Data moat in attribution software
Sean Frank, CEO of Ridge, on the state of ecommerce post-COVID
The real moat in measurement software is not the dashboard, it is the data exhaust from watching thousands of brands spend money across the same ad platforms. A company like Northbeam sees enough conversion paths, spend shifts, and channel mix changes to build better models for where credit should go after Apple privacy changes broke simpler tracking. That makes attribution software harder to copy than email or reviews tools, where competitors can reproduce most features with much less data and infrastructure.
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Northbeam is not just counting clicks. Its product maps full customer journeys, applies multiple attribution models, and uses modeled views and an in house device graph to reconnect ad exposure and purchases across devices. More spend flowing through the system gives it more examples for tuning those models.
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Haus is a close parallel, but for incrementality instead of attribution. Rather than assign credit touch by touch, it runs geo and time based tests to estimate what sales would have happened anyway. Ridge uses both because one tool helps allocate day to day budget, while the other checks whether a channel is truly causing extra sales.
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This is why analytics vendors have held pricing power longer than commodity Shopify apps. Ridge moved from Klaviyo to Sendlane and from Yotpo to Okendo because those products are mostly workflow software. It kept paying for Northbeam and Haus because the hard part is the underlying measurement engine, not the interface.
The next step is a split between cheap execution software and premium decision software. As privacy rules and channel fragmentation make basic tracking weaker, brands will keep consolidating around measurement platforms with the deepest data loops, strongest modeling, and enough customer scale to turn raw spend into a sharper read on what is actually driving growth.