Incrementality by Northbeam
A new kind of methodology for modern incrementality
What makes most incrementality tests fail, and how do you prevent it? This whitepaper walks through Northbeam's full incrementality methodology: from how we analyze your business data before a test runs, to how results feed back into your MTA and MMM so every test becomes a daily input rather than a quarterly artifact.
Shuling Ding,
Vice President of Data Science
Shuling has spent 15+ years turning complex data problems into clear decisions, working across media, education, travel, digital health, and SaaS. At Northbeam, she built the statistical infrastructure behind our incrementality methodology.
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Ensuring good, reliable testing
A bad test doesn't just waste budget. It produces false confidence that compounds errors across every downstream decision. Learn how Northbeam's safeguards protect against this.
A data-first approach to designing tests that work
How Northbeam uses all your MTA data; conversion lag, baseline ROAS, channel mix, and more to size tests correctly and select geo groups that actually reflect your market.
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Why automation is the difference between a clean test and a compromised one
Most tests don't fail at design. They fail mid-flight. See how Northbeam monitors for drift and contamination daily and alerts you while there is still time to act.
A data-backed reference for what comes next
How iROAS results, halo effects, and calibration factors feed directly into MTA and MMM+ so every test makes your entire measurement stack smarter.
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