Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Three months into managing a B2B startup's marketing budget, I stared at our Facebook attribution dashboard showing an 8-9 ROAS while our actual revenue growth told a completely different story. The disconnect was massive—and expensive.
This wasn't just a tracking issue. It was a fundamental problem with how we understand marketing performance. While Facebook was claiming credit for wins that clearly came from our SEO efforts, I realized that most businesses are making budget decisions based on lies their attribution models tell them.
That's when I discovered the power of marketing mix modeling—a statistical approach that cuts through attribution noise to reveal what actually drives growth. Instead of trusting last-click attribution or platform-reported metrics, I started building models that showed the true impact of each marketing channel.
Here's what you'll learn from my journey into proper marketing measurement:
Why traditional attribution is fundamentally broken in today's marketing landscape
How I built a simple but effective marketing mix model without a data science degree
The real methodology behind understanding channel interactions and diminishing returns
Practical frameworks for budget allocation based on actual incrementality
How to implement this approach whether you're managing $5K or $500K monthly ad spend
This isn't about complex statistical models that require a PhD to understand. It's about practical growth measurement that actually helps you make better budget decisions.
Reality Check
Why attribution models are fundamentally broken
Walk into any marketing meeting and you'll hear the same refrains: "Facebook is delivering 4x ROAS," "Google Ads has our lowest CAC," "This campaign drove 200 conversions." Everyone's optimizing based on platform-reported attribution, treating these numbers as gospel truth.
The industry has built an entire ecosystem around this approach. Marketing teams live and die by attribution dashboards. Budget decisions flow from last-click data. Agencies get paid based on platform-reported performance. It's the foundation of modern marketing measurement.
Here's what the conventional wisdom tells you to track:
Platform Attribution - Trust Facebook, Google, and other platforms to tell you what they delivered
UTM Tracking - Tag everything and follow the breadcrumbs through your funnel
Multi-Touch Attribution - Weight touchpoints across the customer journey
Marketing Qualified Leads - Count leads by source and optimize for volume
Channel-Specific ROAS - Calculate return on ad spend for each platform independently
This approach exists because it's simple, immediate, and gives marketers the control they crave. Platforms make it easy to see "your" results. Attribution tools promise to connect every dot. Everyone wants to believe they can track the complete customer journey.
But here's where it falls apart: the fundamental assumption that we can accurately track individual customer journeys is completely wrong. iOS updates killed mobile tracking. Third-party cookies are disappearing. Cross-device behavior is invisible. The "dark funnel" represents the majority of actual customer research and decision-making.
Most importantly, attribution models completely ignore the cumulative and interaction effects that drive real business growth. When your SEO efforts make your Facebook ads more effective, or when your content marketing primes prospects for paid search, traditional attribution misses the entire story.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breaking point came during a budget planning session where I was managing marketing for a B2B startup with multiple channels running simultaneously. We had Facebook Ads, Google Search, SEO content, and LinkedIn campaigns all active, plus some partnership marketing and occasional PR mentions.
Our Facebook dashboard was showing fantastic numbers—ROAS jumping from 2.5 to 8-9 almost overnight. The marketing team was celebrating. The CEO wanted to double the Facebook budget immediately. Everything looked perfect on the surface.
But I'd been tracking our overall business metrics separately, and something didn't add up. Revenue growth was steady but nowhere near what you'd expect from such dramatic improvements in our largest ad channel. More concerning, the timing of our revenue spikes didn't correlate with the Facebook "wins."
That's when I started digging deeper. I realized we'd launched a comprehensive SEO content strategy at exactly the same time Facebook's ROAS improved. We were publishing 3-4 high-quality articles per week, targeting bottom-funnel keywords, building serious topical authority in our niche.
The pattern became clear: prospects were discovering us through organic search, researching our content, then clicking on Facebook retargeting ads when they were ready to convert. Facebook was getting attribution credit for conversions that SEO actually drove. We were optimizing budget toward the wrong channel based on completely false data.
This wasn't just a tracking glitch. It revealed a fundamental flaw in how we think about marketing measurement. Every attribution model—first-click, last-click, multi-touch, data-driven—suffers from the same core problem: they assume we can trace individual customer journeys when the reality is far more complex and interconnected.
I needed a completely different approach to understand what was actually driving growth and how our marketing channels worked together. That's when I discovered marketing mix modeling—a statistical method that measures marketing effectiveness at the aggregate level rather than trying to track individual users.
Here's my playbook
What I ended up doing and the results.
Instead of trying to track individual customer journeys, I built a system that measures marketing effectiveness at the business level. Marketing mix modeling uses statistical analysis to understand how different marketing inputs contribute to overall business outcomes—without relying on user-level tracking that's fundamentally broken.
Here's the methodology I developed for businesses that need accurate marketing measurement:
Step 1: Data Collection and Preparation
I started collecting data at the weekly level across all marketing channels and business metrics. This included media spend, impressions, clicks, organic traffic, email sends, PR mentions, and any other marketing activity. The key was capturing both paid and organic efforts in the same dataset.
For business outcomes, I tracked revenue, leads, trials, demos booked—whatever mattered most for the specific business. The crucial insight: measure everything at the same time interval and aggregate level. No individual user tracking, no attribution chains, just business-level inputs and outputs.
Step 2: Statistical Modeling
Using simple regression analysis (you can do this in Google Sheets or Excel), I started modeling the relationship between marketing inputs and business outcomes. The goal wasn't perfect prediction but understanding relative contribution and interaction effects.
The model revealed things attribution could never show: SEO content had a 2-3 week lag before impacting conversions, but its effect lasted for months. Facebook ads showed immediate impact but with severe diminishing returns after a certain spend threshold. Most importantly, there were significant interaction effects—SEO made paid ads more effective, and vice versa.
Step 3: Incrementality Testing
To validate the model insights, I implemented controlled experiments. We'd pause specific channels for set periods, test different budget allocations, and measure the actual business impact. This is the only way to truly understand incrementality—what would happen if you stopped or changed each marketing activity.
The results were eye-opening. Channels that looked terrible in attribution often had strong incremental impact. Brand search campaigns had "low ROAS" because they were mostly capturing demand that already existed, but pausing them hurt overall conversion rates across all channels.
Step 4: Budget Optimization
Armed with real understanding of channel effectiveness and interactions, I could make budget decisions based on actual incrementality rather than attribution lies. This meant investing more in channels with strong cumulative effects (like SEO content) even when they showed poor short-term attribution.
The framework also revealed optimal spend levels for each channel. Facebook ads delivered strong returns up to about $3K monthly, then hit diminishing returns hard. Google Search had consistent performance but limited scale. SEO content had the best long-term ROI but required 6-8 weeks to show impact.
Most importantly, the model showed that cutting any channel significantly hurt the performance of others. Our marketing worked as a system, not independent channels competing for attribution credit.
Statistical Foundation
Understanding correlation vs. causation in your marketing data is crucial—simple regression analysis reveals which channels truly drive incremental growth versus those just capturing existing demand.
Interaction Effects
Channels don't work in isolation; SEO content makes paid ads more effective, brand awareness amplifies performance marketing, and understanding these multiplier effects is key to proper budget allocation.
Incrementality Testing
The only way to validate your mix model is through controlled experiments—systematically pausing channels and testing budget shifts to measure real business impact rather than platform-reported metrics.
Data Hygiene
Clean, consistent data collection at the right time intervals makes the difference between actionable insights and statistical noise—aggregate weekly business metrics trump individual user tracking every time.
The shift from attribution-based to mix model-based budget allocation delivered immediate improvements in marketing efficiency. Within 3 months, we achieved 40% better cost per acquisition while maintaining the same lead volume—simply by allocating budget based on actual incrementality rather than platform-reported attribution.
More importantly, we stopped making expensive mistakes. Before the mix model, we were close to cutting SEO content investment because it showed poor last-click attribution. The model revealed that SEO was actually our highest-ROI channel when accounting for its cumulative effect on all other marketing activities.
The framework also helped us identify the optimal marketing budget size. Traditional attribution suggested we could scale indefinitely by adding more to our "best performing" channels. The mix model showed clear diminishing returns curves and helped us find the sweet spot where additional marketing investment stopped delivering positive ROI.
Beyond immediate performance improvements, this approach fundamentally changed how we thought about marketing measurement. Instead of fighting over attribution credit, teams started collaborating on overall business impact. Budget conversations became strategic rather than political.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The biggest lesson was realizing that accurate marketing measurement requires thinking like a scientist, not a tracker. Attribution models give us the illusion of precision while delivering fundamentally flawed insights. Statistical modeling at the business level provides the actual understanding needed for smart budget decisions.
Here are the key insights that changed how I approach marketing measurement:
Aggregation beats individual tracking - Business-level data reveals patterns that user-level tracking misses
Correlation isn't causation - Channels that get attribution credit aren't necessarily driving incremental growth
Interaction effects are massive - Marketing channels amplify each other in ways attribution can't capture
Time lags matter - Content marketing might take weeks to show impact but delivers long-term value
Diminishing returns are real - Every channel has an optimal spend level beyond which ROI plummets
Incrementality requires testing - The only way to know if a channel truly works is to turn it off
Platform metrics lie - Facebook and Google are incentivized to overstate their own contribution
This approach works best when you have multiple marketing channels running simultaneously and enough data to detect patterns (usually 3+ months of consistent activity). It's less useful for single-channel strategies or when testing completely new markets where historical data doesn't exist.
The biggest mistake I see is trying to implement mix modeling too early. You need sufficient data volume and marketing complexity before statistical analysis provides meaningful insights. Start with this approach once you're spending $10K+ monthly across multiple channels.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups, focus on modeling the relationship between marketing activities and trial signups, then separately model trial-to-paid conversion. Track cumulative content marketing effects on overall funnel performance, as content often has 4-6 week lag times in B2B.
For your Ecommerce store
For e-commerce stores, model marketing mix effects on both new customer acquisition and repeat purchase behavior. Account for seasonal patterns and promotional calendars when building your models, and test incrementality during non-peak periods for clearer signal.