You've outgrown Ads Manager. Meta says it drove 140 sales last week, Google says 90, Shopify says you had 180 orders in total, and nobody can tell you which ads actually paid for themselves. So you start looking at attribution tools, two names come up in every conversation, and you end up weighing Northbeam vs Triple Whale.
It's a fair question, because on the surface they look alike. Both put a tracking pixel on your store (a pixel is a small piece of code that records what visitors do on your site), both build their own picture of which ads earned each sale, and both promise numbers you can trust more than the ad platforms' own reports.
Underneath, they're built around different ideas of who you are and what you need. This guide walks through how each one works, where they differ, and the questions that should actually decide it for your store. We'll stick to what each company says publicly about its own product, and we won't quote prices, since they change and depend on your size.
First, what both tools are trying to fix
Every ad platform grades its own homework. Meta counts a sale if someone clicked or viewed a Meta ad within its window. Google does the same for Google ads. TikTok does the same for TikTok. If one shopper touched all three before buying, all three can claim that one sale. Add up their reports and you get more sales than your store ever had.
An attribution tool sits outside the platforms. It records every visit to your store, connects those visits to the order that eventually happened, and then splits the credit for that order across the ads that led to it. The rule for splitting credit is called an attribution model. "Give it all to the last ad clicked" is one model. "Split it evenly across every visit" is another. If you want the basics first, start with what multi-touch attribution is .
Both Northbeam and Triple Whale do this. The differences are in how they collect the data, which models they offer, and what they put around the attribution itself.
Northbeam in plain English
Northbeam describes itself as a measurement platform for direct-to-consumer brands, ecommerce companies and agencies. Its center of gravity is the attribution model itself, and the analysis you do on top of it.
What Northbeam lists on its own site and documentation:
- Multi-touch attribution built on its own first-party pixel. A first-party pixel is tracking code that runs under your store's own domain, which makes it harder for browsers to block than a third-party one.
- [Seven attribution models](https://docs.northbeam.io/docs/attribution-models). Three simple ones (First Touch, Last Touch, Last Non-Direct Touch) and four multi-touch ones: Linear, Clicks-Only, Clicks + Modeled Views and Clicks + Deterministic Views.
- View-through measurement. "Modeled views" uses machine learning to estimate how much ad impressions (people who saw an ad without clicking) contributed. "Deterministic views" matches impressions using data from the platforms themselves.
- Accounting modes, including an accrual mode that credits revenue back to when the ad was shown, rather than only to the day the order landed.
- [MMM+](https://www.northbeam.io/products/mmm-plus), its marketing mix modeling product, and a separate incrementality offering. Marketing mix modeling (MMM) is a statistical method that estimates each channel's effect from patterns in total spend and total sales over time, without tracking individual shoppers. Incrementality testing measures how many sales an ad actually caused by comparing people who saw it against people who didn't.
- Apex, which sends Northbeam's performance data back to ad platforms so their algorithms optimize on it.
- Creative analytics, product analytics and a metrics explorer for spotting effects across channels.
- Integrations with 20+ platforms, including Meta, Google Ads, TikTok, Snapchat, Pinterest, Amazon Ads, Klaviyo and The Trade Desk.
The thing to understand about Northbeam's Clicks-Only model: by Northbeam's own description, it gives no credit to direct visits, organic search, paid branded search, or email and SMS whenever the journey also includes an upper-funnel touchpoint. Upper-funnel means ads aimed at people who don't know you yet, like prospecting campaigns on Meta or TikTok, as opposed to channels that mostly catch people who already decided to buy. That's an opinion baked into the model. It can be the right opinion for a brand spending heavily on prospecting, but it's worth knowing that's what the default view is doing.
Triple Whale in plain English
Triple Whale positions itself as an operating system for ecommerce brands, and it's built primarily for Shopify stores. Where Northbeam centers on measurement, Triple Whale wraps measurement in a wider set of day-to-day tools.
What Triple Whale lists publicly:
- [Triple Pixel](https://www.triplewhale.com/blog/triple-pixel), its first-party pixel, which combines browser and server-side tracking and does identity resolution (working out that visits from different devices or sessions belong to the same person). It supports headless Shopify stores.
- [Several attribution models](https://kb.triplewhale.com/en/articles/5960333-understanding-and-utilizing-attribution-models), including First Click, Last Click, Linear All, Linear Paid, Total Impact, Triple Attribution, and Clicks & Deterministic Views.
- Compass, which brings multi-touch attribution, marketing mix modeling and incrementality testing into one measurement framework.
- Moby, its AI layer. Triple Whale describes Moby as doing automated analysis and also more operational work, such as creative generation, email campaigns and landing pages.
- Dashboards and templates that pull Shopify, Meta, Google and other sources into one summary view.
The practical upshot: Triple Whale tries to be the screen you open every morning, with attribution as one part of it. Northbeam tries to be the source of truth you make big budget decisions from.
Northbeam vs Triple Whale, side by side
| Question | Northbeam | Triple Whale |
|---|---|---|
| Main focus | Measurement and attribution depth | Broad ecommerce operating platform |
| Store platform focus | DTC and ecommerce generally | Built primarily for Shopify |
| Tracking | First-party pixel | First-party pixel, browser plus server, identity resolution |
| Multi-touch models | Linear, Clicks-Only, Clicks + Modeled Views, Clicks + Deterministic Views | Linear All, Linear Paid, Total Impact, Triple Attribution, Clicks & Deterministic Views |
| View-through credit | Modeled and deterministic | Deterministic, plus platform-reported views |
| MMM and incrementality | MMM+ and incrementality products | Combined in Compass |
| AI | Analysis features | Moby, which also generates creative and campaigns |
| Sending data back to platforms | Apex | Yes, through its pixel and integrations |
Both lists change often. Before you decide, check each company's current product pages, because a feature listed as separate today might be bundled tomorrow.
The differences that actually matter
Feature lists look similar. These are the questions that separate the two in practice.
1. How much of the model do you want to see?
Some proprietary models are partly black boxes. You can see the output, but you can't fully reproduce how a given order's credit was split. Northbeam's Clicks + Modeled Views and Triple Whale's Total Impact and Triple Attribution are their own designs, and they rely on modeling.
That isn't automatically bad. Modeling can fill gaps that tracking can't. But it means you're trusting the vendor's choices. If your team needs to explain every number to a finance lead, simple rules you can check by hand (first touch, last touch, linear, time decay, position based) are easier to defend. A useful habit: look at a single real order in the tool and see whether you can understand why each ad got the credit it got.
2. Do you count view-throughs?
A view-through conversion is a sale credited to an ad the shopper saw but didn't click. Both tools offer some form of view credit. It matters most if you spend heavily on video or prospecting ads that people watch and don't click.
The risk is the opposite one: views are cheap to claim. Someone scrolling past a video for half a second counts as an impression. If view credit is on, compare the same period with it off. If one channel's results swing wildly, that channel's value rests mostly on views, and you'll want a holdout test before you scale it.
3. Do you want one tool or a focused one?
Triple Whale's pitch includes doing more of the work inside one product, including AI that generates ads and emails. Northbeam's pitch is depth of measurement. Neither is wrong. The question is whether you already have tools you like for creative and email, and whether you want your attribution vendor also running those.
4. Are you planning for MMM and incrementality?
Both now sell ways to go beyond tracked clicks. If you spend across many channels, including ones you can't track click by click (TV, podcasts, out-of-home), you'll eventually want an MMM or a lift test alongside attribution. Ask each vendor how much data their MMM needs, how often it updates, and whether it's included or an add-on.
5. Where does your store run?
Triple Whale is built around Shopify. Northbeam talks about DTC brands more broadly. If you're on Shopify, both fit. If you're not, check platform support with each one directly.
Common mistakes when comparing attribution tools
- Judging by which tool shows the best ROAS. ROAS (return on ad spend) is revenue divided by ad spend. A tool that makes your ads look great isn't more accurate, it may just be more generous. The better test is whether total attributed revenue adds up to roughly what your store actually took.
- Running a trial for two weeks and comparing to Ads Manager. Attribution only starts from the day the pixel goes in. Orders in the first weeks often came from visits the tool never saw. Give it a full buying cycle before you judge.
- Comparing different models. Northbeam's default view and Triple Whale's default view aren't the same model. Line them up on the same model (both offer a Linear model) before you compare numbers.
- Forgetting the unconnected accounts. If you run Pinterest ads and don't connect Pinterest, its sales still show up, under "direct" or "organic," and your other channels' ROAS looks better than it is.
- Ignoring setup work. Ask who installs the pixel, how long it takes, and what happens when Shopify changes its checkout.
A checklist before you sign either contract
- Write down the three decisions you want this tool to help you make (for example: "which prospecting campaigns to cut").
- Ask both vendors to show you one real order's journey and how credit was split.
- Confirm which ad platforms import spend, and which receive conversions back.
- Ask whether attributed revenue reconciles to your Shopify revenue.
- Check whether you can change attribution windows and models yourself.
- Ask what's included and what's an add-on (MMM, incrementality, AI).
- Find out how data leaves the tool: exports, API, warehouse sync.
- Plan a trial long enough to cover your typical time from first visit to purchase.
Where Soran fits
Soran is a third option, aimed at Shopify brands and agencies that want attribution and profit in one place without a black box. It tracks orders on the server, so ad blockers and closed tabs don't lose them, and splits each order's credit once across the visits that led to it. That means totals add up to what the store actually took, not the sum of every platform's claims.
You can switch between ten attribution models, all simple rules you can check by hand (first touch, last touch, linear, time decay with an adjustable half-life, U-shaped, J-shaped and others), and every number in the report updates. Spend from Meta and TikTok is matched to the exact ad behind each order. If a channel shows revenue but no spend, Soran flags it as a probably unconnected ad account instead of showing a flattering ROAS. Revenue steps down to contribution margin after discounts, refunds, product costs, shipping and fees, and conversions go back to Meta and TikTok with duplicate removal.
It's also worth being clear about what Soran doesn't do. There's no MMM or incrementality testing, Google Ads isn't connected yet, and Shopify is the only store platform. If those are must-haves, Northbeam or Triple Whale may suit you better. Pricing is one flat plan with every feature and no event, seat or volume limits, and the free trial needs no card.
FAQ
Is Northbeam better than Triple Whale?
Neither is better across the board. Northbeam leans toward measurement depth, with several proprietary models and view-through options. Triple Whale leans toward being an all-in-one platform for Shopify brands, with dashboards and AI that also does creative and campaign work. The better one is the one that fits the decisions you need to make.
Do Northbeam and Triple Whale show the same numbers?
Usually not, even on the same store. They use different pixels, different default models and different rules for view-throughs. To compare fairly, set both to the same model, such as Linear, and the same attribution window, then compare a full month.
Can I use Northbeam or Triple Whale for past orders?
Like any pixel-based tool, they can only see visit paths from the day tracking was installed. Past orders may be imported for revenue totals, but which ads drove them can't be reconstructed after the fact.
Do I need MMM if I already have multi-touch attribution?
Not at first. Attribution answers "which tracked ads were involved in each sale." MMM answers "how much does each channel move total sales, including the ones I can't track." Once you spend meaningfully on channels without clicks, like TV or podcasts, MMM or lift tests become worth adding.
What should a small Shopify store use?
If you mostly run Meta and maybe TikTok, you don't need the heaviest tool. Look for server-side tracking, clear models you can understand, spend matched to ads, and profit, not just revenue. The pros and cons of last-click attribution is a good read before you pick a default model.
Want to see how Soran compares on your own store's data? Book a demo .