Measure site and spend against one conversion

Each platform reports from its own perspective, applies its own attribution window, and counts an assisted conversion as a full conversion. One purchase can appear as 1 order in your store, 1.4 conversions in Google Ads on a 30-day window, and 1.2 conversions in Meta Ads on a 7-day click. TrustData starts from your Shopify orders or your CRM, deduplicates across platforms, and only then applies a model, so total credited conversions always equals the orders you actually took. The gap between the two views is typically 30 to 50%.

Data-driven attribution is a statistical model that uses your actual conversion data to calculate the marginal contribution of each marketing channel. Unlike rule-based models such as last-click, first-click or linear, it analyses converting and non-converting paths together to work out which touchpoints genuinely changed the outcome. TrustData's DDA is built on a cooperative game theory framework, so a channel's credit is its average marginal contribution across every combination of channels it appears in, rather than a share handed out by a fixed rule.

No, and it should not. Keep Google Ads and Meta Ads for campaign management and bidding, because those platforms need their own signal to optimise against. TrustData answers a different question: how should the total budget be split across channels, given deduplicated conversions measured the same way on every surface. One is in-platform optimisation, the other is cross-platform allocation, and using the first to answer the second is what produces the over-reporting in the first place.

No. We do not replace Looker, Metabase or a warehouse. The difference is that a BI tool visualises whatever you load into it and takes no position on whether the numbers are right, while this starts by deciding what a conversion is and measures every channel against that one definition. If you want the raw rows in your own warehouse you can have them, exported to your storage on the same schema the interface reads, and you can build whatever you like on top.

No, and nothing here will imply it does. Every attribution model, DDA included, distributes credit among the touchpoints that were present. None of them can tell you what would have happened if the ad had never run, because that path does not exist in your data. Answering that needs a controlled experiment with an untreated control group, which is what the incrementality page covers, including the fact that no customer test has concluded yet. Attribution tells you where the credit went. Only an experiment tells you whether the credit was real.