Cassandra vs Triple Whale: a commerce operating system with measurement inside, or a marketing mix model calibrated by experiments
Triple Whale publishes an AI operating system for ecommerce, with attribution, a proprietary marketing mix model and GeoLift tests combined in its Compass product, and a free plan. Cassandra, the marketing mix modeling platform, publishes a Bayesian model with geo experiments run in the same system, and no attribution.
Triple Whale covers far more of an ecommerce team's daily work: data, analytics, attribution, creative and an AI agent that can act on ad accounts. Measurement is one part of that product. We cover one part of the work, the planning model, with experiments inside it. This page sets out what each publishes, how each approaches the model and tests, what each publishes about pricing, and when Triple Whale is the better choice. Many teams use both kinds of tool, so the page also covers running them side by side.
What Cassandra is
Cassandra is a marketing mix modeling platform. Its production engine is a Bayesian model built on PyMC, so every channel effect comes back as a distribution with its uncertainty stated. Geo experiments are designed and run in the same system, using the same method family as GeoLift, and each result enters the model as a constraint rather than as a separate report.
Reads sit at campaign level, in the planning layer, and nothing changes in an ad account unless a person confirms the exact figure. The platform does not do multi-touch attribution.
It is not built on Google Meridian. Meridian is one of three model engines a project can choose, it is being retired, and it is not offered on new projects.
How this comparison was made
Everything below about Triple Whale comes from what Triple Whale publishes. We read twelve of their pages, including the homepage, pricing, marketing mix modeling, attribution, free plan and integrations pages, on 24 September 2026, in a browser, and quoted what we found.
We have not tested their product. Where we describe an absence, we mean we could not find it on those twelve pages, not that it does not exist.
If something here is out of date, tell us and we will correct it.
When Triple Whale is the better choice
You run an ecommerce brand and want one platform for most of the work. Triple Whale describes itself as "the AI operating system built for modern commerce", covering data, analytics, attribution, creative and measurement. A team that wants one tool for all of it is its intended buyer.
You need attribution every day. They publish eight attribution models and their own tracking pixel. We do not offer attribution.
You want to start free. They publish a free plan with first- and last-click attribution and no credit card required, which lets a small team try the platform before any sales conversation.
You want an AI agent that can act on ad accounts. They describe Moby as connected to ad accounts, able to scale budgets and pause campaigns, with the team in control. Our platform changes nothing in an ad account unless a person confirms the exact figure.
What each one says it is
Triple Whale describes itself as "the AI operating system built for modern commerce". Its Compass product "unifies Attribution, Marketing mix Modeling (MMM), and Incrementality into one trusted view of performance", and its homepage invites teams to "validate true incremental impact with GeoLift tests". Captured 24 September 2026.
Cassandra, the marketing mix modeling platform, runs a Bayesian model of the whole mix with geo experiments designed and run in the same system, and each result constrains the model. Reads sit at campaign level, in the planning layer.
Triple Whale describes a broad commerce platform with measurement as one of its parts. We describe a measurement product. That difference in scope shapes most of the comparison.
How each describes its marketing mix model
Triple Whale states that "MMM is a statistical analysis powered by our own proprietary model". It describes the model as going beyond attribution to include offline spend, owned media and factors such as promotions and seasonality, with scenario planning and confidence scores. It also states that "MMM works best alongside attribution from Triple Whale".
We found no published description of the model's method, and no Bayesian claim, on the pages we reached.
Our production engine is a Bayesian model built on PyMC. Every channel effect comes with a stated range, and priors are co-set with your team and stay visible. For either vendor, the useful request is the same: show the assumptions behind one channel's figure, and what happens when one changes.
How each approaches geo tests
Triple Whale publishes GeoLift tests on its homepage, and its pricing page lists "Run holdouts, geo-lift tests, and marketing mix modeling in one place" in its Enterprise tier. It also states that "every test sharpens the next budget allocation".
We design and run geo experiments in the same system as the model, using the same method family as GeoLift. Regions are matched on their history before the test, and the result enters the Bayesian model as a constraint on the tested channel.
Both vendors say that tests should shape the budget. The questions worth asking are how each result changes the model's numbers, and how long its effect lasts across later refreshes.
It is also worth asking which tier includes the tests. On Triple Whale's pricing page, holdouts and geo-lift tests appear in the Enterprise tier, and incrementality testing is listed as an add-on.
How far apart platform-reported and modelled returns are
Attribution and a marketing mix model often give different answers for the same spend. We measured how far apart on our own data. Across 449 channel-model pairs from 87 models and 22 advertisers, week-matched, the middle half of advertisers saw incremental return between 0.47 and 1.14 times what Meta reported and between 0.27 and 1.23 times what Google reported. ([full analysis](/blog/marketing-attribution-software-analysis))
Platform-reported was the higher figure for 58% of advertisers on Meta and 72% on Google. For the rest, the model found more value than the platform reported.
These are ad-platform figures, not Triple Whale's. Triple Whale publishes its own attribution based on its own pixel. The analysis shows why a tracked number and a modelled number can differ, and why a test is the way to settle which one to plan with.
What each publishes about pricing
Triple Whale publishes a pricing page with four tiers: Free, Foundation, Automate and Enterprise. Its meta description asks visitors to select a revenue tier, and we found no price figure in the page text as it loaded. The free plan needs no credit card. Its feature comparison lists marketing mix modeling and incrementality testing as add-ons, and the Enterprise tier routes to sales.
We publish a price, and every route in is a booked demo.
For a like-for-like comparison, compare the tier that includes the marketing mix model and incrementality testing with our published price. Ask each vendor what is included at your revenue and spend level.
Automation and control
Triple Whale describes Moby as connected to ad accounts, so it "can scale budgets, pause underperformers, launch campaigns, and more", with the team "always in control".
Our platform proposes budget changes and changes nothing in an ad account unless a person confirms the exact figure. Reads sit at campaign level, in the planning layer. Daily campaign management is outside what we do.
These are different jobs. Daily execution benefits from speed. A planning decision reviewed by finance benefits from a figure a person has checked. A team can reasonably want both, from different tools.
An example: an agent pauses a campaign after three weak days, which is a sensible daily action. The quarterly question of how much the channel should get is a different one. It rests on what the channel added over months, and on any test that measured it. Keeping the two decisions apart lets each tool do the job it was built for.
Questions to ask both vendors
How is the model built? Ask for the method, the assumptions, and how uncertainty is shown.
Which channels have been tested, and when? A figure anchored by a recent test carries more weight than one resting on history alone.
How does a test result change the model? Ask to see one channel before and after a result was added.
What happens to a channel that adds nothing? Attribution divides credit among touchpoints on converting paths, so it has no mechanism to return zero for a channel. Ask how each tool would show that result.
What does the tier with the model and tests cost at your scale?
What we do not do
We are not a commerce operating system. Analytics, creative tools and daily campaign management are outside what we sell.
We do not offer multi-touch attribution. Journey analysis, creative rotation and daily optimisation need another tool alongside ours.
Reads sit at campaign level. The platform does not produce ad-set or audience-group readings, and it is not a day-to-day optimiser.
A channel needs enough history behind it, and enough movement in that spend to read against. Geo experiments need regional structure, so a single-market business with no way to build a comparison group cannot run one.
Cassandra, the marketing mix modeling platform, is for the planning question: where the budget should go, with experiments behind the main channels, in a form someone outside the marketing team can check.
A price is published. Priors are co-set with your team, and every experiment result stays visible in the model it constrains.
What running both involves
Many ecommerce teams use an attribution platform and a marketing mix model side by side. The friction is rarely technical.
Label each report with its scope. An attribution dashboard headed "share of tracked conversions" and a model read headed "share of total revenue" stop looking contradictory once both labels are visible.
Agree which report answers which question before the numbers disagree. Budget allocation and growth planning read the model. Journey analysis, creative and daily optimisation read attribution.
If an agent acts on ad accounts, decide which figures it acts on. A daily signal can drive daily actions, while quarterly budget splits follow the plan.
How to choose
An ecommerce brand that wants one platform for data, attribution, creative and measurement, starting free: Triple Whale.
A planning read of the whole outcome, calibrated by geo experiments run in the same system, with every budget change confirmed by a person: Cassandra, the marketing mix modeling platform.
Both needs: many teams run the daily platform and add a planning model when budgets grow or finance starts asking where the money went.
Questions
What is the difference between Cassandra and Triple Whale for marketing mix modeling?
Triple Whale publishes an ecommerce operating system that includes attribution, a proprietary marketing mix model and GeoLift tests. Cassandra, a marketing mix modeling platform, publishes a Bayesian model calibrated by geo experiments run in the same system, and no attribution.
Does Triple Whale have a free plan?
Yes. It publishes a free plan with first- and last-click attribution and no credit card required. Its marketing mix model and incrementality testing are listed as add-ons on its pricing page.
Is Triple Whale's marketing mix model Bayesian?
Triple Whale describes its marketing mix model as its own proprietary model. We found no Bayesian claim and no published description of its method on the pages we reached on 24 September 2026.
Does Triple Whale run geo experiments?
It publishes GeoLift tests on its homepage, and lists holdouts and geo-lift tests in its Enterprise tier.
What are alternatives to Triple Whale?
Tools named alongside Triple Whale include Northbeam and Rockerbox for attribution. For a planning read of the whole outcome, marketing mix modeling platforms such as Cassandra are the other route.