.
Key indicators:
Notification preferences setup
Team member invitations sent
Scheduled actions or recurring processes created
Export/download behavior (taking data out means they value it)
3. Value Realization Timing
Instead of measuring time to first action, I track time to first "wow moment" - when users achieve something meaningful for their business.
How to identify this:
Survey users about their primary use case during onboarding
Track when they complete workflows related to that use case
Measure success metrics specific to their goals, not your product features
4. Trust Signal Accumulation
This was the biggest game-changer. I started measuring how users demonstrate increasing trust in the product over time.
Trust progression indicators:
Moving from test data to real business data
Increasing session duration and return frequency
Sharing product results externally (exports, screenshots, reports)
Upgrading trial limitations before being prompted
Value Discovery
Track when users achieve their primary goal, not when they click through features
Commitment Depth
Measure how much users invest in setup vs. how many features they browse
Trust Progression
Monitor increasing confidence signals rather than basic engagement metrics
Retention Predictors
Focus on actions that predict long-term usage rather than short-term activity
The results of this approach were eye-opening. Instead of optimizing for vanity metrics, my clients started focusing on the signals that actually mattered for their business.
What Changed:
Product teams stopped celebrating high trial signup numbers when conversion rates remained low
Customer success teams could identify at-risk users earlier based on commitment signals
Marketing teams focused on attracting users who were more likely to find value, not just any users
The most significant change was in how teams thought about their product. Instead of building features to improve traditional activation metrics, they started building experiences that increased user investment and trust.
This shift led to better product-market fit conversations, more targeted feature development, and ultimately higher conversion rates from trial to paid subscriptions.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The Five Key Lessons from Rethinking Activation Metrics:
Activity ≠ Engagement - A user can be very active and still have zero intention of paying. Look for commitment signals instead.
Trust Takes Time - SaaS activation isn't a moment, it's a process. You're measuring trust-building, not feature adoption.
Context Matters More Than Features - Users activate based on their specific use case, not your product's capabilities. Measure success relative to their goals.
Leading Indicators Beat Lagging Ones - Focus on early signals that predict long-term behavior rather than waiting for conversion data.
Depth Beats Breadth - One user deeply invested in a core workflow is worth more than ten users casually trying multiple features.
What I'd Do Differently:
I'd implement commitment tracking from day one instead of retrofitting it later. Most teams wait until they have conversion problems to question their metrics, but the insights from trust-based measurement can guide better product decisions from the start.
When This Approach Works Best: Complex B2B products where users need to integrate your solution into existing workflows. When This Doesn't Work: Simple consumer apps with obvious immediate value.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this activation framework:
Start measuring user investment depth during onboarding
Track integration attempts and data input volume
Monitor team collaboration signals early
Focus on value realization timing over feature adoption
For your Ecommerce store
For ecommerce stores applying these principles:
Measure account setup completion beyond just registration
Track wishlist creation and sharing behavior
Monitor return visit patterns and browsing depth
Focus on purchase intent signals over traffic metrics