RevOps · January 2026 · 10 min read

Your Attribution Data Is Lying to You

You spent six figures on marketing last quarter. The attribution report says paid search drove 40% of pipeline, events drove 25%, and content drove 15%. The rest is "unknown" or "direct."

Based on this, you're planning to increase paid search budget and cut back on content. After all, the data is clear.

Except it's not. That attribution report is built on a foundation of broken data, and the conclusions you're drawing from it are probably wrong.

The Attribution Accuracy Problem Nobody Talks About

Marketing teams obsess over which attribution model to use. First-touch vs. last-touch vs. multi-touch vs. time-decay. They debate algorithms and weighting and decay curves.

But here's what nobody wants to acknowledge: the model doesn't matter if the underlying data is garbage. You can have the most sophisticated attribution algorithm in the world, and it will still give you misleading results if it's working with incomplete, duplicated, or disconnected data.

Attribution tools don't create data. They analyze it. And what they're analyzing in most CRMs is a mess.

Five Data Problems That Break Attribution

1. Duplicate Records Fragment the Customer Journey

This is the most common attribution killer, and most teams don't even realize it's happening.

Sarah downloads a whitepaper through a paid search ad. She's created as a lead. A month later, she registers for a webinar using a different email. New lead record. Two months after that, she requests a demo. The SDR creates a contact manually.

Sarah is now three different people in your CRM. Her journey looks like this to your attribution tool:

• Record A: Paid search > whitepaper download > nothing
• Record B: Direct > webinar registration > nothing
• Record C: Unknown > demo request > opportunity

The attribution tool does its job perfectly. It attributes the opportunity to Record C, which shows "unknown" as the first touch because no marketing touchpoints are connected to it.

Your report now shows paid search influenced a whitepaper download that went nowhere, and an opportunity came from "unknown." Paid search gets zero credit. The webinar gets zero credit. And you have no idea what actually drove this deal.

Now multiply this by hundreds of duplicates.

2. Missing Contact-Account Associations Hide Account-Level Influence

B2B buying involves multiple people. You might have five contacts at a company who all engaged with your marketing before someone created an opportunity. But if those contacts aren't properly associated to the account, your attribution can't connect their activity to the deal.

The CTO read three blog posts. The VP of Engineering attended a webinar. The developer downloaded your technical docs. All valuable touchpoints. But when the opportunity closes, attribution only sees the touchpoints from the one contact who happens to be listed on the opportunity.

The problem gets worse with contact-company association issues. Contacts floating around without company associations are invisible to account-based attribution. Their touchpoints exist, but they're not connected to anything that matters for revenue reporting.

3. UTM Parameters Are Inconsistent or Missing

UTM parameters are how attribution tools know where traffic came from. But most companies have no standards for how these are set.

Your paid search manager uses "utm_source=google" while your social media person uses "utm_source=Google" and your content team uses "utm_source=google-ads" for the same thing. Your attribution tool sees three different sources.

Or worse, nobody's adding UTM parameters at all. Half your traffic shows up as "direct" because the links in your email campaigns don't have tracking. That "15% unknown" in your attribution report? A good chunk of it is probably email, but you'll never know.

4. Opportunity Contact Roles Are Wrong or Missing

In Salesforce, contact roles determine which people are connected to an opportunity. In HubSpot, it's deal associations. This is how attribution tools know which contacts' touchpoints should be credited when a deal closes.

The problem: sales reps rarely fill this out correctly. They add the one person they're talking to and ignore the other four people who were involved in the buying process. Or they add contacts after the deal is already closed, which means touchpoint timestamps don't match up right.

Some attribution tools try to auto-associate contacts based on email domain, but that creates its own problems. You end up including every contact at the company, even ones who had nothing to do with the deal.

5. Campaign Naming Has No Standards

Beyond UTMs, attribution relies on how campaigns are named and organized in your CRM. If naming is inconsistent, grouping and analysis become impossible.

"Q1-2026-Webinar-DataQuality" and "Data Quality Webinar Jan 2026" and "webinar_dataquality_q1" are the same thing to a human. They're three different campaigns to your attribution tool.

When you try to report on "how did webinars perform this quarter," you're missing campaigns because they're named differently. Or you're manually combining them, which introduces human error and takes forever.

How Broken Data Distorts Your Marketing Decisions

These data problems don't just make attribution reports inaccurate. They systematically mislead you in specific ways:

Channels with longer sales cycles look worse. Content and events often touch people early in the journey. If duplicates fragment that journey, early touchpoints get disconnected from eventual conversions. Paid search, which often touches people later (when they're actively looking), stays connected because there's less time for records to get duplicated.

"Unknown" and "direct" get inflated. Every missing UTM parameter, every manually-created contact without marketing touchpoints, every broken association adds to the "unknown" bucket. That bucket can easily be 20-30% of your pipeline, which means you're making budget decisions with a quarter of the picture missing.

Sales-sourced pipeline gets overcounted. When a duplicate is created manually by sales (without the marketing touchpoints from the original record), it looks like sales sourced the deal. Marketing's influenced pipeline shrinks. This creates org-level conflict over attribution that's really just a data problem.

Multi-touch models can't work right. Multi-touch attribution is supposed to give credit to every touchpoint in the journey. But if the journey is split across duplicates, there is no complete journey to analyze. You end up with a bunch of single-touch fragments that look like first-touch data.

How to Fix Your Attribution Data

The fix isn't changing your attribution model. It's fixing the data underneath it.

Step 1: Merge Duplicate Records

This is the highest-impact fix. When you merge duplicates, you're reuniting fragmented customer journeys. Suddenly that paid search touchpoint from six months ago connects to the demo request that became an opportunity.

The key is merging correctly. You need to preserve all touchpoints from all records, keeping the earliest first touch date and the complete activity history. If you merge and delete touchpoints, you've just made attribution worse.

See our guides on deduplication for Salesforce and HubSpot.

Step 2: Fix Contact-Account Associations

Every contact needs to be associated to the right company. This is especially critical for account-based attribution, but it matters for any attribution that tries to look at deal influence.

Start with contacts who have opportunities. Make sure every contact role is filled in correctly. Then work backward through accounts with open opportunities, ensuring all relevant contacts are associated.

For HubSpot specifically, see how to fix missing contact-company associations.

Step 3: Standardize UTM Parameters

Create a UTM taxonomy document that everyone follows. Define exactly what values to use for source, medium, campaign, and content. Build it into templates and tools so people don't have to think about it.

Common standards that work:

• Source: The platform (google, linkedin, facebook, email)
• Medium: The type (cpc, organic, social, email, referral)
• Campaign: The initiative (product-launch-q1, webinar-data-quality)
• Content: The specific asset or variation (banner-a, cta-footer)

Then audit existing links and fix the obvious ones. You can't go back and add UTMs to historical traffic, but you can clean up what's there and prevent future problems.

Step 4: Audit Opportunity Contact Roles

For every closed-won opportunity in the last quarter, check the contact roles. Are all buying committee members included? Are the roles accurate (decision maker, influencer, etc.)?

This is tedious, but it's often eye-opening. Teams regularly discover that half their opportunities are missing key contacts. Adding them retroactively updates the attribution picture.

Step 5: Standardize Campaign Naming

Create a naming convention and enforce it. Something like: [Year]-[Quarter]-[Type]-[Topic]-[Audience].

Example: 2026-Q1-Webinar-DataQuality-Enterprise

It's not glamorous, but it's the only way to get consistent roll-ups. And go back and rename existing campaigns to match. Yes, it takes time. Yes, it's worth it.

What Good Attribution Data Looks Like

After you fix these issues, your attribution reports will change. Sometimes dramatically.

That "unknown" bucket shrinks. Channels that looked underperforming get credit for touchpoints that were previously disconnected. The actual customer journey becomes visible instead of the fragmented version.

You might discover that content is driving more than you thought, because early touchpoints are now connected to later conversions. Or that a specific event is really good at accelerating deals, something you couldn't see when the touchpoints were split across duplicates.

The decisions you make from this data will be better. Not because you changed the attribution model, but because the model finally has accurate data to work with.

The Ongoing Work

Data quality isn't a one-time fix. New duplicates get created. New campaigns launch without proper UTMs. Contact associations break when records get updated.

Build attribution data quality into your regular operations:

• Run duplicate detection monthly
• Audit contact-account associations quarterly
• Check UTM parameters on every new campaign before it launches
• Review opportunity contact roles as part of deal review

This is operations work. It's not exciting. But it's the difference between attribution that guides strategy and attribution that misleads you into bad decisions.