Data Requirements Checklist Before
Buying Multi-Touch Attribution Software

What Data Does Multi-Touch Attribution Software Actually Need?

When you start evaluating attribution software, it’s easy to get pulled into the product itself. Which models does it support? Does it connect to the CRM? What does the reporting look like? How much will it cost? Those are all reasonable questions, but eventually someone has to look underneath the interface and work out whether the underlying data is there.

At a minimum, a B2B attribution system needs three categories of data: marketing interaction data, identity data, and commercial outcome data.

Your Marketing Stack Audit for Attribution Readiness

Every category in the table above lives somewhere in your stack right now. Use this checklist to document exactly where, before comparing platforms, or making any changes.

Start With Your Website Analytics and Review the Data Being Captured

The first part of the audit is straightforward: establish whether your website analytics contain the interaction data shown in the table above. For most B2B organisations, that means checking the GA4 property, tracking setup, key pages, events, and traffic-source data.

Step 1: Confirm the Correct GA4 Property and Web Stream

First, confirm that the GA4 property and web stream you are auditing are the ones actually collecting data for the website and buyer journey in scope.

Do not assume that because GA4 is installed, the implementation is suitable for attribution. A site can have valid page-view data while still losing important conversion or identity information.

Step 2: Confirm Page-Level Tracking

For content attribution, page-level activity is particularly important. At minimum, validate that important pages generate page-view activity in GA4:

You can test this yourself in a few minutes. Pick five important pages from different parts of your site and check that visits to each one are appearing correctly in GA4. If a page is missing or reporting incorrectly, fix the tracking before relying on attribution data from it.

Step 3: Identify Your Meaningful Conversion Events

Not every event in GA4 deserves to be treated as a conversion, and this is where a lot of attribution setups go wrong. Use the inventory below to check which events are currently tracked and whether the events marked as commercially important are being captured.

Step 4: Check Traffic-Source Dimensions

This is another area where a GA4 account can appear perfectly healthy while the underlying data is harder to use than it should be.

Open your GA4 reports or explorations and check whether you have usable values for:

What you want to see here is consistent, usable data across the journey. Missing or inconsistent traffic-source values are worth flagging now, particularly where they affect campaigns or channels that contribute meaningfully to pipeline.

Check Your CRM for Complete Pipeline and Revenue Data

For this part of the audit, focus on whether your deal records are complete, consistently maintained, and tied back to the right contacts and companies.

That is what allows an MTA platform to connect earlier marketing interactions to a specific opportunity and, eventually, the revenue that came from it.

Step 1: Define the Commercial Outcome

Before checking individual deal fields, decide what the attribution analysis needs to explain. For most B2B organisations, that will include some combination of:

Write down the primary outcome and any secondary outcomes you want the software to report. For example: primary outcome: closed-won revenue; secondary outcomes: qualified pipeline and closed-won deals.

This gives the rest of the CRM audit something concrete to work towards. It also prevents the vendor from effectively deciding what “revenue” or “pipeline” should mean for you.

Step 2: Audit Your Deal Records

Once the commercial outcomes are clear, check whether your CRM records contain the information needed to identify and measure them.

For a representative sample of recent deals, verify that these fields are populated and reliable:

Step 3: Check Deal-Stage Consistency

Next, look at how opportunities actually move through the pipeline. If your sales process has clearly defined stages, the CRM should show a reasonably consistent progression between them.

Review the last 50–100 opportunities, where volume allows, and check:

This is one of those areas where a CRM can look perfectly healthy from an administrative perspective while still creating problems for attribution. If closed-won opportunities are regularly left in open stages, for example, a report cannot reliably distinguish active pipeline from realised revenue.

Check Whether Your UTM Tracking Is Consistent Across Campaigns

UTM tracking is easy to overlook because campaigns can still run normally when the naming is messy. The problem usually appears later, when the same channel or campaign starts showing up under several different values in your reports.

These may all represent the same activity, but GA4 will treat the values as different. That can fragment channel reporting and make the underlying attribution data harder to work with.

Step 1: Review Your Existing Campaign Data

Start with the data you already have rather than creating a naming standard in isolation. Pull a meaningful sample from GA4, ideally covering several months, and look for:

The aim is to see how much inconsistency already exists and where it is concentrated.

Step 2: Create a Consistent UTM Naming Standard

Once you know where the problems are, define the naming rules your marketing teams should follow going forward.

The exact naming system is up to your organisation. What matters is that the same source, channel, and campaign are represented the same way every time.

Step 3: Check How Each Major Channel Is Tagged

Work through your main acquisition channels and document how each one currently sends campaign information into GA4.

For each channel, note whether the data comes from manual UTM tagging, platform integrations, auto-tagging, referrer information, or another method. This helps separate genuine gaps in campaign tracking from channels that are intentionally handled differently.

Step 4: Investigate Direct Traffic

Large amounts of (direct) / (none) traffic are worth investigating, particularly if they appear around campaigns where you would expect source information to be available.

Do not automatically assume that all direct traffic is misattributed. Instead, look for patterns: specific landing pages, campaigns, channels, or periods where direct traffic is unusually high. Record anything that needs further investigation rather than trying to explain every visit individually.

Identify the Data Quality Problems That Could Distort Attribution Results

By this point, you’ve checked the main data sources that feed attribution: website activity, conversion events, campaign data, CRM records, and the connections between them. The next step is to look across those findings and work out which problems could actually affect the numbers.

Build a Data-Quality Scorecard

The table above gives you the common failure points, but simply knowing that a problem exists is not enough. You need some sense of its scale.

Go back through the checks you have completed and record the result for each one.

Once you’ve quantified the gaps, you can decide what needs fixing internally before you start comparing vendors, and what you should expect the attribution platform to handle.

Use Your Data Audit to Run a Sharper MTA Vendor Evaluation

By this point, you should have a much clearer picture of what your data can support, where the gaps are, and which issues need attention.

Now you can take that assessment into the vendor process instead of starting with a blank sheet and comparing whichever features happen to appear in the demo.

Step 1: Create Your Minimum Data Requirement

Work backwards from the commercial questions the platform needs to answer, and write the requirement clearly enough that every vendor is assessed against the same bar. For example:

I need to see more than website activity in isolation. The platform should connect those interactions to known contacts and CRM opportunities, so the team can understand which channels, campaigns, and content are creating momentum in the pipeline and contributing to closed-won revenue.

Then translate that into technical requirements:

This becomes your baseline for the rest of the evaluation. A vendor can have an impressive feature list and still fail the more basic question of whether it can work with the data your business actually has.

Step 2: Ask What the Vendor Requires From You

Every vendor should be able to explain what needs to happen on your side before attribution can work properly. Here’s how AttributeIQ answers each one:

Step 3: Decide What Needs Fixing Before You Sign

You now know what the attribution platform needs, what your data can currently provide, and where the gaps are. Before you move forward, put each gap in the right bucket. Some are yours to fix, some are genuine vendor limitations, and some simply need a clear commitment before you sign.

Once you’ve worked through the audit and fixed the gaps that could affect your results, you’re in a much better position to evaluate attribution properly. If GA4 and HubSpot are ready to go, you can connect them to AttributeIQ and start seeing which marketing activity is influencing pipeline and revenue. Try it free for 14 days →

Author

Muiz Thomasin

Founder & CEO, AttributeIQ

Muiz Thomas is the Founder & CEO of AttributeIQ, a multi-touch attribution platform. He previously founded GrowUp, a B2B SEO agency, and has worked with SaaS, construction technology, and enterprise software companies on organic growth, content strategy, and demand generation. He has helped connect marketing programmes to £5M+ in qualified pipeline and writes about attribution, content ROI, and revenue measurement.