Triple Whale and Ripplux both promise a truer picture of what your ads are doing. They get there by opposite routes. Triple Whale observes as many customer touches as it can and models credit across them. Ripplux withholds ads from a randomised slice of your audience and measures what changes. One is attribution. The other is measurement. The distinction decides which tool answers your question.
What each tool actually measures
Triple Whale is built around the Triple Pixel, a first-party tracking layer that observes on-site behaviour and stitches it to orders. On top of that data it offers multi-touch attribution models, a no-code dashboard builder, a SQL editor, and Moby, its AI analyst. The output is a credit allocation: this campaign gets 0.4 of this order, that one gets 0.6.
Ripplux never builds a customer identity graph. It reads your Shopify order history and your Meta and Google spend through official APIs, then runs a holdout experiment on the ad platform itself: a randomised share of your audience is withheld from a campaign, and the difference in orders between the exposed and withheld groups is the incremental effect. The output is a causal estimate with a confidence interval, not a credit split.
The practical difference shows up when the two disagree. If Triple Whale says a branded search campaign drove 200 orders and a holdout says withholding it changed order volume by almost nothing, both are reporting correctly. The campaign was present on the path to purchase and was not the reason for it. Attribution measures presence. Experiments measure causation.
Feature comparison
| Triple Whale | Ripplux | |
|---|---|---|
| Core method | Pixel-based multi-touch attribution | Randomised holdout experiments |
| Tracking script required | Yes, the Triple Pixel | No |
| Identity graph | Yes | No |
| Incrementality testing | Compass, on the Enterprise tier | Included on both paid plans |
| Media mix modelling | Compass, on the Enterprise tier | Not offered |
| Custom dashboards | Yes, no-code builder plus SQL editor | No, the output is a verdict |
| AI analyst layer | Moby, with automations on higher tiers | No |
| Platforms tested | Attribution across many channels | Holdouts on Meta and Google |
| Pricing basis | Annual GMV | Flat per plan |
| Free tier | Yes | No |
Pricing, side by side
As of 2026-07-28, Triple Whale's published pricing page lists a Free plan, Foundation starting at $219 per month, Automate starting at $749 per month, and a custom-priced Enterprise tier. Prices scale with your brand's annual GMV, so the starting figures are floors rather than quotes. Triple Whale's own FAQ states that Sonar, its server-side tracking layer, is included at no additional cost on Foundation, Automate, and Enterprise.
Ripplux prices flat: Pro at $149 per month and Growth at $299 per month, independent of your revenue. Pro includes one holdout experiment per calendar month. Growth includes unlimited experiments.
Two consequences follow from the pricing shape. First, a GMV-scaled price rises as you grow even if your usage does not, while a flat price does not. Second, and more consequentially for this comparison, the capability that answers the causal question sits at different heights in the two products. On Triple Whale's published tiers, incrementality testing arrives with Compass at Enterprise. On Ripplux it is the entry-level feature, because it is the entire product.
When Triple Whale is the better choice
We would rather you buy the right tool than buy ours, so here is the honest version.
Triple Whale is the better choice if you need a daily operating dashboard. Ripplux does not build one. If your team starts the morning by opening a screen that shows blended ROAS, contribution margin, cohort performance, and channel splits together, Triple Whale was designed for exactly that and Ripplux was not.
It is also the better choice if you want per-campaign credit at daily granularity. Holdout experiments take time to reach significance and are run on a subset of campaigns, not continuously across everything. If you are making budget shifts every morning, an attribution model that updates daily is more useful than an experiment that concludes in two weeks, even though the experiment is more rigorous.
Finally, it fits if you already have a pixel-based measurement culture and the engineering attention to maintain it. Triple Whale's pixel, dashboard builder, SQL editor, and AI layer reward a team that invests in them.
When Ripplux is the better choice
Ripplux fits the solo operator running Meta and Google themselves, without a marketer on payroll, who wants an answer rather than a surface. It fits when the question is not "how should credit be divided" but "if I turned this off, would I lose sales."
It fits when you do not want a tracking script in your theme. Some merchants have consent-mode constraints, some have had bad experiences with theme-level scripts, and some simply do not want another dependency in the checkout path. Ripplux reads through APIs and tests on-platform, so there is nothing to install.
And it fits when the budget for measurement is a few hundred dollars a month rather than a few thousand, but the rigour still has to be real.
How Ripplux measures incrementality
A holdout experiment on Meta or Google splits your audience randomly. One group can see the campaign. The other cannot. Because assignment is random, the two groups differ only in exposure, so the difference in orders between them is the campaign's causal effect. This is the same logic as a clinical trial, applied to ad spend.
That rigour comes with real eligibility limits, and we would rather state them here than after you subscribe. A holdout needs enough volume to detect an effect. Ripplux requires roughly $5,000 in monthly spend on the platform being tested and about 50 conversions per month for a test to be worth running. Below that, the confidence interval is wide enough that the result would not justify a decision, and Ripplux will tell you so instead of reporting a number.
The asymmetry is worth naming too. Pausing a campaign and restoring it later restores the setting, not the auction position or the learning state. Ripplux discloses that before you approve a change rather than after.
Do the two numbers ever agree?
Sometimes, and that is a useful signal in itself. When a holdout confirms what attribution reported, you have converging evidence from two independent methods and can act with more confidence than either alone would justify. When they diverge, the experiment is the more reliable of the two, because it is the only one of the two that manipulated anything. Observational data cannot distinguish a campaign that caused sales from a campaign that appeared alongside them.
Can you run both?
Yes, and for a brand above roughly $5M in revenue it is often the right answer. Triple Whale runs the daily operating picture. Ripplux runs periodically as the referee: every quarter, or whenever a channel's reported performance looks too good, a holdout tests whether the attributed lift survives a controlled comparison. The two do not compete for the same slot in the stack, because one reports state and the other tests causation.
If you are choosing only one, the deciding question is what you will do with the output. If the answer is "look at it every morning," buy the dashboard. If the answer is "decide whether to keep spending on this," buy the experiment.
Frequently asked
- Do I need to install a pixel to use Ripplux?
- No. Ripplux reads historical Shopify orders and ad-platform spend through official APIs, then runs holdout experiments on the ad platforms themselves. There is no tracking script to install, no theme edit, and no Custom Pixel sandbox to maintain.
- Why does Triple Whale's ROAS differ from Shopify's?
- Pixel-based multi-touch attribution credits every touch it can observe, so it counts assisted conversions Shopify does not. Shopify counts orders. Neither number answers whether the ad caused the order. A holdout experiment answers that directly by withholding ads from a randomised slice of your audience and comparing outcomes.
- Is incrementality testing available on Triple Whale's entry plans?
- As of 2026-07-28, Triple Whale's published pricing page places Compass, which carries its MMM and incrementality testing, on the Enterprise tier. Ripplux includes one holdout experiment per month on Pro at $149 and unlimited experiments on Growth at $299.
- Can I run Triple Whale and Ripplux together?
- Yes, and some merchants do. Triple Whale becomes the daily operating dashboard and Ripplux becomes the periodic referee that checks whether the attributed numbers hold up under a controlled test. The two answer different questions and do not conflict.