A few years ago now, Google removed rules-based attribution models from Google Analytics. If you used Universal Analytics, or even early versions of GA4, you may remember these: last click, first click, linear, time decay, position-based. You could select a model that reflected your marketing efforts or customer journey, apply it to your conversion data, and get a clear view of how credit was distributed across the channels that touched a conversion.
These attribution reports could highlight the importance or impact of upper-funnel and awareness content that won’t necessarily get the credit it deserves when looking at a last click or ad-focused model. I can’t count the number of times I’ve seen investments in organic social traffic how up in the first click or time decay reports, but disappear in last click.
When Google replaced these with last click or data-driven attribution, which is a bit of a black box, it made determining the importance of these broader awareness marketing initiatives much more challenging.
It’s not that the new options are bad, exactly. The data-driven model uses machine learning to assign credit based on patterns in your conversion data, and Google’s position is that this is more accurate – especially in a cookie-reduced world. But while that may be true in some cases, it is also a model you cannot inspect, cannot replicate in a spreadsheet, cannot explain to a client in a meeting, and cannot meaningfully compare across time periods or assisted conversion values of specific campaigns, when the underlying model updates itself.
For large advertisers with high conversion volumes and dedicated analytics teams, data-driven attribution is probably fine. The model has enough data to work with, and there are people whose job it is to interpret the outputs. For the marketing manager at a 50 to 500 person company running a handful of campaigns, which is most marketing managers, it is a significant step backwards in practical utility.
What was actually lost is not a feature. It is control. Rule-based attribution models were transparent by design. You could disagree with a model’s assumptions, switch to a different one, and immediately see how your channel credit changed. That transparency made it possible to have an informed conversation about what was actually driving performance.
The workaround most teams have landed on is some combination of UTM parameters, last-click data from individual ad platforms, and manual reconciliation in a spreadsheet. This works, after a fashion. It is also time-consuming, error-prone, and depends entirely on everyone applying UTM conventions correctly every time. In practice, they don’t.
CampaignCheck is my attempt to recapture some of that control as a standalone tool that a marketing team controls rather than a feature inside a platform with other priorities. It is campaign-scoped rather than site-wide, which sidesteps most of the consent and data collection complexity, and it uses rule-based models because transparency is the point, not a limitation.
It is not finished yet. But this is why it exists.