Northbeam is the heavier-modeling attribution platform for Shopify brands spending serious paid-media budget. This guide covers what its AI does and the floor below which it does not make sense.
What the AI does
Northbeam’s core is machine-learning multi-touch attribution combined with MMM+ (high-frequency media mix modeling) that retrains weekly. Where lighter tools assign credit with simpler rules, Northbeam runs ML across deterministic view-through data, a first-party pixel, and server-side tracking to model which touchpoints actually drove a conversion. Apex then pushes that modeled data back to the ad platforms so their algorithms optimize on better signal — closing the loop between measurement and spend.
What you get
The output is decision-grade: profit benchmarks, creative analytics, correlation analysis, and cross-channel granularity across Meta, Google, TikTok, Snap, Pinterest, and CTV (with an MNTN CTV partnership). Mid-market and enterprise growth teams get a dedicated media strategist, which is part of how Northbeam positions against self-serve dashboards.
The honest caveats
Northbeam is a serious tool with a serious floor. Pricing is custom-quoted; per Northbeam’s own pricing page the Starter MTA tier begins at $1,500/mo (volume-based, for brands under ~$1.5M/year in media spend), and the effective floor is brands spending $50K+/mo on paid media. Two more honest notes: there is a steep learning curve and technical setup, and Northbeam has a small G2 footprint, so independent validation is thinner than for higher-volume tools. See the Northbeam pricing breakdown.
Who this is for
Northbeam fits mid-market to enterprise DTC brands spending $1M+ annually on paid media that need attribution accuracy worth modeling for. It is covered in our best analytics roundup; Northbeam vs Triple Whale is the head-to-head against the dashboard-first alternative.