Northbeam

Northbeam is a marketing attribution and analytics platform that uses data science to show which channels and campaigns actually drive e-commerce revenue.

Overview

Northbeam helps e-commerce brands answer the question every marketer debates: what is actually working. Its attribution engine combines multi-touch modeling with media mix analysis and incrementality testing, producing a coherent view of how channels contribute to revenue rather than competing platform-reported numbers. Data pipelines unify ad spend, orders, and customer journeys, and its forecasting tools project revenue and guide budget allocation across channels. For brands scaling paid acquisition, the difference between platform claims and true contribution is often the difference between profitable growth and quietly burning margin; Northbeam’s modeling makes that difference visible and actionable weekly. Its incrementality experiments run alongside always-on modeling, so teams can validate the model’s conclusions with controlled tests instead of treating any attribution methodology as unquestionable truth. Implementation includes data-pipeline setup and analytical onboarding, so growth teams spend their time acting on allocation guidance rather than maintaining brittle spreadsheets that break whenever a platform changes its reporting definitions, especially during peak trading seasons and product launch periods alike.

Key Features

• Multi-touch attribution with media mix modeling
• Incrementality testing validating modeled conclusions
• Unified data pipelines for spend, orders, and journeys
• Revenue forecasting and budget-allocation guidance
• Weekly actionable reporting for growth teams
• Controlled experiments that keep attribution conclusions honest

Pricing

Northbeam contracts with brands through agreements scaled by revenue and data volume, including onboarding and analytical support. Its modeling approach is positioned for brands with meaningful paid media investment.

Who Is It For

E-commerce brands scaling paid acquisition who need true channel contribution rather than platform-claimed credit, and growth leaders allocating seven-figure media budgets against real evidence. It fits teams sophisticated enough to act on modeled guidance and to run the experiments that keep that guidance calibrated.