Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, a client came to me frustrated. They were spending $15k monthly on Google Ads, getting plenty of phone calls, but couldn't prove ROI to their board. Their call tracking showed "direct" for most conversions, leaving them blind to what was actually working.
Sound familiar? You're not alone. Most businesses are flying blind when it comes to call attribution, especially when trying to separate paid traffic results from organic efforts. The standard advice of "just use call tracking software" falls apart the moment you realize these tools can't distinguish between someone who found you through a $50 Google Ad click and someone who discovered you through your SEO efforts six months ago.
After working with dozens of service businesses struggling with this exact problem, I've learned that the issue isn't the technology—it's how we approach attribution in the first place. The multi-touch customer journey breaks traditional tracking, and most attribution models are built for e-commerce, not service businesses that rely on phone calls.
In this playbook, you'll discover:
Why most call tracking attribution is fundamentally flawed
The "dark funnel" reality of how customers actually find service businesses
A practical framework for measuring what really drives phone conversions
How to set up attribution systems that work in the real world
Alternative metrics that reveal true channel performance
Attribution Reality
What the marketing industry won't tell you
Walk into any marketing conference and you'll hear the same advice: "Track everything, attribute everything, optimize everything." The call tracking industry has built billion-dollar businesses selling this dream of perfect attribution.
Here's what they typically recommend:
Dynamic Number Insertion (DNI): Show different phone numbers based on traffic source, so each channel gets its own number
Keyword-Level Tracking: Assign specific numbers to individual keywords or ad campaigns
First-Touch Attribution: Credit the channel where the customer first discovered you
Call Recording & Scoring: Listen to calls and score them for quality to optimize for "good" leads
Integration with Analytics: Connect call data to Google Analytics for unified reporting
This approach exists because it's clean, reportable, and makes agencies look good in monthly reports. It gives the illusion of control and scientific precision that businesses crave.
But here's where it falls apart: real customer journeys are messy. Someone might see your Google Ad, not click it, then search for your company name a week later, browse your website, check your reviews, and finally call. Traditional call tracking would credit "organic search" or "direct" for that conversion, completely missing the original paid ad influence.
The dirty secret? Most call tracking platforms show 40-60% of calls as "direct" or "unknown source" because they can't track the complex, multi-device, multi-session journeys that actually drive phone calls in 2025.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I learned this lesson the hard way while working with an e-commerce client who was heavily dependent on Facebook Ads. They had what looked like a successful operation—2.5 ROAS, consistent revenue, and steady growth. But something felt off about their attribution.
The client called me in because they wanted to diversify beyond Facebook and were concerned about putting all their eggs in one platform's basket. Smart thinking, right? That's when I discovered the attribution nightmare that exists in most businesses.
We implemented a complete SEO overhaul alongside their existing Facebook campaigns. Within a month, something bizarre happened: Facebook's reported ROAS jumped from 2.5 to 8-9. The client was celebrating what they thought was dramatically improved ad performance. But I knew better.
The reality? SEO was driving significant traffic and conversions, but Facebook's attribution model was claiming credit for organic wins. This is the classic "dark funnel" problem that plagues every business trying to measure channel performance.
Here's what was actually happening: Someone would search Google (organic), find their product, maybe not buy immediately, then see a Facebook retargeting ad later and purchase. Facebook's attribution gave full credit to the ad, completely ignoring the organic search that started the journey.
This taught me that the customer journey is far more complex than any attribution model can capture. A typical journey actually looks like: Google search → social media browsing → retargeting ad → review site research → email nurture → multiple website visits → final conversion. Which touchpoint deserves the "credit"? All of them, none of them, or somewhere in between?
The breakthrough came when I stopped trying to control and track every interaction. Instead, I focused on expanding visibility across all possible touchpoints. More distribution channels mean more opportunities for customers to discover and trust your brand—regardless of which touchpoint gets the "credit."
Here's my playbook
What I ended up doing and the results.
After dealing with this attribution mess across multiple client projects, I developed a practical framework that acknowledges reality instead of chasing perfect tracking fantasies. This isn't about having cleaner reports—it's about making better business decisions.
Step 1: Accept the Dark Funnel Reality
Stop believing in "build it and they will come." Start believing in "distribute everywhere they already are." Your customers interact with your brand across multiple channels before converting. Instead of trying to track every interaction (impossible in today's privacy-focused world), focus on expanding your presence across all relevant touchpoints.
I implemented this with my e-commerce client by building a comprehensive distribution system rather than tweaking ad copy. This meant:
Complete website restructuring for SEO optimization
Development of a full e-commerce SEO strategy
Content creation focused on search intent, not just brand messaging
Step 2: Implement Coverage-Based Metrics
Instead of obsessing over which channel gets credit, I started measuring "coverage" across all touchpoints. The key insight: customers need multiple exposures across different contexts to build trust and make purchase decisions.
I tracked:
Total branded search volume (indicates overall demand creation)
Direct traffic growth (shows brand recall and word-of-mouth)
Engagement metrics across all channels (not just last-click conversions)
Customer survey data about discovery paths
Step 3: Build Channel Synergy Models
Rather than competing for attribution credit, I designed channels to work together. Paid ads drive initial awareness, SEO captures search demand, email nurtures relationships, and retargeting converts browsers.
The strategy shifted from "which channel converts best" to "how can we create more touchpoints in the customer journey." This approach acknowledges that attribution lies, but distribution doesn't.
Step 4: Use Directional Data, Not Perfect Tracking
I stopped chasing perfect attribution and started using directional indicators. When Facebook ROAS jumped after implementing SEO, I knew it was synergy, not improved ad performance. This led to better budget allocation decisions based on reality, not reporting artifacts.
The key metrics became: overall revenue growth, customer lifetime value, and total cost of acquisition across all channels combined—not individual channel performance in isolation.
Framework Foundation
Accept that perfect attribution is impossible and focus on creating multiple touchpoints across the customer journey instead
Coverage Metrics
Track total branded search, direct traffic growth, and cross-channel engagement rather than last-click conversions only
Channel Synergy
Design paid and organic channels to complement each other rather than compete for attribution credit
Reality-Based Decisions
Use directional data and overall business metrics to guide strategy rather than chasing perfect tracking reports
The results of this approach were immediately visible in business performance, even when the reporting became less "clean." My e-commerce client saw significant purchase generation through organic traffic—customers who had the time and intent to explore their full product range rather than making quick decisions from ads.
More importantly, they gained a realistic understanding of how their marketing actually worked. Instead of optimizing individual channels in isolation, they could make strategic decisions about budget allocation based on how channels worked together.
The "messy" attribution data actually told a more honest story: customers discovered them through search, engaged through social media, received email nurturing, and converted after multiple touchpoints. This insight led to better customer experience design and more sustainable growth.
Total revenue increased significantly, but more importantly, they built a resilient marketing system that didn't depend on any single platform or perfect tracking mechanism.
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 wrestling with attribution across multiple client projects:
Attribution models are retrospective fiction: They tell a story about what happened, but that story is usually wrong. Customer journeys are messy and non-linear.
Dark funnel dynamics rule everything: Most customer interactions happen where you can't track them—conversations, recommendations, research sessions, comparison shopping.
Channel synergy beats channel optimization: Channels working together always outperform channels optimized in isolation, even if the reporting is messier.
Coverage matters more than precision: Being present at multiple touchpoints is more valuable than perfectly tracking which touchpoint "wins."
Customer surveys reveal truth: Asking customers how they found you often reveals attribution patterns that tracking tools miss completely.
Business metrics trump channel metrics: Focus on overall revenue, LTV, and blended CAC rather than individual channel performance.
Privacy changes make this worse: iOS updates, cookie restrictions, and privacy regulations will continue breaking traditional attribution models.
The biggest shift is moving from "what can we track" to "what can we influence." You can influence brand awareness, search demand, customer experience, and word-of-mouth. You can't control attribution, but you can control distribution strategy.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups dealing with call tracking attribution:
Focus on branded search growth as a proxy for demand generation across all channels
Implement customer onboarding surveys asking about discovery paths
Track demo requests and trial signups as leading indicators, not just final conversions
Measure total pipeline influence rather than last-touch attribution
For your Ecommerce store
For ecommerce stores managing call and conversion attribution:
Monitor direct traffic growth as an indicator of brand recall and word-of-mouth impact
Use customer lifetime value analysis to understand true channel performance over time
Implement post-purchase surveys to capture the real customer journey story
Focus on blended CAC across all channels rather than individual channel ROI