Northbeam Review
Multi-touch attribution and media mix modeling platform for DTC and ecommerce brands managing spend across paid social, search, and streaming channels.
Northbeam is a serious marketing attribution platform built for performance-driven brands spending meaningful budgets across Meta, Google, TikTok, and streaming channels. Its media mix modeling gives brands a defensible view of channel performance that does not collapse when cookies disappear or iOS attribution breaks. The tool is not self-serve and requires onboarding investment, but brands that commit to it consistently report better spend allocation decisions than they were making with platform-native attribution. Enterprise pricing and a demo-required evaluation process make it a considered purchase rather than a quick trial.
Pros and cons
- Northbeam Pixel and first-party data infrastructure make attribution defensible in a cookieless, post-iOS-14 environment where platform-reported ROAS is increasingly unreliable
- Multi-touch attribution models (first-touch, last-touch, linear, time-decay, data-driven) let brands compare attribution philosophies rather than being locked into one view
- Media mix modeling (MMM) layer provides incrementality measurement and budget scenario planning without requiring randomized holdout experiments
- Near-real-time data refresh (often same-day) is faster than traditional MMM providers that operate on weekly or monthly cycles
- Dedicated onboarding and customer success teams are included, reflecting that this is a high-touch partnership rather than a self-serve tool
- Enterprise pricing with no self-serve option requires a sales conversation and typically a meaningful monthly spend minimum before the ROI makes sense
- The onboarding process requires pixel implementation and data connector setup that takes real engineering time before you see any attribution data
- MMM outputs are probabilistic estimates, not definitive answers, and require data literacy to interpret correctly and act on confidently
- Smaller brands under a certain monthly ad spend threshold will not see enough data volume to make the attribution models statistically reliable
- Platform attribution discrepancies are a feature (they are usually evidence of inflated platform numbers) but can create internal friction when media buyers disagree with Northbeam numbers
What is Northbeam?
Northbeam is a marketing attribution and media mix modeling platform designed for direct-to-consumer and ecommerce brands that run significant paid advertising budgets across multiple channels. Its core problem statement is that platform-native attribution (Meta Ads Manager, Google Ads, TikTok Ads) systematically overstates performance because each platform takes credit for the same conversions. Northbeam provides a unified, first-party view of attribution that removes this double-counting and gives brands a single source of truth for channel performance.
The platform combines multi-touch attribution (MTA) with media mix modeling (MMM) in a way that most standalone MTA tools do not. MTA tells you which touchpoints in the customer journey preceded a conversion and assigns credit accordingly. MMM uses statistical modeling across your full business data to estimate the causal contribution of each channel to revenue, including channels that are hard to track at the user level like TV, podcast, and streaming ads. The combination gives brands coverage across the full funnel and both the tactical and strategic views of spend efficiency.
Northbeam differentiates itself from legacy MMM providers through speed: most traditional MMM providers operate on weekly or monthly model refresh cycles, while Northbeam updates attribution data on a near-real-time or daily basis. This makes the output actionable for media buyers making daily or weekly budget allocation decisions rather than just useful for quarterly planning reviews.
Core features
Multi-Touch Attribution (MTA)
Northbeam tracks the full customer journey across paid channels using a first-party pixel plus server-side data to maintain attribution accuracy in iOS 14+ and cookieless environments. You can switch between attribution models (first-touch, last-touch, linear, time-decay, data-driven) to understand how credit assignment affects your channel performance view. The cross-channel view eliminates the double-counting that makes platform-native ROAS comparisons misleading.
Media Mix Modeling (MMM)
Statistical modeling that estimates the causal contribution of each marketing channel to revenue using your historical spend and sales data. Unlike MTA, MMM works even for channels without user-level tracking: streaming ads, podcast sponsorships, billboards, and other upper-funnel spend can be included. Northbeam's MMM refreshes faster than most legacy providers, making it useful for campaign-level decisions rather than only annual budget planning.
Budget Scenario Planning
Northbeam's scenario planner lets you model the projected revenue impact of increasing or decreasing spend on each channel, based on the MMM's estimated response curves. This turns attribution data into a budget allocation recommendation rather than just a historical report. Media teams can run "what if we shift 20% of Meta budget to TikTok" scenarios and see the modeled outcome before making the change.
Creative Analytics
Ad creative performance analytics that breaks down conversion rates, revenue per click, and attribution by individual creative asset rather than just by campaign or ad set. This is particularly valuable for brands running large creative testing programs across Meta and TikTok, where the signal in platform dashboards is noisy due to attribution inflation. Northbeam's view is grounded in the same first-party attribution data as the channel-level reporting.
Data Connectors and Custom Reporting
Native integrations with Meta, Google, TikTok, Snapchat, Pinterest, and most major ad platforms bring spend data alongside conversion data in a single view. Shopify, WooCommerce, and other ecommerce platforms connect for revenue data. A BI connector lets teams push Northbeam data into Power BI, Tableau, or Looker for custom reporting within existing analytics infrastructure.
Pricing
| Feature | Growth Contact sales | Scale Contact sales | Enterprise Contact sales |
|---|---|---|---|
| Multi-touch attribution | ✓ | ✓ | ✓ |
| Media mix modeling | ✗ | ✓ | ✓ |
| Budget scenario planning | ✗ | ✓ | ✓ |
| Creative analytics | ✓ | ✓ | ✓ |
| Custom attribution models | Standard | Custom | Custom |
| Data refresh cadence | Daily | Near real-time | Near real-time |
| BI connector | ✗ | ✓ | ✓ |
| Dedicated CSM | ✗ | ✓ | ✓ |
| Custom integrations | ✗ | ✗ | ✓ |
Who it is for
Direct-to-consumer brands spending $50k or more per month across Meta, Google, and TikTok who have lost confidence in platform-reported ROAS after iOS 14. Northbeam gives these teams a unified, first-party attribution view that survives tracking changes and removes cross-platform double-counting.
In-house media buying teams that need daily data to make budget reallocation decisions across multiple channels. The near-real-time data refresh and scenario planning tools make Northbeam actionable for weekly or even daily budget adjustments, rather than only useful for quarterly strategy reviews.
Brands adding streaming, podcast, or TV advertising who need to measure the impact of channels that do not have user-level click tracking. The MMM layer provides estimates of upper-funnel contribution that standard attribution tools cannot capture, making it possible to justify brand-building spend alongside direct response.
Verdict
Northbeam earns its place as a premium attribution tool for brands serious about making data-driven media allocation decisions. The combination of real-time MTA and fast-refresh MMM is genuinely differentiated from both legacy MMM providers and simpler last-click attribution tools. The cost and complexity are real barriers, but for brands spending enough to make the investment worthwhile, the clarity on true channel contribution pays for itself.
Frequently asked questions
How does Northbeam handle iOS 14 and cookie deprecation?
Northbeam uses a first-party server-side pixel alongside browser-based tracking, which reduces dependence on third-party cookies and browser-level identifiers. Server-side tracking captures conversion signals that iOS 14 privacy changes would otherwise block at the browser level. The MMM layer provides additional coverage for scenarios where user-level tracking is unavailable, using aggregate spend and revenue patterns to estimate channel contribution.
What is the difference between Northbeam's MTA and its MMM?
Multi-touch attribution (MTA) tracks individual user journeys and assigns credit to the touchpoints a specific user encountered before converting. Media mix modeling (MMM) uses statistical regression across historical spend and revenue data to estimate the aggregate effect of each channel on total revenue. MTA is granular and fast but breaks when tracking is unavailable. MMM is slower to update but works even for channels with no user-level data. Northbeam runs both and lets you compare the outputs.
How long does Northbeam take to set up?
Onboarding typically takes two to four weeks for full data connectivity and model calibration. You need to implement the Northbeam pixel on your website, connect your ad platforms, and connect your ecommerce or CRM data. Historical data import is part of onboarding and is necessary for the MMM models to have enough data to produce reliable estimates. Northbeam provides a dedicated onboarding team to manage this process.
Does Northbeam work for B2B companies or only ecommerce?
Northbeam is primarily designed for ecommerce and DTC brands with short purchase cycles and high-volume ad spend across Meta and Google. B2B companies with long sales cycles and small deal volumes will not generate the conversion data volume needed for reliable attribution modeling. Some B2B brands with self-serve components or high lead volumes use it, but it is not the primary target market.
How is Northbeam different from Triple Whale?
Both tools target DTC ecommerce brands and address post-iOS-14 attribution. Triple Whale has a broader SMB focus with self-serve pricing and a wider feature set including influencer tracking and cohort analysis. Northbeam is more focused on attribution accuracy and MMM for brands at higher spend levels, with a more hands-on onboarding and support model. Triple Whale is easier to get started with; Northbeam goes deeper on statistical rigor at scale.