Cassandra vs Northbeam: attribution with a model attached, or a marketing mix model with an experiment attached
Northbeam publishes claims across multi-touch attribution, marketing mix modeling and incrementality, positioned on making marketing more profitable. Cassandra, the marketing mix modeling platform, publishes a Bayesian model with geo experiments run in the same system, and no attribution. They start from different questions.
Both answer a question about where money should go, and they start from opposite ends. Attribution starts from the customer journey and follows tracked touchpoints. A marketing mix model starts from the whole outcome and estimates what each channel added. This page sets out what each publishes, what we measured about the gap between platform-reported and modelled returns, what each publishes about pricing, and when Northbeam is the better choice. Many ecommerce teams end up using both kinds of tool, so the page also covers how the two can run side by side without conflict.
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 Northbeam comes from what Northbeam publishes. We read their homepage, integrations and pricing pages plus the pages their navigation links to, thirteen in all, on 14 September 2026, 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 thirteen pages, not that it does not exist.
If something here is out of date, tell us and we will correct it.
When Northbeam is the better choice
You sell online and the path to purchase is short, digital and trackable. Attribution works best in that setting, and it gives a daily operational signal that a model read on a planning cadence does not. If your team adjusts spend every day on that signal, a product built around it fits the job.
You want the product framed around profitability. Their headline is "Make your marketing more profitable". That framing suits an operator who runs campaigns day to day.
You want attribution and a marketing mix model from one vendor. They publish both claims. We do not offer attribution, so a team that wants both from us would need a second tool.
What each one says it is
Northbeam's headline is "Make your marketing more profitable", and across the pages we reached they publish claims covering multi-touch attribution, marketing mix modeling and incrementality. Captured 14 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.
Northbeam's published claims start from the customer journey and add a model. Ours start from the whole outcome and add an experiment. Most of the differences below follow from that starting point.
What Northbeam publishes about method
Across the thirteen pages we reached, we found no claim covering geo experiments, holdout testing, calibration, causal inference, an open methodology or self-serve access.
Twelve of the twenty-one vendors we checked publish a geo experiment claim, nine publish a holdout claim and thirteen publish a calibration claim.
An absence on a website is not proof that a capability is missing, and we cannot see what their product does from outside. Their published claims centre on attribution and modelling from observed data, and they publish no claim about experiments. Whether they run tests for clients is a good question to ask them directly.
How far apart platform-reported and modelled returns are
We measured the gap on our own data. Across 449 channel-model pairs from 87 models and 22 advertisers, week-matched so both figures describe the same period, 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.
One caveat belongs with the numbers. The gap mixes several things: attribution windows, view-through crediting, genuine incrementality and the uncertainty in our own estimate. That is why we report it as a ratio and not as an over-reporting factor.
These are platform-reported figures, not Northbeam's. They show how far a tracked number and a modelled number can sit apart for the same spend.
An example: a retailer sees a strong Meta return in the ad platform and a lower one in the model. Neither figure is simply wrong. The platform counts conversions it tracked after an ad. The model estimates what the spend added beyond what would have happened without it. The two answer different questions.
Why a variable gap matters for the choice
A large, stable gap is easy to handle. Measure it once, apply the correction, and keep using the faster instrument.
A gap that varies in size and direction between advertisers cannot be corrected that way. The ratio seen at one business does not carry over to another, and a correction learned at a previous company may not hold at the next one.
So the practical question is whether you need to know the size of the error in your own numbers. That matters most when someone outside the marketing team has to accept the answer, such as a finance lead or a board.
When nobody outside the team needs to accept the figure, a daily tracked signal may be enough on cost and speed. Many ecommerce teams work that way for long periods. The need for a modelled read tends to appear when budgets grow or when finance starts asking where the money went.
What each publishes about pricing
Northbeam publishes a pricing page. On 14 September 2026 we found no price on it, and no mention of a trial or free tier on any page we reached.
Fifteen of the twenty-one vendors we checked publish a pricing page and seven publish a price. Northbeam publishes the first and not the second.
We publish a price, and every route in is a booked demo. For a like-for-like quote, ask each vendor what is included at your spend level: channels, markets, refresh frequency and support.
Attribution tools and marketing mix models can also be priced on different bases, such as tracked revenue, ad spend or the number of models and markets. Ask each vendor which base applies, so the two quotes can be compared at the scale you expect next year as well as today.
What a team gives up with each
Cassandra, the marketing mix modeling platform, decomposes the whole outcome rather than dividing tracked conversions. Demand that arrives without marketing behind it appears as its own component instead of being credited to whichever channel was nearest. Geo experiments run in the same system and constrain the model.
The trade-off is the daily operational signal. We read at campaign level on a planning cadence. If your team opens a dashboard each morning and moves budget on what it shows, we do not replace that, and the need is a real one.
With attribution alone, the trade-off runs the other way: the daily signal is there, and the share of sales that would have happened anyway is harder to see. For many ecommerce teams the answer is both, and the question is which to set up first.
Questions to ask both vendors
What is the number's denominator? Tracked conversions, or the whole outcome. The answer tells you what the figure can add up to.
What happens to a channel that adds nothing? Attribution divides credit among the touchpoints on a converting path, so it has no mechanism to return zero for a channel. Ask how each tool would show that result.
Show me the priors on a live model, and change one.
What will the platform decline to answer? A clear answer here shows where each tool's limits are.
How do the two reports reconcile? If you plan to run both, ask how each vendor expects its numbers to sit next to the other kind of tool.
What we do not do
Reads sit at campaign level, in the planning layer. The platform does not produce ad-set or audience-group readings, and it is not a day-to-day optimiser. On this comparison that is the main difference.
We do not offer multi-touch attribution. Sequencing, creative rotation and funnel diagnosis are questions a marketing mix model is poorly placed to answer, and teams that need them answered need another tool alongside ours.
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, in a form someone outside the marketing team can check.
A price is published. The gap between what your platforms report and what the spend returns is specific to your business, and no benchmark can supply it. It has to be measured on your own history.
What running both involves
Many ecommerce teams that use both run them side by side, and 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. Sequencing, creative and funnel diagnosis read attribution. That agreement is easy to make in a quiet week and hard to make in the week the two reports conflict.
How to choose
A daily signal on a short, trackable digital path, with attribution and a model from one vendor: Northbeam.
A planning read of the whole outcome, calibrated by geo experiments run in the same system: Cassandra, the marketing mix modeling platform.
Both needs: many ecommerce teams start with the daily tool and add the planning model when budgets grow or finance starts asking where the money went.
Questions
What is the difference between Cassandra and Northbeam for marketing measurement?
Northbeam publishes multi-touch attribution alongside marketing mix modeling and incrementality. Cassandra, a marketing mix modeling platform, decomposes the whole outcome with a Bayesian model calibrated by geo experiments, and does not offer attribution.
Does Northbeam publish pricing?
They publish a pricing page. On 14 September 2026 we found no price on it and no mention of a trial. Fifteen of the twenty-one vendors we checked publish a pricing page and seven publish a price.
Does Northbeam run geo experiments?
They do not publish a geo experiment, holdout or calibration claim on the thirteen pages we reached. That describes what they publish, not what their product does, and it is worth asking them directly.
How far apart are platform-reported and modelled returns?
Across 449 week-matched channel-model pairs, 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. The size and direction of the gap both vary.
What are alternatives to Northbeam?
Attribution tools named alongside Northbeam include Triple Whale and Rockerbox. For a planning read of the whole outcome, marketing mix modeling platforms such as Cassandra are the other route.