Northbeam

★★★★☆4.2 / 5 editorial Paid Pricing is quote-based via demo, aimed at brands with substantial ad spend — check the official site for current pricing.

ML-powered attribution and media mix modeling for e-commerce brands.

✓ Editor-verified listing — details reviewed by Grab AI Tool editors; pricing re-checked regularly.

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#attribution#analytics#ecommerce#paid social#reporting

What is Northbeam?

Northbeam is an attribution platform that blends machine-learning models with media mix modeling to show e-commerce brands which ads actually drive revenue. It tracks the full journey across Meta, Google, TikTok, and more, then attributes sales with first-party data built to survive the loss of third-party cookies and iOS tracking limits.

Dashboards break performance down by creative, audience, and journey stage, and the MMM layer helps plan budgets beyond last-click thinking. It is priced for brands spending meaningfully on ads, sold through demos rather than self-serve signup. Smaller stores will find simpler analytics enough; scaling DTC brands use Northbeam to decide where the next dollar goes.

Key Features

ML attribution across paid channels
Media mix modeling for budget planning
Creative and audience performance breakdowns
First-party tracking built for post-iOS privacy
Journey-level revenue reporting

Pros

Attribution built for the post-cookie era
MMM adds planning beyond last-click thinking
Strong for scaling DTC brands

Cons

Quote-based pricing via sales process
Needs meaningful ad spend to pay off
Setup and data onboarding take effort

Pricing

Model: Paid
Pricing is quote-based via demo, aimed at brands with substantial ad spend — check the official site for current pricing.

Adding Up Your AI Bill?

If you pay for Northbeam alongside other AI subscriptions, the monthly total adds up quietly. Tick every tool you pay for in our free AI Tool Cost Calculator to see your real monthly and yearly bill — and spot overlaps you can cut. Still choosing? Start with our guide on how to choose the right AI tool.