Bayesian MMM Comparison

Pangolin vs Cassandra

Cassandra and Pangolin both bring Bayesian Marketing Mix Modelling to teams without a data science function, but they hand you different amounts of the work. Cassandra is a no-code builder you configure and run yourself. Pangolin is a managed system that models your data and generates the budget recommendation for you.

CASSANDRA: NO-CODE BUILDER Marketers configure, run, and maintain their own models. Gives ultimate setup control but requires operational overhead. Self-Configured

➔vs

PANGOLIN: MANAGED OPTIMISER No model configuration required. Connected datasets are automatically modelled to output human-approvable budget actions. Zero-Overhead MMM

See how Pangolin compares using your own data

At a Glance

Who's each platform for?

Pangolin BEST SUITED FOR Growth-stage DTC and ecommerce brands that want contribution-profit-optimised budget recommendations generated automatically, without building or configuring a model themselves.

Cassandra BEST SUITED FOR Marketing teams and agencies across B2B and other verticals who want a self-service, no-code tool to build and run their own MMM, and are comfortable owning the model configuration.

Head to Head

Direct Capability Mapping

Capability	Pangolin	Cassandra
1. Measurement Foundations
Marketing Mix Modelling	Core, Bayesian (proprietary), managed	Core, Bayesian (built on Meta's Robyn), self-configured
Incrementality testing	Built into the contribution model	Available (GeoMatch geo-experiments) for calibration
Model ownership	Modelled and maintained by Pangolin	Built and configured by the user via a no-code UI
Refresh cadence	Continuous	Regular (weekly to monthly, depending on plan)
2. Action & Optimisation
Budget optimisation	AI-generated recommendations, core feature	Budget Allocator tool, user-run scenario simulation
Autonomous execution	Yes, core (human-approved)	Not core — recommendations are actioned manually
Profit focus	Core contribution profit optimisation	Not a primary focus (primarily revenue/ROI-based)
3. Product & Audience Fit
Target user	Marketing leaders, zero data science required	Marketers and agencies comfortable with configuration
Vertical focus	DTC and ecommerce specifically	Broad (DTC, B2B, Higher Ed, Charity, Fintech)
Setup approach	Managed, fully automated pipeline	Self-service, no-code model builder
Agency support	Not a current focus	Dedicated agency workspaces and bespoke pricing

Foundational Beliefs

Two contrasting approaches to measurement

APPROACH A Cassandra: Self-Service Model Building Provide a no-code UI for marketers to build and train their own Bayesian MMM.Let users run budget allocation scenarios and calibrate with incrementality tests.Leave interpretation and execution to the team operating the platform.

APPROACH B Pangolin: Managed, Decision-First MMM Model incremental contribution and contribution profit automatically from connected data.Generate a specific budget recommendation without requiring model configuration.Route the recommendation for human approval before execution.

The Pangolin Advantage

Where Pangolin is different

Managed, not self-built Cassandra hands you a no-code builder; Pangolin builds and maintains the model for you, so there's no configuration step between connecting your data and getting a recommendation.

Optimised for profit specifically Pangolin's recommendations target contribution profit, whereas Cassandra's Budget Allocator is built around revenue-based ROI scenarios.

From recommendation to approved action Pangolin routes AI-generated recommendations through human approval as a core workflow; Cassandra's output is a scenario suggestion the team implements manually.

Purpose-built for DTC and ecommerce Cassandra is designed to work across many verticals; Pangolin's product and category positioning are built specifically around DTC and ecommerce brands' data and channel mix.

Pangolin's Continuous Optimization

From data to a better budget decision

Data➔ Measurement➔ Insights➔ Recommendations➔ Better budget decisions

Live Bayesian MMM Output A live view of channel contribution profit and saturation, generated automatically with AI-written explanations.

Meta AdsModel refreshes continuously - scale marginScale

Google SearchMarginal return saturation detectedReduce

Action Recommendation · Target Focus: Contribution Profit · Workflow: One click to approve

Choose Pangolin if: ✓You want a managed system that generates the recommendation, not a model you configure yourself ✓Contribution profit needs to drive the decision, not just revenue-based ROI scenarios ✓You're a DTC or ecommerce brand and want a platform built specifically around that channel mix ✓You'd rather approve a specific recommendation than run your own allocation scenarios

Consider Cassandra if: ○You or your agency want direct, no-code control over building and adjusting the MMM ○You're operating outside DTC/ecommerce, in B2B, charity or another vertical ○You're an agency managing MMM across multiple clients and want dedicated workspace tooling ○Your team is comfortable owning model configuration and interpretation

Pangolin vs Cassandra FAQ

Pangolin vs Cassandra Comparison FAQ

How does Pangolin compare with Cassandra?

Cassandra is a no-code MMM builder that marketers or agencies configure and run themselves. Pangolin is a managed platform that models your data and generates an AI-driven, contribution-profit-optimised budget recommendation automatically.

Do I need to build my own model with Pangolin, like with Cassandra?

No. Pangolin's model is built and maintained for you once your data is connected, without a configuration step.

Which platform is better for DTC brands?

DTC and ecommerce brands wanting a managed, profit-optimised recommendation without configuring a model themselves tend to fit Pangolin better. Brands or agencies wanting direct, no-code control over their own MMM, or operating outside DTC/ecommerce, may prefer Cassandra.

Is Cassandra's no-code approach more transparent than Pangolin's?

Building your own model in a no-code UI does give direct visibility into its configuration. Pangolin trades that hands-on configuration for a managed model and an automatically generated recommendation, which suits teams who'd rather not own the modelling process themselves.

Does Pangolin support agencies managing multiple clients, like Cassandra?

Not as a current focus. Cassandra's agency-specific workspaces and pricing are built for that use case; Pangolin is built for a single growth-stage DTC or ecommerce brand.

Skip the model building. Get straight to the budget decision.

See what an automated, profit-optimised recommendation looks like for your own brand.

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