Triple Whale, Northbeam and Polar Analytics ship three different default attribution models, so they disagree about the same store before any of them applies modelling. No independent test has ever validated any of them against a controlled experiment, and the academic literature suggests they share a common accuracy ceiling.
- Entry pricing observed 31 July 2026: Triple Whale free, Polar Analytics $720 a month, Northbeam from $1,500 a month.
- Default models differ: Triple Whale uses a 28-day window, Northbeam uses proprietary clicks-only, Polar uses first click for pixel metrics.
- At the cheap tiers you are buying pixel attribution only. Modelling and incrementality sit behind a sales call at all three vendors.
- Triple Whale's own documentation says its headline model should not be used for financial reporting because attributed revenue will exceed actual revenue.
- A study of 663 randomised controlled trials found observational methods overestimated true lift by 696 to 948 percent at the median, using richer data than any of these tools can access.
- The cheapest reliable answer is a geo holdout you run yourself, which costs a subscription-free amount of forgone revenue and settles the argument.
Point Triple Whale, Northbeam and Polar Analytics at the same store and they will disagree about your Meta ROAS before any of them has done anything clever. Not because one is broken. Because they ship different default attribution models, and almost nobody changes the default.
Triple Whale defaults to a 28-day window. Northbeam defaults to its proprietary clicks-only model. Polar defaults to first click for its pixel metrics. Three different answers to "who gets credit," decided for you at signup. Most of the "tool A says 3.2, tool B says 2.1" arguments I get pulled into are measuring configuration drift, not accuracy.
This is the comparison I wish existed when I was buying these for a nine-figure brand. Pricing, what each one actually does, and an honest answer to the question underneath the whole category, which is whether any of them is telling you the truth.
The floor is $0,
$720, or $1,500
a month.
These three are not competing for the same buyer, and the pricing makes that obvious faster than any feature matrix. Everything in this table was read off the vendors' own pricing pages on 31 July 2026. Attribution pricing changes often, so treat the date as part of the number.
| Triple Whale | Northbeam | Polar Analytics | |
|---|---|---|---|
Entry price | $0, free tier | From $1,500/mo | $720/mo |
Published tiers | Free, Foundation $219/mo, Automate $749/mo, Enterprise on request | Starter from $1,500/mo. Professional and Enterprise are demo-form only | 18 published GMV bands, $720 up to $21,210/mo |
What the bill scales on | Flat per tier | Pageviews and data refresh cadence. Ad spend sets the tier, data volume sets the bill | Annual GMV band. Not orders, not seats |
Where the rigour lives | MMM and incrementality are Enterprise only | MMM+ is an Enterprise add-on. Incrementality launched Apr 2026, Meta only, US only | No MMM product. Causal Lift is a geo holdout run as a service, $3,200 to $6,000 per test |
Hidden pricing | Enterprise only | Two of three tiers | Add-ons, CAPI signals, custom plans |
Shopify App Store | 4.1 stars, 85 reviews | 5.0 stars, 2 reviews, both from Aug 2023 | 4.8 stars, 109 reviews |
Two warnings about that table. Northbeam's 5.0 star rating comes from two reviews written on the same day in August 2023, so do not read it as "best rated." And Triple Whale's pricing page still carries stale legacy blocks in its markup, including a slider that renders a $1,290 figure for a plan that no longer exists. If you scrape it you will get the wrong numbers.
The structural point is the one buried in row four. At $0 and at $219 a month you are buying pixel-based multi-touch attribution and nothing else. The triangulation layer that makes attribution defensible, the modelling and the experiments, sits behind a sales call at all three vendors. That is worth understanding before you interpret the free tier as a cheap version of the paid one. It is a different product.
They are not three
versions of the same
product.
Northbeam is the purest attribution engine of the three. Its own pixel on a first-party domain, order sync, appended UTMs, platform spend APIs, all stitched through in-house device and identity graphs on an indefinite window. It is also the most explicit about what that does to your numbers. Northbeam publishes a help document titled, more or less, "my Facebook ROAS is 0.7 in Northbeam, is this normal." The tool is designed to make your reported numbers go down, which is an organisational problem before it is a data problem. Their position on platform-reported figures is blunt: add up all your in-platform revenue and it will far exceed your actuals.
One genuinely useful thing they publish is a QA benchmark. Your visit-to-click rate should sit between 30 and 50 percent. Below 30 percent means broken UTMs or pixel gaps. That is a testable implementation check, and it is a good stick to beat any attribution vendor with, including the other two.
Triple Whale has the widest surface area and the most candid disclaimer. Seven models across five windows, plus a native post-purchase survey feeding its Total Impact model, plus AI agents, plus MMM and incrementality at the top tier. Its headline model, Triple Attribution, deliberately mimics ad-platform logic by giving last click full credit within each platform. Their own knowledge base says not to use it for financial reporting or total revenue analysis, because total attributed revenue across channels will exceed actual revenue. That is an unusually honest thing to put in writing, and it is also a model the marketing site presents prominently.
Worth knowing that Triple Whale has repositioned. It acquired an AI-visibility platform in January 2026 and launched a revamped agent product in May 2026, explicitly framing itself as moving beyond measurement into AI execution. If you are evaluating it as an attribution company you are evaluating what it was eighteen months ago.
Polar is a data warehouse that grew an attribution layer. Every plan ships a dedicated Snowflake database the customer holds keys to, a large semantic layer and 45-plus connectors. It has its own pixel and ten models including a Shapley value allocation, and it warns that its overlap models intentionally inflate totals and are not meant for strict ROAS calculations. Polar competes with the decision to build your own data stack rather than with the other two tools on this page.
Its most interesting product is also its least bundled. Causal Lift is a geo synthetic-control holdout run by a Polar data scientist and billed per test. That is arguably the most methodologically honest measurement any of the three sells, because it is an actual experiment rather than a model. The fact that it is priced per test tells you it is labour, not software.
There is no independent
test showing any of
them is accurate.
I went looking for one properly. No neutral party has published a rigorous head-to-head running two or more of these simultaneously on the same store and validating reported ROAS against a holdout or geo-lift experiment with the methodology disclosed. The entire comparison corpus is vendor marketing, affiliate content, or agency content with undisclosed conflicts.
The specific numbers that circulate are worse than useless. The most-cited comparison quotes precise incrementality percentages for each tool with no test names, dates, brands, sample sizes or confidence intervals, and it is published by a company selling a competing attribution product. Another widely shared piece cites similar figures while listing one of the vendors as a technology partner on its own site, without disclosing it in the post. If you have seen a number like "Triple Whale shows lift at 70 to 85 percent of true," that is where it came from.
The strongest evidence available is academic, and it indicts the whole category rather than ranking anyone inside it. Gordon, Moakler and Zettelmeyer, working with 663 randomised controlled trials, 1,673 experiment-outcome pairs and roughly 7.9 billion user-experiment observations, found that for the median RCT, standard observational methods overestimated the true lift by factors of 696, 948 and 764 percent for upper, mid and lower funnel outcomes respectively.
Their conclusion is the part worth sitting with: despite access to large-scale experiments and rich user-level data, they were unable to reliably estimate an ad campaign's causal effect. And then the line that matters most here, which is that the granularity and detail of the data they used exceeds what individual advertisers or their third-party measurement partners typically could access.
Triple Whale, Northbeam and Polar are those third-party measurement partners. They are working with strictly less data than the study that failed. Meta shipping its own separate incremental attribution model in 2025 is a fairly loud admission from the largest ad platform that standard attribution credits conversions that would have happened anyway.
So the defensible position is this. No independent test ranks these tools. The literature suggests they share an accuracy ceiling imposed by observational click data. Any specific accuracy percentage attached to a named vendor is marketing. The only way to know your own numbers is to run your own geo holdout, which is exactly why the one product in this comparison that runs real experiments is priced per test rather than bundled into a subscription.
What each one earns
its cost on, and what
it usually replaces.
Here is how I would actually advise on this, having bought and later cancelled versions of all three categories.
Northbeam earns its $1,500 a month floor when you are spending enough that a ten percent misallocation costs more than the subscription. That is roughly the point where you have real multi-channel spend and a person whose job is to act on the output. It replaces arguing with platform-reported ROAS. It does not replace an experiment. Buy it if you will change budget based on what it says, and skip it if the honest answer is that Meta's number is what your team will use anyway.
Triple Whale earns its keep as an operating dashboard long before it earns it as a measurement system. The free and $219 tiers are genuinely useful for seeing the business in one place every morning, and that is a real job. Just do not confuse it with attribution rigour, because the rigour is at the Enterprise tier. It usually replaces a manual morning spreadsheet, which is worth more than most people admit.
Polar earns it when the alternative is hiring a data engineer. Its own lead case study is replacing an in-house data stack, and that is the honest comparison. If you need a warehouse, a semantic layer and BI across more than just ads, the GMV-band pricing will beat building. If you only want to know what your Meta ROAS really is, you are buying a great deal of machinery for one question.
All three stop paying when nobody changes a decision because of them. That is the actual test, and it is not a technology question. If your last three budget shifts were made on gut, platform numbers, or a founder's opinion, the tool is a very expensive reporting habit. Before renewing any of them, look at what your contribution margin says the misallocation is actually costing, and check the ceiling with your max allowable CAC. Those two numbers decide whether measurement precision is worth paying for at your size.
The cheapest useful thing in this entire category is a geo holdout you run yourself. Turn a channel off in a set of matched regions, leave it off long enough to matter, and compare. It costs you some revenue and no subscription, and it answers the question the models are estimating. The framework in high-ROI ecommerce tests covers how to structure that so the result is readable.
If you want the wider picture of where measurement sits relative to everything else you are paying for, the stack by revenue stage puts attribution in context against the rest of the tooling budget, and the channel benchmarks give you something to compare the output against once you have it. For the specific question of whether Meta or Google is actually carrying your growth, that comparison is its own piece of work.
My summary after years of this: buy the tool that changes a decision, run the experiment that settles the argument, and treat every attributed number, including the expensive ones, as an estimate with a wide error bar rather than a fact.
Q: Which attribution tool is the most accurate?
Nobody has published a credible answer, and be sceptical of any source that gives you one. No neutral party has run two or more of these simultaneously on the same store and validated the reported numbers against a holdout or geo-lift experiment with methodology disclosed. Every specific accuracy percentage in circulation traces back either to a company selling a competing product or to an agency with an undisclosed partnership with one of the vendors. The academic work on observational attribution suggests all of these tools share an accuracy ceiling set by the limits of click data, so the more useful question is which one changes a decision you would otherwise get wrong.
Q: Why do Triple Whale and Northbeam show different ROAS for the same campaign?
Usually because they are answering different questions by default and nobody changed the setting. Triple Whale defaults to a 28-day window and its Triple Attribution model assigns last-click credit within each platform. Northbeam defaults to a proprietary clicks-only model with fractionalised credit and an indefinite window. Those two choices alone will produce materially different channel credit on identical data. Before concluding that one tool is wrong, align the models and windows, then check the implementation: Northbeam publishes a visit-to-click benchmark of 30 to 50 percent, and anything below 30 percent points at broken UTMs or pixel gaps rather than a modelling disagreement.
Q: Is the Triple Whale free plan good enough?
It is good enough to be a morning dashboard and not good enough to be a measurement system, which is a real distinction rather than a dig. The free and $219 a month tiers give you pixel-based multi-touch attribution and a consolidated view of the business, which genuinely replaces a manual spreadsheet. What they do not give you is the modelling and incrementality layer that makes attribution defensible, because that sits at the Enterprise tier behind a sales conversation. Use it to see the business daily. Do not use it to settle an argument about whether a channel is incremental.
Q: Do I need an attribution tool at all?
Only if you will change a budget decision because of it. The test I use with operators is to look at the last three meaningful spend shifts and ask what drove them. If the answer is gut, platform-reported numbers, or the founder's opinion, then adding a subscription changes your reporting and not your decisions. The threshold where these tools start paying is roughly the point where a ten percent misallocation of spend costs more per month than the software, and where somebody owns acting on the output. Below that, a geo holdout run once a quarter will teach you more for less.
Q: What is a geo holdout and why is it better than attribution software?
You switch a channel off in a set of matched regions, leave it off long enough for the effect to show, and compare those regions against the ones where it kept running. It is a real experiment, so it measures incrementality directly rather than estimating it from click paths. It is better in the specific sense that it answers the causal question the models are approximating, and worse in the sense that it costs real forgone revenue, takes weeks, and only answers one question at a time. Notably, the one product in this comparison that runs genuine geo experiments is billed per test rather than bundled, which tells you it is skilled labour rather than software.
Deciding whether the subscription is worth it.
I have bought, run and cancelled these tools as an operator, and I now help brands work out whether measurement precision is the constraint or whether something further upstream is. If you are staring at three quotes and cannot tell which problem you are solving, that is a short conversation.
Start a conversation Run your max allowable CAC →