Three attribution tools.
Three different answers.
None of them tested.

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,
$750, 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 on 30 October 2026, from the vendors' own pricing pages and their Shopify App Store listings. Triple Whale and Polar no longer show paid prices on their own sites, so those figures come from the App Store. Attribution pricing changes often, so treat the date as part of the number.

Two warnings about that table. Northbeam's 5.0 star rating comes from three reviews written within three days in August 2023, so do not read it as "best rated." And Polar's pricing page hides figures that do not match its App Store listing: a $720 entry price and GMV bands up to $21,210 sit in its markup. If you scrape it you will get $720, not the $750 Polar lists on the App Store.

The structural point is the one buried in row four. At $0 and at $219 a month you are buying pixel-based attribution and nothing else: first and last click on the free tier, multi-touch from Foundation. 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 as paid add-ons or inside Enterprise. 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 40-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, listed on Polar's pricing page as Incrementality Testing, 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. Even perfect measurement would tell you where the sale landed, never which belief the buyer was still missing. That is a different question the same journey can answer.

Two cross-checks
that cost almost
nothing to run.

If no tool can prove its own accuracy, you need numbers that don't depend on any one tool's model. Two kinds are close to free: blended ratios that no platform can take credit inside, and asking the buyer. Shopify's own reports already give you a third opinion, with five attribution models and last click as the default once sales metrics meet a marketing dimension.

MER, the number the tools can't argue with

Marketing efficiency ratio is total revenue divided by total marketing spend, which is how Shopify defines it. No channel gets credit inside it, so nothing is counted twice. That makes it the check for the most common attribution failure: Meta, Google and your attribution tool between them claiming more revenue than the store actually booked. What MER can't do is tell you which channel to cut. How to set a MER target from your margins and overheads is in break-even ROAS and MER.

One trap when you compare tools: Triple Whale's documentation defines MER the other way up, as ad spend divided by revenue. It reads as a cost percentage, not a multiple, so a 20% MER in Triple Whale and a MER of 5.0 elsewhere describe the same store.

Spend and revenue per visitor

Alongside MER, I like to track two more ratios: total marketing spend divided by site visitors, and total revenue divided by the same visitors. With a large enough sample, the pair is a good proxy for whether everything you're paying for is working together. That includes top-of-funnel campaigns, out-of-home and other efforts that don't always lead to an attributed click or conversion. They bring visitors all the same, and those visitors' orders land in revenue.

If spend per visitor rises and revenue per visitor rises with it, the extra money is bringing in people who buy. When spend per visitor climbs and revenue per visitor stays flat, you're paying for traffic that doesn't convert, whatever the platforms report. At low volume, though, a promotion or a stock-out can swing both numbers more than any channel change, so compare weeks or months rather than days, and read the pair next to MER.

Post-purchase surveys: ask the buyer

A post-purchase survey asks "how did you hear about us?" on the thank-you page. It is the only input in this comparison that comes from the customer instead of a click log, so it picks up channels click-based tools can't see: podcasts, creators, word of mouth. Triple Whale feeds its own survey into its Total Impact model, and Fairing and KnoCommerce sell surveys on their own.

Response rates are high by the vendors' own accounts. KnoCommerce reports a 45% average response rate, and Fairing says at least 60% of customers respond for its average client. Neither publishes a method, so read both as vendor claims.

Surveys carry their own bias, because they record what the buyer remembers. And nobody has published an independent study comparing survey answers with holdout results, which is the same gap section 03 found for the tools. Use the survey to find channels your click data misses, then test those channels before you move budget to them.

What each one earns
its cost on, and what
it usually replaces.

This is how I'd actually advise on it, 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 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 costs extra below Enterprise. 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-based 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.