Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
I used to believe that tracking was everything. Three years ago, I watched a client burn through $50,000 on Facebook ads that showed "great" click-through rates but zero actual conversions. The attribution tools were lying to us, and we had no real way to understand which channels actually drove revenue.
Here's what nobody talks about: most businesses are drowning in attribution data that tells them nothing about product-channel fit. You're tracking pixels, conversion paths, and multi-touch attribution while completely missing the fundamental question - does your product actually belong on this channel?
After working with dozens of SaaS startups and e-commerce stores, I've learned that the right tools don't just track performance - they help you understand channel fit. This isn't about finding the perfect attribution model. It's about identifying which channels naturally align with how your customers actually buy.
In this playbook, you'll learn:
Why traditional tracking tools mask product-channel misalignment
The 3-layer tool stack I use to analyze true channel fit
How to spot when attribution is lying about channel performance
My framework for testing channel fit before scaling spend
Real examples from client projects where tools revealed hidden patterns
Ready to stop wasting money on channels that look good on paper but don't actually work? Let's dive into the tools that actually matter for product-channel fit analysis.
Industry Reality
What everyone thinks they need for channel analysis
Walk into any marketing team meeting, and you'll hear the same conversation: "We need better attribution." "Let's implement multi-touch tracking." "We should use UTM parameters everywhere." The entire industry has convinced itself that more tracking equals better channel decisions.
Here's what most businesses are doing wrong with their channel analysis:
Over-relying on last-click attribution - Giving all credit to the final touchpoint
Trusting platform reporting - Facebook says ROAS is 8x, Google says it's 4x for the same customer
Focusing on vanity metrics - Click-through rates and impressions instead of actual business impact
Ignoring dark funnel behavior - Customers research on multiple channels before buying
Using generic tools for specific problems - Google Analytics for everything instead of specialized solutions
The problem with this approach? You end up optimizing for attribution accuracy instead of channel effectiveness. You're measuring the wrong thing entirely.
Most attribution tools tell you what happened, but they don't tell you why it happened or whether it's sustainable. They can't tell you if your product fundamentally doesn't fit the channel's buying behavior. And that's where most businesses waste money - doubling down on channels that show good metrics but poor product-market fit.
The real question isn't "How do we track better?" It's "How do we identify which channels naturally align with how our customers want to discover and evaluate our product?"
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Two years ago, I was working with an e-commerce client who had built an incredible product catalog - over 1,000 SKUs of high-quality items. They'd been running Facebook ads for months with what looked like decent numbers: 2.5 ROAS, reasonable click-through rates, steady traffic.
But here's what was really happening: customers needed time to browse and discover. Their strength was variety and quality, not impulse purchases. Facebook's quick-decision environment was fundamentally incompatible with their shopping behavior.
The tracking tools showed "success" because they measured clicks and immediate conversions. But they completely missed the deeper problem: the channel didn't match the product. Customers who found them through Facebook would click, browse for 30 seconds, then leave. Those who found them through SEO would spend 15 minutes exploring the catalog before buying.
This is when I realized that traditional attribution tools were actually hiding the channel fit problem. Google Analytics showed traffic sources. Facebook showed ROAS. But nothing showed us that we were forcing a discovery-based product into an impulse-purchase channel.
The real breakthrough came when I started looking beyond attribution data to understand user behavior patterns by channel. I needed tools that could show me not just where traffic came from, but how different traffic sources actually used the product.
That's when I developed my 3-layer approach to channel fit analysis - tools that reveal the story behind the metrics.
Here's my playbook
What I ended up doing and the results.
After that Facebook ads disaster, I completely rebuilt how I analyze channel fit. Instead of chasing perfect attribution, I focus on three layers of analysis that reveal whether a channel naturally aligns with how customers want to buy.
Layer 1: Behavioral Analysis Tools
The first layer reveals how people from different channels actually use your product. I use Hotjar or FullStory to track session recordings by traffic source. Here's what I look for:
Time on site by channel - SEO traffic spending 10+ minutes vs social traffic bouncing in 30 seconds
Page depth by source - Which channels drive exploration vs quick exits
Feature usage patterns - Do paid traffic users actually engage with your core value proposition?
For that e-commerce client, Hotjar revealed that Facebook traffic had a 15-second average session time, while organic search traffic averaged 8 minutes. The tools weren't lying - the channel was just wrong for the product.
Layer 2: Customer Journey Mapping
The second layer maps the actual customer journey across channels. I use customer interviews combined with survey tools like Typeform to understand the real path to purchase:
"How did you first hear about us?" (often different from last-click attribution)
"What made you decide to buy?" (reveals the actual conversion trigger)
"How long did you research before purchasing?" (shows channel-product fit)
For a B2B SaaS client, this revealed that LinkedIn personal branding was driving 60% of quality leads, even though Google Analytics attributed most conversions to "direct" traffic. People were following the founder's content, building trust, then typing the URL directly when ready to buy.
Layer 3: Cohort Performance Analysis
The third layer tracks long-term value by acquisition channel. I use tools like Amplitude or Mixpanel to segment customers by original traffic source and track:
Retention rates by channel - Which channels bring customers who stick around
Lifetime value by source - Not just first purchase, but total customer value
Feature adoption by acquisition channel - Which traffic sources find your core value prop
This layer often reveals that cheaper channels bring higher-value customers. For another client, organic search had 3x higher retention than paid social, even though paid social had better "conversion rates."
The Integration That Changes Everything
The magic happens when you layer these tools together. Hotjar shows you behavior, customer interviews reveal motivation, and cohort analysis proves long-term fit. Together, they tell you which channels naturally align with your product's buying process.
I also use Google Sheets with custom scripts to pull data from multiple sources and create a channel fit scorecard. This simple tool has saved clients thousands in misallocated ad spend.
Behavior Tracking
Use session recordings to see how different traffic sources actually interact with your product
Customer Interviews
Survey users to understand their real journey - often very different from attribution data
Cohort Analysis
Track long-term value by acquisition channel to identify sustainable growth sources
Integration Dashboard
Combine all three layers into a simple scorecard that reveals true channel-product fit
The results from this approach have been dramatic across multiple client projects. Instead of chasing attribution perfection, we focus on channel-product alignment - and the numbers speak for themselves.
For the e-commerce client with 1,000+ SKUs, we shifted budget from Facebook ads to SEO and saw a 10x increase in organic traffic within 3 months. More importantly, these visitors had 5x higher lifetime value because they matched the product's discovery-based buying process.
The B2B SaaS client doubled down on founder-led content on LinkedIn after our analysis revealed it was the true growth driver. They stopped wasting money on cold email campaigns that showed good "open rates" but terrible customer quality.
But the most important result? Clarity. Instead of constantly questioning whether their marketing was working, these clients now had a systematic way to evaluate new channels before scaling spend. They could spot product-channel misalignment early and avoid expensive mistakes.
The tools didn't just save money - they revealed entirely new growth opportunities that traditional attribution would have missed.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Attribution lies more often than it tells the truth - Don't trust single-source attribution data
Behavior beats metrics every time - How customers use your product matters more than click-through rates
The dark funnel is real - Most customer journeys span multiple touchpoints and timeframes
Channel fit is product-specific - What works for one product type might be terrible for another
Long-term value reveals true channel quality - Some channels bring customers who stick, others bring quick churners
Customer interviews trump data analysis - Ask people directly about their journey instead of guessing from data
Simple tools often work better than complex ones - A Google Sheets dashboard can be more valuable than expensive analytics platforms
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement this approach:
Start with behavioral analysis using Hotjar to understand user engagement by traffic source
Use in-app surveys to ask users how they discovered your product
Track feature adoption and retention by acquisition channel
Focus on channels that bring users who actually use your core features
For your Ecommerce store
For e-commerce stores implementing channel fit analysis:
Use session recordings to see how different traffic sources browse your catalog
Track average order value and repeat purchase rate by channel
Survey customers about their discovery and research process
Match your product complexity to the channel's buying behavior