Northbeam pricing: what the vendor publishes
Northbeam's pricing page lists Starter and Professional prices and quotes Growth and Enterprise through sales, as read on 2026-09-08. Compare it with a one-off read by cost per budget decision, then test its numbers against Shopify orders.
€99 once, excl. VAT. Full refund within 30 days, no questions asked. You keep the read.
By Joris van Huët, Founder & CEOUpdated 4 min read
Northbeam's pricing page lists Starter at $1,500/mo and Professional at $3,500/mo, with Growth and Enterprise quoted; Starter is month-to-month, and Professional and Enterprise are annual terms (as read on 2026-09-08). Those are list prices for a continuous platform, so compare it with a one-off read by cost per budget decision, not cost per month. For illustration: 12 months of Starter is 12 x $1,500 = $18,000, before any higher tier or quote.
What does Northbeam publish, and what does it quote?
The comparison table lists Northbeam's method as machine-learning multi-touch attribution, and its comparison page adds media mix modeling and incrementality. The price lines come from Northbeam's pricing page, as read on 2026-09-08.
The same page has a FAQ asking why plans start at the listed rates and what your actual price will be, so treat each list price as a starting point and ask for the actual price in writing. Its FAQ also says Northbeam's figures "may not line up with the numbers you see in Meta Ads Manager". Decide before you buy which number you will trust and how you will test it.
How do you compare a subscription with a one-off read?
They answer different questions: a platform that runs continuously, and a read of one export. Put both on one scale: annual cost divided by the number of budget decisions it changed. For illustration: $18,000 a year that changes six decisions is $3,000 a decision, and a €99 read that changes one is €99. Currencies are not converted and VAT is ignored. Neither figure says which decision was right, and the decision count is yours to estimate, because neither tool supplies it.
If you would pay for Northbeam's media mix modeling or incrementality products, ask how experiments calibrate them. Meta's Robyn documentation cites a third-party whitepaper finding that uncalibrated models show a 25% average difference to the ground truth, and Google's Meridian documentation says incrementality experiments are "perhaps the strongest basis for formulating your intuition" about a channel's performance. Both are documentation published by the tool makers, not audits. The questions to ask any MMM provider cover the rest.
How do you check a platform's numbers before you commit?
Two checks, both free, both before the contract:
- Reconcile to Shopify. Take the last full week and add up the orders the tool credits across your channels. Pass: the total matches Shopify's order count, less a gap you can explain, such as orders left unattributed. Fail: the total is above the order count, or below it by more than you can explain, so ask the vendor why before you buy.
- Test the biggest disagreement. Find the channel where the tool and Meta Ads Manager differ most. Cut that channel in some regions for long enough to count orders, and keep it on in matched regions (the holdout calculator gives the number of days). Pass: the drop in total orders in the cut regions is close to what one of the two numbers credited there. Fail: it is far from both, so neither number is ready to move budget.
Why run a test? Gordon and colleagues (Marketing Science, 2019, peer-reviewed; two of the four authors worked at Facebook) used 15 U.S. advertising experiments at Facebook to check observational models against randomized results. Their abstract says the observational methods "often fail to produce the same effects as the randomized experiments, even after conditioning on extensive demographic and behavioral variables". Those were user-level methods on Facebook data, not Northbeam, and no source cited here tests Northbeam against a holdout.
Where does a one-off causal read fit?
A causal attribution read like Causality Engine's is priced per read: €99 once, excluding VAT, refundable within 30 days, so four reads a year is €396; Pro is €299 a month. It takes one GA4 export and needs no pixel, and it shows what each channel caused next to what last-click gave it. It works on GA4 channel groups rather than campaigns, and it takes no spend data.
Sources, 30 September 2026: Northbeam pricing (Northbeam, as read 2026-09-08); Key Features (Robyn documentation, Meta Marketing Science, updated December 2024); Calibrate treatment priors (Meridian documentation, Google for Developers, updated 2026-09-24); A Comparison of Approaches to Advertising Measurement (Gordon et al., Marketing Science, 2019).
Related answers
Frequently asked questions
How much does Northbeam cost?
Northbeam lists Starter at $1,500/mo and Professional at $3,500/mo, with Growth and Enterprise quoted through sales (northbeam.io/pricing, as read on 2026-09-08). Check the page for current figures and ask for your actual price in writing.Is Northbeam month-to-month or annual?
Per its pricing page, as read on 2026-09-08, Starter is month-to-month and Professional and Enterprise are annual terms. Ask for the renewal terms in writing before you sign.Why don't Northbeam's numbers match Meta Ads Manager?
Northbeam's own FAQ says its figures may not line up with the numbers in Meta Ads Manager. Two systems that define credit differently will disagree, so reconcile both to Shopify orders and test the biggest gap with a holdout.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
Keep reading
Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Causal AttributionCausal Attribution uses causal inference to determine which marketing touchpoints genuinely cause conversions, not just correlate with them.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- ExperimentsExperiments are scientific procedures that test hypotheses or demonstrate facts. In marketing, experiments like A/B tests determine the causal effect of campaign changes, enabling data-driven decisions.
- IncrementalityIncrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
- Incrementality TestingIncrementality Testing measures the additional impact of a marketing campaign. It compares exposed and control groups to determine causal effect.
- Media Mix ModelingMedia Mix Modeling is a statistical technique that measures the collective impact of marketing and advertising on sales. It uses historical data to inform budget allocation.
- Multi-Touch AttributionMulti-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a comprehensive view of channel impact on conversions.