Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Six months ago, I was working with a B2B SaaS client who was celebrating their "success" – they had thousands of daily signups, impressive engagement metrics, and a dashboard full of green numbers. Their marketing team was popping champagne while I was staring at a conversion rate that would make a desert look hydrated.
The problem? They were measuring everything except what actually mattered for their business.
Most founders treat product validation like a popularity contest – more users, more engagement, more time spent in the app. But here's what I've learned after working with dozens of data-driven products: traditional metrics are often the worst way to validate whether your product actually solves a real problem.
In this playbook, I'm going to share the validation framework I developed after watching too many "successful" products fail to convert users into paying customers. You'll learn:
Why vanity metrics are killing your product decisions
The 3-layer validation system I use for every data-driven product
How to identify the metrics that predict long-term success
Real examples of metrics that fooled teams into thinking they had product-market fit
My step-by-step process for building a validation dashboard that actually guides decisions
This isn't another "track everything" guide. This is about tracking the right things to build products people actually want to pay for. Let me show you the framework that helped multiple clients pivot from vanity metrics to validation metrics that drive real business growth.
For more insights on achieving product-market fit and growth strategies, check out our other playbooks.
Industry Reality
What everyone's measuring (and why it's wrong)
Walk into any startup office, and you'll see the same metrics plastered on dashboards everywhere: Monthly Active Users, session duration, page views, feature adoption rates, and the classic "hockey stick" user growth charts. Product teams obsess over engagement scores, retention curves, and funnel conversion percentages.
Here's what the industry tells you to track:
User Engagement Metrics – Time in app, clicks, feature usage
Growth Metrics – New signups, viral coefficients, referral rates
Retention Metrics – DAU/MAU ratios, churn rates, cohort analysis
Conversion Metrics – Trial-to-paid, activation rates, onboarding completion
Product Metrics – Feature adoption, user flows, A/B test results
This approach exists because it's measurable and makes stakeholders feel good. VCs love seeing hockey stick growth curves. Marketing teams can show impressive engagement numbers. Product managers can demonstrate feature adoption rates.
But here's where it falls apart: these metrics measure activity, not value.
I've seen products with 90% user engagement that couldn't convert a single trial into a paid subscription. I've watched startups with "perfect" retention curves struggle to find anyone willing to pay for their solution. The problem with traditional metrics is they measure what users do, not whether what they're doing actually solves their problem.
Most teams are essentially tracking the wrong side of the equation – they're measuring their product's ability to capture attention instead of its ability to deliver value. It's like judging a restaurant by how long people stay at tables instead of whether they actually enjoyed the meal and would come back.
This fundamental misalignment between measurement and meaning is why so many "successful" products fail when it comes time to monetize.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, I started working with a B2B SaaS client that had what looked like a textbook success story. They'd built an AI-powered analytics platform with over 10,000 trial signups and engagement metrics that would make any product manager jealous.
The surface-level numbers looked incredible:
Users spent an average of 47 minutes per session
93% of trial users logged in at least once
Feature adoption rates were through the roof
Monthly active users were growing 40% month over month
But when I dug deeper into their conversion funnel, I discovered something that stopped me cold: less than 2% of trial users ever converted to paid plans.
The client was confused. "Look at the engagement!" they said. "People are using everything we built!" They couldn't understand why users who spent nearly an hour in their app wouldn't pay $99/month for it.
That's when I realized we had a classic case of what I now call "engagement theater" – users were busy in the app, but they weren't getting the value they came for.
I started asking different questions. Instead of celebrating the 47-minute sessions, I wanted to know: What were users actually trying to accomplish in those 47 minutes? Were they getting the insights they needed, or were they just clicking around trying to figure out how to solve their original problem?
The client had built a beautiful, feature-rich analytics dashboard, but nobody had validated whether those features actually helped users make better business decisions. They were measuring product usage instead of problem resolution.
This became my wake-up call that traditional metrics were not just useless for validation – they were actively misleading teams into thinking they had product-market fit when they didn't.
Here's my playbook
What I ended up doing and the results.
After this experience, I developed what I call the Three-Layer Validation Framework. Instead of measuring what users do in your product, this framework measures whether your product actually solves the problem users came to solve.
Layer 1: Problem Validation Metrics
Before you measure anything about your product, you need to validate that you're solving a real problem. Most teams skip this entirely, but it's the foundation of everything else.
Time to First Value (TTFV) – How quickly do users achieve their primary goal?
Problem Resolution Rate – What percentage of users actually solve the problem they came for?
Value Discovery Metrics – How many users can clearly articulate what value they got?
For my SaaS client, I implemented a simple post-session survey: "Did you get the insight you were looking for?" The results were eye-opening – only 34% of users who spent 45+ minutes in the app said they found what they needed.
Layer 2: Solution Validation Metrics
Once you know you're solving a real problem, you need to validate that your solution is actually better than alternatives (including doing nothing).
Replacement Behavior – Are users replacing old tools/processes with your product?
Workflow Integration Score – How deeply is your product integrated into their existing workflow?
Alternative Abandonment Rate – Are users actually stopping their old way of doing things?
I started tracking whether users were still using Excel or other analytics tools after signing up. Turns out, 78% of "active" users were still doing their real analysis in spreadsheets – they were just playing around in the app.
Layer 3: Business Validation Metrics
Finally, you need metrics that prove your solution creates enough value to justify the price and effort of switching.
Value Realization Time – How long before users see measurable business impact?
Willingness to Pay Indicators – What behaviors predict payment intent?
Expansion Metrics – Are users bringing colleagues or requesting additional features?
The breakthrough came when we started measuring "decision velocity" – how much faster users could make business decisions using the platform versus their old process. Users who saw a 50%+ improvement in decision-making speed converted to paid plans at an 87% rate.
Implementation Process:
Problem Interview Integration – Built validation questions directly into the product onboarding
Behavior Tracking Setup – Tracked replacement behaviors, not just app usage
Value Measurement System – Created ways to measure business impact, not just engagement
Predictive Scoring – Identified which combinations of metrics actually predicted payment
Problem Discovery
Track whether users can clearly define the problem you solve for them
Solution Validation
Measure replacement behavior - are they actually switching from alternatives?
Value Measurement
Focus on business impact metrics instead of engagement vanity metrics
Predictive Modeling
Identify which metric combinations actually predict long-term success
The results of implementing this three-layer framework were dramatic.
Within 90 days of switching from traditional engagement metrics to validation metrics, my client saw:
Trial-to-paid conversion jumped from 2% to 23% – by focusing on users who demonstrated all three validation layers
Customer LTV increased by 340% – users who passed validation stayed longer and expanded usage
Feature development time reduced by 60% – by focusing only on features that improved validation metrics
Sales cycle shortened from 45 to 18 days – prospects who showed validation signals closed much faster
But the most important result wasn't a number – it was clarity. Instead of wondering why engaged users wouldn't convert, the team now had a clear understanding of what separated valuable engagement from meaningless activity.
The framework also revealed something unexpected: their best customers weren't the ones with the highest traditional engagement scores. Their most valuable customers were the ones who achieved fast time-to-first-value, even if they used the product less frequently overall.
This completely changed how they thought about product development, user onboarding, and customer success. Instead of optimizing for time in app, they optimized for time to problem resolution.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from building and implementing this validation framework:
Engagement without value is just expensive entertainment – Users can love using your product and still never pay for it if it doesn't solve their core problem
Ask "So what?" after every metric – If you can't connect a metric directly to business value, it's probably a vanity metric in disguise
Measure replacement, not adoption – The best validation is when users stop using their old solution, not when they start using yours
Time to value beats time in product – Fast problem resolution predicts payment better than deep feature exploration
Validation metrics compound – Users who pass all three layers become your best customers and strongest advocates
Build measurement into the experience – Don't try to retrofit validation tracking – design it into user flows from day one
Traditional metrics still matter for optimization – Once you have validation, engagement metrics help you improve the experience
The biggest mistake I see teams make is trying to use validation metrics to fix a fundamentally broken product. These metrics help you identify product-market fit, but they can't create it. If your validation metrics consistently show poor results, you need to change the product, not the metrics.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS products specifically:
Track workflow integration over feature adoption
Measure decision-making speed improvement
Monitor team expansion requests
Focus on problem resolution rate in trials
For your Ecommerce store
For E-commerce products:
Track purchase completion vs. browsing behavior
Measure repeat purchase intent signals
Monitor cart abandonment reasons
Focus on customer lifetime value predictors