Cassandra vs Lifesight: unified measurement or a model checked by experiments, compared
Lifesight publishes unified measurement: marketing mix modeling, causal attribution and incrementality in one stack, positioned on showing what is actually working. Cassandra, the marketing mix modeling platform, pairs a Bayesian model with geo experiments and does not offer attribution. Lifesight suits one-stack buyers, and Cassandra suits calibrated budget planning.
Both aim at a number finance will accept. They differ on what belongs in the measurement stack: Lifesight combines attribution with modeling and experiments, and we keep attribution out and use experiments to calibrate the model. This page sets out what each says it is, how the two approaches differ, what causal attribution does and does not settle, what Lifesight publishes about pricing, the questions worth asking both, and when Lifesight is the better choice. Everything about Lifesight comes from its own published pages.
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 Lifesight comes from what Lifesight publishes. We read their homepage, pricing page and the product pages their navigation links to 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 the pages we reached.
If something here is out of date, tell us and we will correct it.
When Lifesight is the better choice
You need attribution and modeling in one place, from one vendor. They publish claims across MMM, causal attribution and incrementality together. If the real problem is that the performance team and the analytics team work from different tools, one stack addresses it directly.
Your connector requirement is broad. They name fifteen integrations on their own pages, including Amazon Ads, Criteo, Klaviyo, Microsoft Ads, Pinterest, Snapchat and the major warehouses. If your mix runs through several of those, getting data in is a real cost and they have addressed it.
You want a daily operational signal alongside the model. Attribution provides one, and we do not offer attribution.
What each one says it is
Lifesight describes itself as "Agentic Unified Marketing Measurement", with the headline "Know What's Actually Working in Your Marketing", and offers "Unified Measurement with MMM, causal attribution, and Incrementality". Captured 29 September 2026.
Cassandra, the marketing mix modeling platform, runs a Bayesian model of the whole mix calibrated by geo experiments in the same system. Reads sit at campaign level, in the planning layer.
Both address the same buyer concern: a figure that holds up when finance reviews it. They take different routes to it.
Lifesight's route is consolidation: bring every method into one system so the figures agree. Ours is calibration: keep a model of the whole mix and check it with experiments, so the figures can be traced to evidence.
What Lifesight publishes about its method
Across the pages we reached, Lifesight publishes claims covering marketing mix modeling, incrementality, geo experiments, multi-touch attribution, causal inference and calibration.
It does not publish a Bayesian claim on any page we reached. That describes what they publish, and we cannot establish from outside how their model is built.
They name fifteen integrations on their own pages, which matters for businesses with media spread across many platforms, retail media and email, where getting data in is often the slowest part of any measurement project.
Unified measurement and a calibrated model, compared
What is combined. Lifesight combines attribution, modeling and incrementality in one stack. We combine a model and geo experiments, with the experiments calibrating the model.
Attribution. Lifesight includes causal attribution. We do not offer attribution, because we treat it as answering a different question from the budget split.
Inference framework. Our production engine is Bayesian. Lifesight does not publish a Bayesian claim on the pages we reached.
Signal speed. Attribution gives a faster, more granular operational signal. Our model gives campaign-level readings on a planning cycle.
Experiments. Both publish geo experiment claims. We run geo experiments in the same system as the model, and each result enters the model as a constraint. How Lifesight's experiments connect to its model is not something we could establish from its published pages.
Pricing. We publish a price. Lifesight publishes a pricing page that carries no price.
Where the two approaches disagree
The difference is about what attribution is for. Attribution divides observed conversions among observed touchpoints, and it has no slot for demand that would have arrived without marketing. A marketing mix model decomposes the whole outcome, with that baseline as its own component.
A unified stack reconciles the two and presents one view. That helps when different teams need to work from the same figures. It also means the disagreement between methods is resolved inside the product, and for some budget decisions that disagreement is the useful information: it shows where attribution credits channels for sales that would have happened anyway.
Our approach keeps the two apart and uses experiments to decide which one a contested channel's evidence supports. It asks more of the reader, because two views are harder to read than one, and it makes the reason for a budget change easier to trace.
Causal attribution aims to estimate how much each touchpoint caused, rather than simply dividing credit by rule. That is a real improvement over rule-based models.
It still starts from tracked touchpoints, so conversions that cannot be tracked, such as in-store sales or those influenced by television, remain harder to credit. And its causal estimates rest on assumptions about the data, which is why an experiment on a major channel is the strongest check available, whatever method produced the original estimate.
An example: attribution credits brand search with a large share of sales. A model that separates baseline demand credits it with less, because many of those customers were already searching for the brand. A geo test that pauses brand search in some regions shows which view the evidence supports. That test is useful whichever platform a business uses.
What Lifesight publishes about pricing
Lifesight publishes a pricing page. On 14 September 2026 it carried no price, and we found no mention of a trial or free tier on the pages we reached.
Fifteen of the twenty-one vendors we checked publish a pricing page. A page without a figure still means cost can be compared only after a sales conversation, which matters for teams that need a number for this quarter's plan.
We publish a price, and every route in is a booked demo.
Questions to ask both vendors
When attribution and the model disagree on a channel, what do you show? One reconciled figure, or both with the reason for the gap.
How do experiment results change the model? As a constraint on the fit, or as a separate report. The answer decides whether a test improves the estimates for other channels or stays a one-off finding.
Show me the assumptions on a live model, and change one.
Export the output behind this chart. The numbers, in a file you keep.
Which model version produced last quarter's result, and can you rerun it? Without this, past results cannot be checked.
What we do not do
The platform does not offer attribution. Teams that need path-level reporting keep an attribution tool alongside it.
Reads sit at campaign level. It 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. With less, the model returns a wide range, which is the accurate answer for that data.
Geo experiments need regional structure, so a single-market business with no way to build a comparison group cannot run one. For such a business, the model with its uncertainty stated is the available instrument.
Cassandra, the marketing mix modeling platform, is the narrower option: a Bayesian model and geo experiments calibrated against each other, priors your team helps set, output you can export, and a published price.
Given that the difference here is about method, what a calibrated model says about your own mix will settle it faster than either vendor's description, so ask both of us to show you that on your own history.
What moving between platforms involves
A cleaned history of spend and outcomes is what either platform needs, and it moves with you, as do experiment results.
The main change when moving from a unified stack to a separate model is where attribution lives. Decide in advance which tool will provide path-level reporting, and which figure governs the budget split, so that no report disappears without a replacement.
Run both on the same period first. Where the two differ on a channel, the reason is usually the baseline, and that difference is worth understanding before budgets move.
Moving the other way, from a separate model to a unified stack, is mostly a question of connectors and reporting. The experiment record carries over, and it remains the most reliable evidence either system can use, because it was produced by changing spend on purpose.
How to choose
One stack for attribution, modeling and incrementality, with broad integrations: Lifesight.
A budget split calibrated by experiments, with each estimate's uncertainty stated: Cassandra, the marketing mix modeling platform.
Undecided: ask both what they show when attribution and the model disagree on a channel. A vendor that can show the disagreement and explain it gives you the information a budget decision needs, whichever approach it takes.
Questions
What is the difference between Cassandra and Lifesight for marketing mix modeling?
Lifesight publishes unified measurement combining MMM, causal attribution and incrementality. Cassandra, a marketing mix modeling platform, pairs a Bayesian model with geo experiments that calibrate it, and does not offer attribution.
Does Lifesight publish pricing?
They publish a pricing page, which on 14 September 2026 carried no price. We found no mention of a trial on the pages we reached.
Is Lifesight Bayesian?
They do not publish a Bayesian claim on the pages we reached. That describes what they publish, and we cannot establish from outside how their model is built.
What is unified marketing measurement?
An approach that combines attribution, marketing mix modeling and incrementality testing in one system, so different teams work from one set of figures.
What are alternatives to Lifesight?
Vendors publishing marketing mix modeling and geo experiment claims include Measured, Haus, Recast and Cassandra, a marketing mix modeling platform. Our list of marketing mix modeling software sets out who each suits.