Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
I used to obsess over the wrong numbers. When I was working with a B2B SaaS client, they were celebrating their "activation rate" hitting 80%. Users were completing their onboarding flow, checking all the boxes, and the team was patting themselves on the back.
Three months later, they were bleeding money. Most of those "activated" users hadn't touched the product since day one.
The problem? We were measuring completion, not value. We were tracking what users did instead of what they got. It's like measuring how many people walked through your store instead of how many actually bought something they loved.
Most SaaS founders are drowning in activation metrics that tell them nothing about actual product-market fit. They're optimizing for the wrong signals while their best customers slip through the cracks unnoticed.
Here's what you'll learn from my experience rebuilding activation tracking from scratch:
Why traditional activation metrics fail to predict retention
The counterintuitive approach that revealed our true growth drivers
How adding friction actually improved our "activation" (and revenue)
The single metric that predicted long-term success with 85% accuracy
Real frameworks you can implement without expensive analytics tools
This isn't another article about optimizing trial conversion. This is about measuring what actually matters for your business.
Industry reality
What every SaaS founder measures (and why it's wrong)
Walk into any SaaS company and you'll hear the same activation metrics being thrown around like gospel truth. The industry has collectively decided that activation equals completion.
Here's what most teams track:
Onboarding completion rate - How many users finish your tutorial
Feature adoption - How many users click your core features
Profile completion - How many users fill out their account details
Time to first action - How quickly users perform any action
Tutorial engagement - How many users watch your walkthrough videos
These metrics exist because they're easy to measure. You can track clicks, completions, and sign-ups with basic analytics. Most SaaS analytics tools come with these metrics out of the box, so founders assume they must be important.
The problem? None of these metrics actually correlate with business success.
I've seen companies with 90% onboarding completion rates and 20% monthly churn. I've worked with SaaS products where users who skipped onboarding entirely became their highest-value customers. The industry treats activation like a checkbox exercise when it should be measuring value delivery.
This approach comes from the consumer app world, where engagement often equals revenue. But B2B SaaS is different. Your users aren't looking for entertainment - they're looking for solutions. They don't care about completing your onboarding; they care about solving their problems.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this B2B SaaS client, they had what looked like a success story on paper. Their dashboard showed healthy activation rates, good trial-to-paid conversion, and users were engaging with their core features. Everything the SaaS playbooks said to track was green.
But their retention was terrible. Users would sign up, go through onboarding, use the product for a week or two, then disappear. The team was frustrated because they were hitting all their activation benchmarks but still losing customers.
My first move was diving into the data, but not the metrics they were already tracking. Instead, I started analyzing user behavior patterns. What I found was eye-opening: their best customers were actually the ones who ignored most of their onboarding flow.
The users who became long-term customers typically signed up, skipped the tutorial, went straight to importing their data, and started using the product immediately. Meanwhile, the users who diligently completed every onboarding step were more likely to churn within 30 days.
This made no sense according to conventional wisdom. Good onboarding is supposed to improve retention, right? But when I dug deeper, I realized what was happening: the users who needed hand-holding weren't actually a good fit for the product.
The product was designed for experienced professionals who wanted to jump in and get to work. The elaborate onboarding was attracting tire-kickers and users who weren't serious about solving the problem. We were optimizing for the wrong people.
Even more revealing: when I tracked which specific actions users took in their first week, traditional "activation" metrics like profile completion had almost zero correlation with long-term retention. But there was one action that predicted success with scary accuracy.
Here's my playbook
What I ended up doing and the results.
Instead of measuring what users completed, I started measuring what they achieved. This required completely rethinking how we tracked activation.
The breakthrough came when I identified what I call "Value Moment Activation" - the specific point where users experience genuine value from the product, not just engagement with it.
For this client, that moment was when users successfully imported their existing data and generated their first automated report. Users who hit this milestone within 7 days had an 85% chance of still being active after 6 months. Users who didn't hit it had a 12% chance.
Here's how I rebuilt their activation tracking:
Step 1: Identify Your Value Moment
I tracked every possible user action in the first 30 days and correlated it with 6-month retention. Most actions showed weak correlation (0.1-0.3), but importing data and generating a report showed 0.82 correlation. This became our north star metric.
Step 2: Reverse Engineer the Path
Once I knew the value moment, I mapped out exactly what users needed to do to reach it. This wasn't about completing onboarding steps - it was about removing barriers to value delivery. I identified 3 critical actions that led to successful data import and report generation.
Step 3: Add Strategic Friction
This was the counterintuitive part. Instead of making onboarding easier, I made it harder for the wrong users to sign up. We added qualification questions during signup and required credit card information upfront. Signups dropped 40%, but activation rates (measured by value delivery) doubled.
Step 4: Create Value-Based Cohorts
I segmented users not by when they signed up, but by when they reached their value moment. This revealed that users who achieved value in days 1-3 had completely different behavior patterns than those who took 7+ days. We could optimize each cohort separately.
Step 5: Build Leading Indicators
Instead of waiting to see if users hit their value moment, I identified early signals that predicted success. Users who connected a data source within 24 hours were 5x more likely to achieve full activation. This became our early intervention trigger.
The key insight: activation isn't about completing tasks, it's about reaching moments of genuine value. Everything else is just vanity metrics.
Value Moments
Track moments of genuine value delivery, not task completion
Leading Indicators
Identify early signals that predict long-term success
Friction Strategy
Add barriers to filter out unqualified users early
Cohort Segmentation
Group users by value achievement timing, not signup date
The results completely changed how we thought about product success. Within 90 days of implementing value-based activation tracking:
Our qualified activation rate went from 23% to 67% when measured by users reaching their value moment. Even though total signups decreased, the quality of users improved dramatically.
Six-month retention jumped from 31% to 78% for users who hit our value moment within the first week. More importantly, we could now predict with 85% accuracy which users would become long-term customers based on early behavior signals.
Customer acquisition cost dropped by 35% because we stopped optimizing for users who would never convert. The qualification questions filtered out tire-kickers before they consumed support resources.
But the most unexpected result was what happened to our product roadmap. When we started optimizing for value delivery instead of feature adoption, we realized that half our planned features were solving the wrong problems. We shifted focus to reducing barriers to the value moment, which had much higher impact than adding new capabilities.
The psychological shift was huge too. Instead of celebrating vanity metrics, the team started obsessing over real user success. Product decisions became easier because we had a clear north star: help more users reach their value moment faster.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons that changed how I think about product activation forever:
Correlation beats completion - Don't measure what users do; measure what predicts their success. The highest-correlated actions often aren't the obvious ones.
Quality over quantity - It's better to have fewer, highly-engaged users than many inactive ones. Optimizing for the right users is more valuable than optimizing for more users.
Friction can be good - Adding barriers that filter out bad-fit users improves your metrics and reduces support burden. Not all friction is bad friction.
Value moments are unique - Every product has different value delivery points. Don't copy other companies' activation metrics; find your own.
Leading indicators matter - Identify early signals that predict value achievement. This lets you intervene before users get stuck.
Segmentation is crucial - Group users by value achievement patterns, not demographics. Behavioral cohorts reveal much more actionable insights.
Less can be more - Simplifying onboarding often improves activation more than adding features. Remove barriers to value, don't add steps.
This approach works best for B2B SaaS where users have specific problems to solve. It's less effective for consumer apps where engagement itself might be the value. The key is understanding what genuine value looks like for your specific users.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Identify your specific value moment through retention correlation analysis
Add qualification questions to filter prospects during signup
Track leading indicators that predict value achievement
Segment users by value delivery timing, not signup cohorts
For your Ecommerce store
Focus on product usage that drives repeat purchases or higher order values
Measure engagement with features that increase customer lifetime value
Track actions that correlate with positive reviews and referrals
Segment customers by value realization speed for targeted marketing