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SEOSEOPaid AcquisitionPaid acquisitionProgrammaticWebsite MonetizationProgrammaticApp UAApp MonetizationWebsite monetizationKeyword ResearchSearch IntentApp acquisitionROASCPAApp monetizationCPCLTVAffiliateeCPMRPMRetail MediaAttributionConversion TrackingCreative IntelMMPHeader BiddingDSPSSPRTBAd ViewabilityFill RateASOSKAdNetworkARPDAURewarded VideoAd MediationAffiliateCreative TestingA/B TestingRetargetingLookalike AudiencesCampaign OptimizationBrand SafetySupply Path

Attribution & Analytics

Northbeam

Paid

Northbeam is an ecommerce attribution and media-mix modeling platform built for advertisers spending across paid social, search, and other channels. It blends a first-party tracking pixel, multi-touch attribution, and machine learning to estimate each channel's incremental contribution rather than relying on platform-reported ROAS. Marketers see full customer journeys, new-versus-returning revenue, and creative-level performance, then push cleaner conversion data back to ad platforms to reallocate budget toward genuinely incremental spend.

Analyticsmedia mix modelingmulti-touch attributionincrementalityecommerce analytics

What it does

Northbeam is a marketing attribution and media-mix platform aimed at ecommerce and DTC advertisers that spend heavily across paid social, search, and other channels. It combines a first-party tracking pixel, multi-touch attribution, and machine-learning media-mix modeling to estimate the true incremental contribution of each channel and campaign. Marketers see customer journeys, time-to-conversion, and new-versus-returning revenue, then use those signals to reallocate budget toward genuinely incremental spend. Northbeam also surfaces creative-level performance and forecasting, and pushes cleaner conversion data back to ad platforms. It targets data-driven teams that have outgrown platform-reported ROAS and want a more defensible view of marketing efficiency.

Where it fits

Sits at the attribution and media-mix-modeling layer for data-driven ecommerce advertisers.

Core features

  • First-party pixel feeding multi-touch attribution
  • Machine-learning media-mix modeling for incrementality
  • Customer-journey and time-to-conversion reporting
  • New-versus-returning revenue and CAC breakdowns
  • Creative-level performance and budget forecasting
  • Conversion data sent back to ad platforms for optimization

Best for

  • Scaling DTC brands reconciling multi-channel ad spend
  • Performance teams seeking incrementality beyond platform ROAS

Beginner notes

  1. Give the model several weeks of data before trusting incrementality reads
  2. Compare Northbeam's view against platform-reported numbers to understand the gaps
  3. Act on channel-level reallocation gradually rather than overhauling budgets overnight
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