Why Most B2B Attribution Fails (and What to Do Instead)

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B2B attribution: the bottom line

• B2B attribution fails in two predictable ways: teams avoid it entirely or overengineer fragile systems.
• The goal of attribution isn’t perfect credit, but confident decision-making about budget, channels, and pipeline.
• Strong attribution starts with fundamentals: frozen lifecycle definitions, protected source fields, UTM discipline, and data coverage.‍
• 80% confidence and coverage is good enough to make smarter bets and reduce budget debates. Start there, then iterate.

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Why B2B attribution is harder than it looks

Why is it so hard to measure B2B marketing efforts nowadays? The reason is simple: buying journeys have become more complicated.

The average B2B purchase now involves around 13 stakeholders, often spanning multiple functions, and nearly 90% of B2B purchases involve two or more departments.

“The buying journey is complicated. It’s not linear, it’s not one channel, and it’s not one buyer.” — Alex Biale, Founder, Domestique

Yet many brands stick with simpler attribution models, which may work for single-buyer ecommerce journeys with short conversion windows and linear paths but consistently miss what actually drives revenue in B2B environments.

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📺 Watch the full webinar: From dashboards to decisions

This article distills the most actionable insights from From Dashboards to Decisions: B2B Attribution Strategies for Real B2B Buying Journeys.

In the full session, Alex Biale (Founder, Domestique) and Kevin Lord Barry (Co-Founder, Right Percent) dive deeper on:

• How attribution data actually flows from ad platforms into your CRM
• Why most attribution models lose trust over time
• How to align marketing and sales around what’s actually working‍

👉 Watch the full webinar to hear the examples, live Q&A, and practical walkthroughs straight from the source.

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B2B attribution typically fails in one of two ways

Across companies, Alex and Kevin see the same two critical mistakes over and over.

1) Teams skip attribution altogether

Ignoring something just because it’s difficult? Never a good idea, in business or in life. Yet some organizations decide attribution is “never going to be perfect,” so they avoid investing in it.

The result is predictable: strategy and budget decisions are driven by opinions rather than insights—and internal prioritization debates rage on since there are no north star metrics. 

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Related article

What other mistakes are B2B teams making right now? Learn what's trending in 2026—plus some bold takes—according to several marketing leaders.

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Alex framed this with a simple test: If you came into extra budget next quarter, could you confidently know where to allocate it and what impact it would have?

“If you had an extra $100,000 to spend next quarter, could you say—confidently—how much pipeline it would create and where you’d invest it?” — Alex Biale, Founder, Domestique

If you can’t answer that question (or its inverse: what happens if we cut $100K?), your attribution is not okay.

2) Teams overbuild before fixing the basics

Realizing your measurement is broken is a good thing. But valid attribution models aren’t built in a day, and attempting to do so does more harm than good.

So before thinking about complex multi-touch or AI-driven models, fix foundational issues. Otherwise you’ll get:

• Models that work briefly, then break at scale
• Shifting definitions and unexplained anomalies
• Stakeholders that eventually lose trust in and ignore the data (you’re back at square one if this happens)

“If the underlying data isn’t solid, even the best attribution model just gives you false confidence.” — Kevin Lord Barry (Co-Founder, Right Percent)

Aim for (statistical) confidence, not perfection

Attribution is not about perfectly assigning credit to every touch, which is impossible. But it does need to give you enough confidence in making credible, repeatable decisions, such as:

• Where should we invest more budget?
• Which programs influence pipeline—even if they don’t source it?
• Where does the buying journey stall?

“This isn’t just a data exercise. The goal is to make capital and resource allocation decisions.” — Alex Biale, Founder, Domestique

In practice, ~80% accuracy with strong data coverage is far more valuable than a theoretically perfect model built on incomplete inputs.

Fix the foundation before choosing a model

Before thinking more seriously about attribution models, teams need to lock in a few operational essentials.

1) Defining lifecycle and qualification metrics

Clearly define:

• Lead
• Qualified lead
• Opportunity
• Closed-won

Marketing and Sales must align on these definitions from the start and hold them steady for as long as possible. Constant redefinition makes trend analysis and attribution outputs impossible to trust.

2) Protect source-of-truth fields

Opportunity and deal source fields often influence compensation, forecasting, and budget decisions. If these fields are editable by reps or overwritten by integrations, attribution outputs lose credibility quickly.

3) Enforce UTM and campaign taxonomy discipline

Attribution only works when raw inputs are consistent:

• Standard UTM parameters (source, medium, campaign, etc.)
• Scalable campaign naming conventions
• Stable channel groupings

Yes, it can be tedious work, but it’s worth it in the long term: Changing taxonomy too frequently makes QoQ and YoY comparisons impossible.

4) Track and improve data coverage

Data coverage should answer a simple question: What percentage of new leads and opportunities actually have usable attribution data populated?

If coverage is low—say 30%—the issue isn’t the attribution model, but broken wiring between ad platforms, forms, CRM imports, or offline sources like events and partners.

Most B2B teams should aim for ~80% coverage (there’s that number again!) before adding complexity.

Choosing the “right” attribution model(s)

There is no universally correct attribution model—only models that fit different stages and goals.

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Related guide

For an in-depth look on how to approach measurement and experimentation, check out our complete guide to experimentation—whether you're a beginner or a pro.

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Common use cases include:

• First-touch: Understanding which channels drive net-new demand
• Last-touch: Evaluating conversion and handoff efficiency
• Multi-touch (linear, position-based, time decay): Capturing influence across long buying journeys
• Advanced or ML-driven models: Useful only when data volume, coverage, and governance are already strong 

“First touch, last touch, any touch—each one tells you something real. But you can’t expect one model to tell the whole story.” — Kevin Lord Barry (Co-Founder, Right Percent)

What “good” attribution looks like

As a reminder, before investing in advanced tools, ensure you’re already implementing the basics of effective B2B attribution:

• Stable lifecycle and source definitions
• Disciplined UTM and taxonomy standards
• ~80% data coverage across leads and opportunities
• Locked source fields in CRM
• Outputs tied directly to budget and pipeline decisions

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Better B2B measurement awaits with Right Side Up

If our webinar or article exposed some flaws in your attribution setup, Right Side Up can help. We’ll help you figure out what’s actually working by building a tailored, scalable B2B measurement strategy for your brand. 

Contact us today to talk through your attribution challenges—or start by watching the full webinar to align your team on what “good” actually looks like.

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