Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
I was sitting across from a client who just told me their Facebook ads had a 4.2% click-through rate. "Amazing results!" they said. Meanwhile, their actual revenue from that channel was declining month over month. This is the problem with most channel fit measurement – we're tracking the wrong metrics entirely.
After working with dozens of startups and e-commerce businesses over the past few years, I've noticed a dangerous pattern. Companies are obsessing over engagement metrics, reach, and cost-per-click while completely missing whether their chosen channels actually fit their business model. The result? Wasted budgets, frustrated teams, and growth that looks good on paper but doesn't translate to sustainable revenue.
Here's what I've learned from watching businesses burn through marketing budgets: channel fit isn't about how well your ads perform – it's about how well your product performs within that channel's natural behavior patterns.
In this playbook, you'll discover:
Why traditional channel metrics are leading you astray
The 3 core metrics that actually predict channel success
My framework for quickly identifying product-channel mismatches
Real examples from failed experiments that saved future budgets
How to build a distribution strategy based on actual fit, not vanity metrics
Industry Reality
What everyone else measures (and why it doesn't work)
Walk into any marketing meeting and you'll hear the same metrics being discussed: CTR, CPM, ROAS, conversion rates, and engagement metrics. Marketing agencies love these numbers because they're easy to improve and look impressive in reports.
The standard approach to measuring channel fit focuses on five key areas:
Cost efficiency – How cheaply can we acquire traffic?
Engagement rates – Are people clicking, liking, sharing?
Conversion optimization – How well does the landing page convert?
Attribution modeling – Which touchpoint gets credit for the sale?
Channel volume – How much traffic can this channel deliver?
This conventional wisdom exists because it's what digital marketing education teaches. It's measurable, it's optimizable, and it makes marketers feel productive. These metrics work well for simple products with short sales cycles and broad market appeal.
But here's where it falls apart: these metrics ignore the fundamental question of whether your product belongs in that channel at all. You can have perfect ROAS and still be in the wrong channel if your product requires behavior patterns that don't match how people use that platform.
I've seen countless businesses optimize their way to beautiful metrics while their actual business slowly dies. The problem isn't execution – it's that they're measuring channel performance instead of channel fit.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came from an e-commerce client with over 1,000 products in their catalog. They came to me frustrated because their Facebook Ads had what looked like decent performance – 2.5 ROAS, reasonable CPCs, and steady traffic flow. But something felt off.
Their challenge wasn't typical. While most successful Facebook Ad campaigns thrive on 1-3 flagship products with broad appeal, my client's strength was their variety. Customers needed time to browse, compare options, and discover products they didn't even know they wanted. The shopping behavior required patience and exploration.
Facebook Ads, by design, demand quick decisions. The platform rewards instant gratification and impulse purchases. Users scroll quickly, make snap judgments, and either buy immediately or disappear forever. There's no time for the careful product discovery that made my client's business model work.
My first instinct was to optimize the ads better. We tested different creatives, refined targeting, improved landing pages. The metrics improved slightly, but the fundamental mismatch remained. We were trying to force a square peg into a round hole.
That's when I realized we were measuring the wrong things entirely. The ROAS looked okay because we were getting some sales, but we were attracting the wrong type of customers – impulse buyers who never became repeat customers and didn't match the business's natural strengths.
Meanwhile, their organic traffic from SEO was converting at much higher rates with better customer lifetime value, even though the "metrics" looked worse on paper. These customers spent time browsing, came back multiple times, and became loyal repeat buyers. The channel fit was obvious – but we'd been blind to it because we were measuring channel performance instead of channel alignment.
Here's my playbook
What I ended up doing and the results.
Instead of continuing to fight against Facebook's natural behavior patterns, I completely pivoted our approach. Here's the framework I developed for measuring actual channel fit instead of just channel performance:
The Product-Channel Fit Analysis
First, I map out what I call "natural channel behavior" versus "required customer behavior" for each product. Facebook rewards quick decisions and visual appeal. SEO rewards patient research and problem-solving intent. LinkedIn favors professional networking and thought leadership.
For this client, I ran a detailed analysis comparing three key areas:
Behavior Pattern Matching: Instead of looking at conversion rates, I tracked how long customers spent browsing before buying across different channels. SEO traffic averaged 4.3 site visits before purchase. Facebook traffic rarely came back if they didn't buy immediately. This revealed a fundamental mismatch – the product needed exploration time that Facebook's environment doesn't provide.
Customer Lifetime Value by Channel: This was the game-changer. Facebook customers had an average LTV of €85. SEO customers averaged €340. Same product, same pricing, but completely different long-term value because the channel attracted different buyer types.
Natural Usage Context: I started tracking not just when people bought, but how they discovered they needed the product. SEO customers were actively searching for solutions. Facebook customers were being interrupted with ads. The discovery context completely changed their relationship with the brand.
Based on this analysis, I made a controversial recommendation: stop Facebook Ads entirely and double down on SEO. The metrics looked worse on paper – SEO takes longer to show results and can't be scaled as quickly as paid ads. But the channel fit was perfect.
We redirected the entire ad budget into content creation, technical SEO improvements, and building organic distribution. Within six months, organic traffic became their primary revenue driver with significantly better unit economics.
Behavior Mapping
Track how customers naturally use each channel versus how your product needs to be discovered and evaluated
LTV by Source
Measure long-term customer value by channel, not just immediate conversion metrics
Context Analysis
Understand whether customers are seeking solutions or being interrupted in each channel
Channel Physics
Accept each platform's natural rules instead of fighting against them
The results spoke for themselves, though they took time to materialize. After shifting from paid ads to SEO-focused content:
Organic traffic increased 10x over eight months, going from around 300 monthly visitors to over 5,000. More importantly, these weren't just vanity metrics – the traffic quality was dramatically better.
Customer lifetime value improved by 280% compared to the Facebook Ad customers we'd been acquiring. These customers made repeat purchases, referred friends, and engaged with the brand long-term.
Cost per acquisition dropped 65% once we stopped competing in Facebook's auction environment. Content creation required upfront investment but paid dividends for months.
The most surprising outcome was customer behavior. SEO customers spent an average of 12 minutes on site during their first visit compared to 45 seconds for Facebook traffic. They viewed 6+ pages versus 1.2 pages. They came back an average of 3.4 times before purchasing versus 1.1 times.
These weren't just better metrics – they revealed that we'd finally found customers whose natural behavior aligned with how the product was meant to be discovered and purchased.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience completely changed how I think about channel measurement. Here are the seven key lessons that now guide every channel decision I make:
Channel fit beats channel performance – A mediocre-looking channel that matches your product's natural usage patterns will always outperform an optimized channel that fights against them.
Customer context matters more than conversion rates – Someone actively searching for a solution behaves completely differently than someone being interrupted with an ad.
LTV reveals true channel quality – Short-term conversion metrics can be misleading. The customers who stay and buy again tell you everything about channel fit.
Each channel has physics you can't change – Facebook rewards impulse decisions. SEO rewards patient research. LinkedIn favors professional relationships. Work with these patterns, not against them.
Product complexity determines optimal channels – Simple products thrive on high-speed channels. Complex products need channels that allow for exploration and consideration.
Time investment creates better fits – Channels that require patience (SEO, content, networking) often create stronger customer relationships than instant channels.
Measurement timing is everything – Judge channel fit over months, not days. The best channels often look worse initially but compound over time.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS specifically:
Focus on trial-to-paid conversion rates by channel, not just signup metrics
Track usage depth during trials - engaged users convert better long-term
Measure support ticket volume by acquisition channel
Monitor churn rates 90 days post-conversion by source
For your Ecommerce store
For e-commerce stores:
Compare average order value and repeat purchase rates across channels
Track browsing depth and session duration by traffic source
Monitor return rates and customer service inquiries by channel
Measure customer lifetime value over 12+ months by acquisition source