The Attribution Challenge: Why Your Conversion Counts From Ad Platforms Don’t Match Your Analytics

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You pull together reports from all your advertising platforms (e.g., Facebook, Google Ads, your DSP, etc.) and add up the conversions, only to find that the total number is triple what you’re seeing in your analytics solution or ecommerce back-end.

Sound familiar?

Here’s why conversion counts are inflated

To better understand why conversion counts are inflated, let’s look at an example user conversion path for “Dave” who is buying a razor from “Ron’s Razors.”

1. Dave conducts a Google Search for men’s razors.
2. He clicks on an ad from Ron’s Razors.
3. Dave is later retargeted by Ron’s Razors via AdRoll.
4. The next day, Dave clicks on a Facebook ad from Ron’s Razors. This takes him to the company website and he purchases a razor.

Looking at the conversion reports across Google Ads, AdRoll and Facebook, you’ll probably find they are all taking credit for the sale, while the company’s internal database confirms that there was only one conversion (Dave’s purchase).

Here’s what’s going on: Google Ads, AdRoll and Facebook each have their own pixel that’s tracking Ron’s Razors conversion event. When Dave converted, each pixel triggered independently. This explains why there are three reported conversions, even though there was actually only one conversion.

how it looks when conversions are inflated

Each ad platform is essentially saying, “I served an ad that led to a conversion, so I’m taking credit.”

Why is every platform taking credit? 

When calculating conversions, ad platforms have no incentive to consider any other platform’s influence. Why would they put a ton of work into building out technology that could ultimately make them look bad?

Understandably, ad platforms need to make their CPA (cost per acquisition) for every advertiser look good. More conversions mean lower CPAs, which incentivizes advertisers to keep spending money on their platforms.

Finding a single source of truth

You’ll always be over-counting if you add up the conversion counts reported by each platform, and only a cross-channel solution can provide a single source of truth. Cross-channel solutions take the entire user conversion path into account and know how to dedupe conversion data across all channels — so reports show the actual number of conversions that occurred.

If you’re using Google Analytics as your single source of truth, the challenge then becomes understanding how much credit each platform truly deserves for driving a conversion. This is because Google Analytics only provides rule-based attribution models (e.g., first-touch, last-touch, even weight) and data-driven models that are uncustomizable.

Rule-based models are problematic because they’re based on overly simplistic assumptions about your marketing data. Take the last-touch attribution model for example. This assigns 100% of conversion credit to your last marketing touchpoint and does not acknowledge any other touchpoint.

Uncustomizable data-driven models are problematic because you can’t tailor a model to the precise set of assumptions that you want to evaluate. For example, you might want to give less credit to display ad touchpoints late in the conversion funnel. You conclude that by that point, you’ve already won the customer through other marketing efforts.

Google Analytics also lacks other functionality needed to fully understand the picture. It doesn’t support spend ingestion (besides Google Ads). It also doesn’t support other marketing channels or events such as offline, view-throughs, coupons and more.

Any attribution is better than none at all, but a custom model that’s based on your actual historical data will provide a more accurate picture of which marketing efforts are performing best. This will put you in a position to make sound decisions about where to invest marketing resources.

To learn more about cross-channel marketing attribution and customized models check out

About the author

Michelle SpagnoliMichelle Spagnoli is currently a marketing manager at Rockerbox, a marketing attribution platform for direct-to-consumer brands. Prior to joining Rockerbox, Michelle has held marketing positions at various SaaS startups. She is a graduate of Fordham University, a registered yoga instructor and an avid cook.