Cross-channel marketing attribution has never been more accessible. With digital analytics tools providing near-instant feedback, it’s easy to believe we have accurate attribution and a complete understanding of our marketing performance. But the biggest challenges of marketing attribution come from this exact illusion.

The truth is simple: Just because something is easy to measure doesn’t mean it’s driving growth.

Most marketing attribution models overvalue channels with clear click-through paths and undervalue the marketing activities that shape demand, influence the customer journey, and create long-term brand equity.

This isn’t a lack of data. It’s the limitations of attribution itself and it affects nearly every marketing team today.

Why Marketing Teams Struggle With Attribution

Platforms like Google Analytics, Shopify, and third-party attribution tools provide clean, tidy dashboards built around a linear cause-and-effect story:

Click → Conversion → Revenue → ROAS

That simplicity feels reassuring. It feels logical. The CFO trusts it.
So what happens next?

• Budgets flow toward easily measurable channels
• Performance looks strong early on
• The bottom-of-funnel gets saturated
• Demand capture peaks
• Demand creation stalls
• Growth plateaus

We run 75–100 incrementality experiments each month—across B2B marketing, ecommerce, retail, and subscription businesses—and this pattern repeats across industries.

The core issue?

Measurement accessibility ≠ marketing effectiveness.

Most marketers don’t have a marketing attribution problem. They have an attribution bias problem.

The Hidden Attribution Bias: Why Harder-to-Measure Channels Get Undervalued

Most marketing attribution challenges come down to this: Attribution gives disproportionate credit to what can be easily tracked.

Channels with clean user-level data—Meta, Search, Shopping, Performance Max—look like heroes in last-touch or even data-driven attribution models.

Meanwhile, channels that influence consumer behavior without a click—TV, YouTube, out-of-home, display, audio, influencers—look ineffective in traditional attribution models.

Real Examples of How Attribution Fails

TV & Streaming
TV increases branded search, direct traffic, and overall conversion rate—but none of that shows up in a linear attribution model.

Out-of-Home (OOH)
OOH drives cultural relevance and memorability, but appears as flat traffic because its impact occurs across various regions, timeframes, and device mixes.

Brand Campaigns
Brand campaigns influence how customers feel about a product—but attribution models capture only the final click.

Email marketing
Email often receives too much credit because of last-touch rules, not because it generated demand.

Multi touch attribution (MTA) tries to solve this, but MTA itself has limitations:

• Requires perfect, person-level identity matching
• Breaks with cookie loss
• Can’t follow cross-device behavior
• Overweights lower-funnel marketing touchpoints

This is why relying solely on an attribution model can create the illusion that some channels are underperforming when, in fact, they are driving massive incremental value. This is also why it’s essential to compare MMM vs. MTA to determine which is best suited for your business. 

The Core Challenges of Marketing Attribution

Below are the most significant structural issues marketers encounter when attempting to establish accurate attribution frameworks.

1. Attribution Overvalues Click-Based Channels

Last-touch attribution funnels budget toward what’s easy to track—not what works.

• Search looks great because it captures high-intent demand
• Remarketing looks amazing because users were already planning to buy
• Performance Max appears efficient because it harvests branded traffic
• Social prospecting, YouTube, OOH, and TV look weak

But these channels are critical inputs into the channels that look strong. A flawed attribution model hides that relationship.

2. Hard-to-Measure Channels Don’t Fit the Attribution Mold

Brand-building channels don’t produce immediate conversions, and they don’t produce trackable user flows.

This creates an attribution challenge: Long-term influence is not visible in short-term models.

Brand impacts:

• consideration
• search volume
• conversion rates
• pricing power
• customer lifetime value (LTV)
• customer behavior

None of this shows up cleanly in standard attribution data.

3. Multi-Touch Attribution (MTA) Still Isn’t the Magic Answer

Even with MTA or data-driven attribution (DDA), platforms struggle to:

• Stitch together anonymous user-level events
• Account for offline influences
• Track cross-device and cross-channel behavior
• Capture the halo impact of brand marketing activities

For many marketers—especially in B2B marketing—accurate attribution is nearly impossible without experimentation.

4. Digital Marketing Attribution Ignores Lagged Impact

Traditional attribution models assume impact is immediate.
But in reality, many high-performing channels show delayed incremental lift.

Example:
A YouTube prospecting campaign may have a 0.2 short-term ROAS, but a 3.0 long-term incremental ROAS.

Attribution will mark it as a failure. Incrementality measurement reveals its true value.

5. Attribution Doesn’t Account for Diminishing Returns

Attribution is terrible at showing the marginal impact of the next dollar spent.

As you increase spend, marginal ROAS declines—but attribution models treat all conversion credit the same.

This is why brands overinvest in already saturated channels while starving upper-funnel activities that could grow their customer base.

How Top Marketers Avoid the Attribution Trap

The companies that overcome the challenges of marketing attribution don’t rely on a single attribution solution. They use a portfolio of measurement methods—each one complementing the others.

Here’s what they do differently.

1. They Use Incrementality Testing as Their North Star

Incrementality tests reveal causal impact—not just correlated conversions.

These tests show:

• How many conversions would have happened without the campaign
• How spend impacts revenue at different levels
• Which marketing channels truly scale vs. just capture demand

Incrementality testing gives marketers the truth that attribution models simply cannot.

2. They Combine Models Instead of Choosing One

Elite teams build a balanced attribution methodology using:

• Incrementality testing
• Marketing mix modeling (MMM)
• Platform-level directional signals
• Proxy metrics for top-of-funnel
• Geographic and channel-level lift tests
• Qualitative customer insights

This approach produces far more accurate attribution analysis across different marketing channels.

3. They Build Proxy Metrics for Harder Channels

Some examples:

• OOH → branded search lift, store traffic, geo-level conversion lift
• TV → reach/frequency combined with MMM results
• Brand campaigns → view-through engagement, search trends
• B2B marketing → pipeline velocity, weighted opportunities, demo quality

Not every outcome needs a click-based attribution model to reveal value.

4. They Analyze Holistic Performance

Instead of obsessing over ROAS, they track:

• MER / marketing efficiency
• CAC and CAC payback
• Cohort LTV
• Brand impression share
• Revenue lift by geo
• Overall marketing spend allocation

This prevents the obsession with attribution data from killing long-term growth.

What the Next Generation of Attribution Looks Like

Marketing attribution is evolving. The companies that win will be the ones that treat attribution as directional, not definitive.

The future will revolve around:

• Experimentation
• Customer journey analysis
• Cross-channel insights
• Causal funnel measurement
• Mix modeling
• Privacy-safe data signals
• Channel saturation curves
• Scenario planning tied to marginal ROAS

Marketers who build systems around how people actually buy, rather than how attribution tools assign credit, will unlock the next era of growth.

The Biggest Challenge of Marketing Attribution Is Believing It’s Accurate

Attribution is helpful—but incomplete. The biggest risk is assuming it tells the full truth.

It doesn’t.

If you want to grow beyond surface-level optimization, you need a measurement approach that values:

• Long-term brand health
• Upper-funnel investment
• True incremental contribution
• The full customer journey
• Experimentation and modeling
• Real human behavior—not just trackable events

At fusepoint, we help brands overcome the challenges of marketing attribution with marketing performance consulting built on incrementality, causal inference, and strategic analytics.

If you’re ready to understand what truly drives growth, not just what’s easy to measure. Let’s talk.