Northbeam vs Triple Whale comes down to focus: both are first-party pixel attribution tools built mainly for Shopify brands, but Northbeam is a dedicated attribution product with published prices and MMM and incrementality add-ons, while Triple Whale is a broader data platform with a free plan, AI agents and surveys, with its MMM and geo tests inside Compass on the Enterprise plan. Neither is a substitute for a full marketing mix model unless you understand what its “MMM” is trained on. The same is true of Rockerbox, Prescient AI, Fospha, Polar Analytics and Hyros, which all use the words “MMM” and “incrementality” to mean slightly different things.
I co-founded a growth agency that spent over $10 million a year on Facebook ads, so pixel tracking and its gaps were a daily concern long before Apple’s iOS 14 changes made them everyone’s problem and helped create this whole category of tools. They exist because ad platforms over-claim credit, and a neutral pixel that de-duplicates conversions across platforms is a genuine improvement on reading five dashboards that each claim the same sale. However, a better pixel is still a pixel. I’ll go through what each tool actually ships, checked against its own site and documentation on 24 October 2026, and then show where these products overlap with a true marketing mix model and where they don’t.
What a pixel can and can’t see
An attribution tool watches individual journeys: a click on a Meta ad, a visit from Google, a purchase on your Shopify store. It then divides credit for the purchase between the touchpoints it saw, using rules or a model. That’s useful for running ads day to day, because it works at campaign and creative level and it’s available tomorrow morning. The MMM vs multi-touch attribution page explains the difference in detail, with a journey you can credit five different ways.
The limits come from what the pixel can’t observe. It can’t see the person who saw your TikTok ad and bought on Amazon, the podcast listener who walked into a store, or the customer who would have bought anyway because they already loved the brand. That last group is the expensive one, because attribution gives them to whichever ad touched them last, which makes retargeting and branded search look much better than they are. Measuring that requires a counterfactual (what would have happened without the ad), which only an experiment or a model of aggregate data can give you. That’s why every one of these companies has added an MMM or incrementality tests, and why it’s worth checking how.
Northbeam vs Triple Whale in detail
Northbeam’s core is first-party multi-touch attribution with what it calls “Clicks + Deterministic Views”, which gives credit for ad impressions it can tie to a buyer, as well as clicks. On top of that it sells MMM+, a daily media mix model that can take Northbeam’s own attribution data as an input, and since 7 April 2026 Northbeam Incrementality, which automates geo lift tests (US Meta campaigns first, with more channels promised) and feeds the results back into both models. It publishes prices: Starter at $1,500 for brands spending under $1.5 million a year, month-to-month, and Professional at $3,500 a month for up to $500,000 a month in spend, with Growth and Enterprise quoted. MMM+ and incrementality are add-ons on the higher tiers.
Triple Whale is wider and more Shopify-native: a pixel and attribution, post-purchase surveys, a Sonar feature that sends conversions back to the ad platforms, BI dashboards, and the Moby AI agents that answer questions and automate reports. Its MMM sits inside Compass (also branded “unified measurement”), where its own setup guide has you choose daily, weekly or monthly granularity and lets you include geo lift experiments and pixel attribution as priors. The geo tests use synthetic control on census-based clusters it calls Geo Pods, need at least 21 days, and run on the Enterprise plan, which also includes Compass. Triple Whale starts with a free plan; paid plans are priced by annual GMV with a slider on its pricing page and run on 12-month contracts.
In practice, Northbeam suits a brand that already knows how to run paid social and wants the cleanest attribution signal plus an upgrade path to models and tests, with month-to-month terms at the bottom. Triple Whale suits a brand that wants one place for its Shopify data, reporting and attribution, starting free, with measurement depth on the top plan. If the reason you’re buying is the MMM or the tests, compare the Enterprise-level offers, because that’s where both companies keep them.
What each attribution tool means by “MMM” and “incrementality”
The rest of the category is easiest to understand by asking two questions of each tool. What is the model trained on? And does the tool run experiments itself, or only accept results you bring?
Rockerbox
Rockerbox, now owned by DoubleVerify (announced February 2025 at $85 million, closed March 2025), has the most complete suite of the attribution companies: de-duplicated user-level attribution, an MMM for long-run channel contribution and forecasts, and incrementality tests whose results calibrate the attribution and act as priors in the MMM. It handles offline channels like linear TV, direct mail and podcasts, exports to BigQuery, Snowflake and Redshift, and tracks promo codes and post-purchase surveys. It doesn’t publish prices and sells to mid-market and enterprise omnichannel brands.
Prescient AI
Prescient AI isn’t really an attribution tool at all: there’s no pixel, and the whole product is a marketing mix model that retrains daily at campaign level and estimates halo effects onto Amazon and retail sales. It doesn’t run experiments; instead a validation layer compares its results with tests and attribution data you bring. It says it’s built for brands spending $100,000 or more a month on ads, and its pricing page names SMB, Growth and Enterprise tiers on annual contracts without figures. Of the tools on this page, it’s the closest to a classic MMM, with the trade-off that you’re trusting a proprietary daily model whose method is only partly documented in public.
Fospha
Fospha, a Blenheim Chalcot venture, calls its core product “Media Mix Attribution”: an always-on daily model at ad level that blends click and impression data with modeled and causal components, including Bayesian saturation curves. It’s essentially an MMM-style model sold as attribution, with a separate MMM suite for planning, geo lift tests run by its own science team, and halo measurement across DTC, Amazon and TikTok Shop. It says it’s built for brands spending over $150,000 a month on advertising and quotes prices by demo.
Polar Analytics
Polar is a Shopify-first data platform (a Snowflake database per brand, over 1,000 connectors, a pixel and multi-touch attribution) whose incrementality product is Causal Lift: geo holdout tests with synthetic control and behaviorally clustered regions, measuring sales across Shopify, Amazon and stores. As of October 2026 it doesn’t have a shipped MMM; its site says to talk to the team about current scope. Unusually, it shows per-test prices for an example $50 million brand: $5,000 per test bundled with its data platform, or $20,000 standalone.
Hyros
Hyros is a tracking and attribution tool aimed at info products, coaching, call-based sales and ecommerce, with call tracking for booked calls and closed deals and a rule-weighted multi-touch model. It doesn’t offer an MMM or incrementality testing, so it belongs in this comparison only because people search for it alongside the others. If your sales happen on the phone after a lead form, that call tracking is the thing the other tools lack.
Stella and WorkMagic
Two newer DTC tools are worth knowing because they lead with incrementality rather than attribution. Stella combines MTA, post-purchase surveys, regional holdouts and an MMM that the holdouts recalibrate, with published prices from a free tier through $750, $3,000 and $6,000 a month. WorkMagic, a Shopify app, runs automated geo tests on matched DMA or ZIP groups and uses them to calibrate an MMM and adjust its attribution. Both are covered in more depth on the incrementality testing tools page.
Compare DTC attribution tools against your needs
The matrix below records what each tool’s public material says it does. Rather than rank them, it lets you mark which features matter to you and scores each tool against your weights. A ”?” means the vendor doesn’t say publicly, which scores zero but might just mean you need to ask.
How an attribution tool’s MMM differs from a true marketing mix model
The first difference is the training data. A classic MMM is fit on total sales (all channels, online and offline) against spend and controls like price, promotions and seasonality, usually over two or more years. Several attribution tools instead feed their own pixel-attributed conversions into the model, either as the target or as priors. That’s not wrong, and Northbeam is upfront that ingesting MTA data is a feature. However, it means the model can inherit the pixel’s blind spots: if retargeting gets too much credit in attribution, a model with attribution priors will start out believing that too, and needs strong data or an experiment to talk it out of it.
The second difference is timescale. Daily, campaign-level models are what make these tools usable for media buyers, but the effects an MMM is best at measuring (TV, brand, the slow build of adstock) play out over weeks and months. A model that’s refit every day on a short window will tend to favor channels whose effect shows up quickly. The third is uncertainty: a good MMM tells you how sure it is about each channel, and I’d ask any of these vendors to show you the interval on a channel’s ROI, not just the point estimate. The last is the outcome. Attribution tools usually model DTC revenue; if a large share of your sales happen on Amazon or in stores, check the model is fit on those sales too, which is what Prescient, Fospha, Polar and Triple Whale’s retail connections are trying to solve.
When an attribution tool is the right tool
If most of your spend is on two or three digital channels, you sell mainly on your own site, and the people using the numbers are media buyers deciding which campaigns and creatives to scale this week, an attribution tool is the right everyday instrument. It’s faster and more granular than any MMM, and the better ones now let you check themselves with tests. Pair it with a blended number like the marketing efficiency ratio so you notice when attributed revenue and actual revenue drift apart.
Once the questions become about splitting budget across channels, especially channels a pixel can’t see, or once retail and Amazon are a big part of revenue, you need a model of total sales and some experiments to keep it honest. That might be the MMM inside the attribution tool you already use, if it passes the questions in the MMM software directory, or it might be a dedicated MMM vendor or an in-house build. Either way, the incrementality guide is the best place to start thinking about what you’re actually trying to measure, and the Vexpower MMM course covers why attribution rules and a marketing mix model can give different answers about the same spend.
