Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Facebook told me my ROAS was 8.5. Google Analytics said it was 3.2. My actual bank account? Well, that told a completely different story.
I was working with an e-commerce client who had become completely dependent on Facebook Ads. On paper, everything looked amazing - great click-through rates, solid reported ROAS, the whole nine yards. But when we dug into the actual customer lifetime value by channel, we discovered we'd been chasing vanity metrics while missing the real picture.
The wake-up call came when I implemented a complete distribution overhaul and saw Facebook's "ROAS" jump from 2.5 to 8-9 overnight. Not because the ads got better, but because SEO was driving significant traffic and conversions that Facebook was claiming credit for.
Here's what you'll learn from my painful journey into attribution reality:
Why platform-reported metrics lie about true customer value
The simple framework I use to track real LTV by channel
How to embrace the "dark funnel" instead of fighting it
The attribution model that actually predicts cash flow
When to trust platform data (and when to ignore it completely)
This isn't another theoretical attribution guide. This is what happens when you stop believing the fairy tales platforms tell you and start tracking what actually drives revenue. Let's get into the real growth strategies that matter.
Industry Reality
What every marketer thinks they know about attribution
Walk into any marketing conference and you'll hear the same attribution gospel being preached:
"Track everything with UTM parameters." Set up detailed campaign tracking, tag every link, build elaborate attribution models that assign credit across touchpoints. The promise? Crystal-clear visibility into which channels drive the highest customer lifetime value.
"Use multi-touch attribution models." Linear attribution, time-decay, position-based - pick your poison. The industry sells sophisticated attribution as the holy grail of marketing measurement.
"Platform reporting is gospel." Facebook Ads Manager shows your ROAS, so that's your real return. Google Ads reports conversions, so that's what's working. Trust the platforms - they know best.
"First-party data solves everything." Install tracking pixels, set up server-side tracking, build data warehouses. More data equals better attribution, right?
"Customer journey mapping is essential." Map every touchpoint, understand the path to purchase, optimize each step in the funnel.
This conventional wisdom exists because it makes marketers feel in control. We love the illusion that we can track, measure, and optimize every interaction. Attribution software companies have built billion-dollar businesses selling this fantasy of perfect measurement.
But here's the uncomfortable truth: the more complex your attribution model, the further you get from understanding what actually drives revenue. While you're busy building elaborate tracking systems, your competitors are focusing on channels that deliver real customers - even if they can't perfectly measure them.
The reality is messier, more uncertain, and far more effective than what the industry wants to admit.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breaking point came while working with an e-commerce client who was convinced Facebook Ads were their golden goose. Their dashboard looked beautiful - consistent 2.5 ROAS, steady traffic, everything tracking perfectly in their attribution model.
But there was a problem: they were completely dependent on one channel. Every dollar of growth required more ad spend. Their customer acquisition costs kept climbing, and despite "profitable" Facebook campaigns, cash flow was tight.
That's when I suggested something that made them uncomfortable: let's build a comprehensive distribution system instead of just optimizing ads. We implemented a complete SEO strategy, optimized their website architecture, and started creating content that would rank organically.
Within a month, something bizarre happened. Facebook's reported ROAS jumped from 2.5 to 8-9. Overnight.
Had our ads suddenly become magical? Of course not. SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins.
This was my "attribution is broken" moment. I realized we'd been optimizing for the wrong metrics. Instead of measuring true customer lifetime value by channel, we were chasing platform-reported numbers that bore little resemblance to reality.
The client's actual customer journey looked nothing like what the attribution model suggested. A typical path was: Google search → social media discovery → email nurture → retargeting ad exposure → direct website visit → purchase. But guess which touchpoint got the credit? Whatever happened to fire last in the tracking system.
That's when I stopped trying to track every interaction and started focusing on what actually mattered: which channels brought customers who stayed, spent more, and referred others. The shift from attribution obsession to lifetime value focus changed everything.
Here's my playbook
What I ended up doing and the results.
After that wake-up call, I developed a completely different approach to measuring customer value by channel. Instead of fighting the dark funnel, I learned to embrace it.
Step 1: Abandon Perfect Attribution
First, I stopped trying to track every touchpoint. The modern customer journey is too complex for traditional attribution. Instead, I focus on channel contribution rather than channel attribution. If a customer discovers you through SEO, engages on social media, and converts via email, all three channels contributed value.
Step 2: Implement Source-Based LTV Tracking
Rather than tracking complex journeys, I tag customers based on their primary discovery channel and track their lifetime value from there. A customer who first found you through organic search gets tagged as an "SEO customer" regardless of their final conversion path.
Step 3: Build a Simple LTV Dashboard
I create a dashboard that tracks four key metrics by discovery channel:
Average order value in first 90 days
Repeat purchase rate by month 6
Customer lifespan (time to churn)
Referral rate (customers who bring others)
Step 4: Use Cohort Analysis by Channel
Instead of looking at blended metrics, I analyze customer cohorts by their discovery channel. SEO customers from January vs Facebook customers from January - how do they compare after 6 months?
Step 5: Apply the 80/20 Channel Focus
Once you have real LTV data, you can see which channels actually drive valuable customers. Often, the channel with the highest "conversion rate" delivers the lowest lifetime value customers. Focus your efforts on channels that bring customers who stick around.
Step 6: Cross-Reference with Contribution Models
For major business decisions, I use contribution models rather than attribution models. If revenue increased after launching a content strategy, that content contributed value - even if the last click was a branded search or direct visit.
The key insight: Stop measuring what you can track perfectly and start tracking what actually matters imperfectly. A rough understanding of true customer value beats perfect measurement of meaningless metrics.
Discovery Channel
Tag customers by how they first found you, not their final conversion path
Cohort Analysis
Compare 6-month LTV of customers from different discovery channels
Contribution Model
Focus on overall business impact rather than last-click attribution
Reality Check
Cross-reference platform data with actual cash flow and customer behavior
The results were eye-opening. For my e-commerce client, the new tracking system revealed that:
SEO customers had 40% higher lifetime value than paid ad customers, despite lower "conversion rates" in traditional tracking. They stayed longer, bought more frequently, and referred more friends.
Email newsletter subscribers (regardless of original source) had the highest LTV of any segment - 60% higher than average. But email barely showed up in last-click attribution models.
Facebook ad customers had the lowest retention rates despite the highest reported ROAS. They were bargain hunters who rarely returned.
Direct traffic (the mysterious "dark funnel") accounted for 40% of revenue and had above-average LTV. These customers had been influenced by multiple channels but showed up as "unattributable" in traditional models.
Most importantly, business decisions became clearer. Instead of optimizing for Facebook's reported metrics, we doubled down on SEO and email marketing. Revenue grew, but more importantly, the quality of customers improved dramatically.
The client went from being dependent on paid ads to having a diversified growth engine with predictable customer lifetime values by channel.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from implementing real LTV tracking across multiple client projects:
1. Platform metrics optimize for platform profits, not your profits. Facebook wants you to spend more on ads, so their attribution model is designed to make ads look more effective than they are.
2. Customer quality matters more than customer quantity. A channel that brings 100 customers who spend $50 once beats a channel that brings 500 customers who spend $20 and never return.
3. The dark funnel is your friend, not your enemy. Customers influenced by multiple touchpoints often have higher lifetime value than those who convert immediately from a single source.
4. Embrace directional accuracy over false precision. It's better to know approximately which channels drive valuable customers than to precisely measure meaningless conversions.
5. Time reveals truth. Short-term conversion metrics lie. Lifetime value measured over 6-12 months tells the real story.
6. Distribution beats optimization. Focus on expanding successful channels rather than optimizing underperforming ones to perfection.
7. Cash flow trumps dashboards. If your attribution model doesn't correlate with actual revenue, trust the bank account.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing LTV tracking by channel:
Track trial-to-paid conversion rates by discovery channel
Measure monthly churn rates for different customer sources
Focus on channels that bring users who activate quickly
Monitor expansion revenue (upsells) by customer source
For your Ecommerce store
For e-commerce stores measuring customer lifetime value:
Track repeat purchase rates by original traffic source
Monitor average order value trends over customer lifetime
Identify which channels bring customers with highest referral rates
Focus on channels that drive browsing behavior, not just conversions