Why click attribution might kill your brand?
Matias Koski, Senior Data & Analytics Consultant at Genero.
Marketers often rely on click attribution to measure success, but this approach can be dangerously misleading. By focusing only on the last click before a conversion, businesses risk misallocating budgets and undervaluing brand-building efforts that drive long-term growth. This article explores why click attribution falls short and outlines how you can get a clearer picture of what actually drives revenue by combining different measurement methods.
Now, let’s take a closer look at the problem.
Imagine you’re trying to figure out which of your marketing efforts actually convinced a customer to buy. Click attribution offers a simple answer: credit goes to the last thing someone clicked before they converted. It’s easy, it’s measurable and unfortunately, misleading.
Think about your own buying behavior. Did you ever click a Google ad and immediately made a purchase? Maybe. But more likely, you saw an ad days before, checked out a review, talked to a friend, got retargeted on social, and then finally clicked and bought. Clicks are just one step in a much bigger journey, yet many marketers still let them dictate their entire budget.
This over-reliance on click attribution is leading companies to make the wrong budget decisions spending too much on what looks good in reports and too little on what actually drives revenue. But here’s the good news: whether you’re running a global brand or a lean marketing team, there are better ways to measure impact.
Why are we stuck with click attribution?
Click attribution stuck around for so long because it’s easy. It gives quick, seemingly precise numbers. And when performance marketing teams need to justify spend, a clear “X clicks led to Y sales” story is tempting.
The problem? It’s like only crediting the waiter for a great meal—ignoring the chef, the ingredients, and everything that got the dish to your table.
Even advanced click-based models (like time decay or data-driven attribution) only look at clicks. They completely ignore:
- Display and Video Ads – You might not click a YouTube ad, but that doesn’t mean it didn’t influence you.
- Brand Awareness – Seeing an ad on social media or TV often plants the idea of a product long before a click happens.
- Word-of-Mouth & Offline Influence – Recommendations from friends, in-store experiences, or events all drive sales, but they don’t generate “click data.”
- The Role of Organic Search & Brand Traffic – If people search for your brand, something convinced them to do that—was it really just the last ad they clicked?
Relying only on clicks to measure success means over-investing in direct response ads and under-investing in channels that drive long-term growth.
How to Escape the Click Attribution Trap
Instead of relying solely on clicks, modern marketers are moving toward a multi-method measurement approach—one that combines:
1. Marketing Mix Modeling (MMM) – The Big Picture
MMM is a statistical approach that analyzes historical data to measure the impact of marketing across channels, including digital, offline, and brand marketing efforts.
✅ Looks at all marketing touchpoints (even those without clicks)
✅ Works across online & offline channels (TV, billboards, organic search, etc.)
✅ Helps with budget allocation by showing which channels truly drive revenue and gives suggestions on how to split the budget optimally between channels
How it works: MMM looks at sales data over time and identifies patterns between marketing spend and revenue growth, factoring in external influences like seasonality, economic trends, and competitor activity.
💡 Example: A brand using MMM might discover that TV ads indirectly drive more paid search conversions, leading to better budget allocation between the two.
2. Incrementality Testing – Proving What Actually Works
While MMM provides high-level insights, incrementality testing helps prove whether a specific campaign or channel is driving additional sales.
✅ A/B testing approach to measure real impact
✅ Helps validate MMM and fine-tune attribution models
✅ Can be done at different scales (from country-wide geo tests to small ad set experiments)
💡 Example: A brand might pause Facebook ads for a test group while keeping them live for another, then compare conversion rates to see if the ads are truly driving new sales or just capturing demand that would have happened anyway.
3. Smarter Attribution – The Middle Ground
Attribution models aren’t useless—they just need calibration. Data-driven attribution (DDA) (which uses machine learning to assign credit across touchpoints) combined with MMM and incrementality testing gives a more complete view of performance.
✅ Useful for daily campaign optimization
✅ Provides real-time insights
✅ Still limited to digital interactions, so needs validation from MMM & incrementality tests
“But What If We Don’t Have the Budget for MMM or Incrementality Testing?”
For many companies, the challenge with MMM isn’t just technical—it’s financial. Traditional MMM is most effective when a company has somewhere around 1M in annual marketing spend. This is because:
- MMM relies on historical data across multiple channels. If a company spends too little on certain channels, there won’t be enough variation in the data to isolate their impact.
- Results improve with more data points. Companies with small budgets tend to change marketing spend less frequently, making it harder to detect meaningful patterns.
- Running MMM requires investment in analytics. While some companies build internal MMM models, others rely on external vendors, which can cost hundreds of thousands of dollars per year.
This is why some companies hesitate to invest in MMM—not because it isn’t useful, but because they don’t meet the spend threshold where MMM produces reliable insights.
Similarly, full-scale incrementality testing can be costly, especially for geo-holdout experiments that require turning off ads in certain regions.
So what can companies do if MMM and large-scale testing are out of reach?
Run “Mini” Incrementality Tests – Even without a massive test budget, brands can:
- Pause-and-Resume Tests – Pause ad spend in a region for a short period and compare sales.
- Targeted A/B Testing – Instead of testing at a country level, test a small audience segment or a single campaign.
- Use Free Platform Lift Studies – Google and Meta offer free lift studies (if you meet their minimum spend).
Leverage CRM & E-commerce Data – If MMM isn’t an option, brands can still analyze:
- First-time vs. returning buyers – Is marketing actually acquiring new customers?
- Sales spikes vs. ad spend – Did revenue grow before ad spend increased? If so, marketing might not have been the cause.
- Cross-channel interactions – Do TV or YouTube ads correlate with higher direct and organic search traffic?
The bottom line: Click attribution might hold you back
Marketing budgets are under pressure. CFOs are demanding more proof that marketing spend drives real business value. If you only measure success by clicks, you’re leaving massive gaps in your analysis—and likely making suboptimal budget decisions.
The companies winning today don’t just optimize for clicks. They optimize for true business impact by combining:
- MMM for strategic, high-level budget planning
- Incrementality testing to validate which channels are actually driving growth
- Smarter attribution models to optimize campaigns in real-time
Even if a company isn’t ready for full-scale MMM, they can still apply the principles of marketing measurement by running lightweight tests, leveraging CRM data, and questioning click-based assumptions.The key isn’t to replace one method with another—it’s to combine multiple approaches to get the clearest picture of what’s really driving revenue.
Matias Koski, Senior Data & Analytics Specialist at Genero.