Bayesian MMM Comparison

Pangolin vs Measured

Measured and Pangolin both aim to move marketing measurement beyond attribution, but they get there differently. Measured proves incrementality through controlled, geo-based experiments and test-calibrated MMM, built for enterprise measurement programmes. Pangolin automates Bayesian MMM directly into an AI-generated budget recommendation, built for growth-stage DTC brands.

MEASURED: EXPERIMENT-CALIBRATED Rigorous incrementality testing via geo holdouts. Calibrates models based on empirical tests. Powerful, but requires high operational bandwidth. Test-Driven

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PANGOLIN: AUTONOMOUS RECOMMENDATIONS Zero configuration. Continual automated data loops generate direct contribution-profit-optimised budget advice with one-click approvals. Continuous Optimization

See how Pangolin compares using your own data

At a Glance

Who's each platform for?

Pangolin is best suited for: BUDGET EFFICIENCY & AUTOMATION Growth-stage DTC and ecommerce brands that want automated, continuously updating Bayesian MMM and AI-generated budget recommendations, without running dedicated controlled experiments or committing to enterprise-scale measurement spend.

Measured is best suited for: ENTERPRISE CAUSAL EXPERIMENTATION Enterprise marketers and performance marketing teams who want causal proof of incremental lift through controlled geo experiments, alongside test-calibrated MMM, and have the budget and planning cycles to support ongoing experimentation.

Head to Head

Direct Capability Mapping

Capability	Pangolin	Measured
1. Measurement Foundations
Marketing Mix Modelling	Core, Bayesian, MMM-first	Available, calibrated using experimental results
Incrementality testing	Not a current capability	Core method - geo-based holdout tests and A/B experiments
Multi-touch attribution	Not the primary method	Available as a secondary/tactical signal
Continuous model refresh	Core, automatic	Available, though experiment cycles add planning time
2. Action & Optimisation
Budget optimisation	AI-generated recommendations, core feature	Available via the Media Plan Optimizer
Saturation modelling	Core output	Available, informed by experimental calibration
Autonomous execution	Yes, core to the product (human-approved)	Not a core feature; output is planning guidance
3. Commercial Measurement
Revenue optimisation	Yes	Yes
Contribution profit optimisation	Core feature	Not a primary focus
4. Product Experience & Audience Fit
Time to first insight	Fast - within the first data connection cycle	Longer - experiment design and holdout periods take planning time
Pricing model	Positioned for growth-stage budgets	Enterprise; incrementality testing has historically started in the tens of thousands per year
Resourcing required	Designed for marketing leaders directly	Best suited to teams with measurement or analytics resourcing

Foundational Beliefs

Two contrasting approaches to measurement

APPROACH A Measured: Experiment-first causal proof Run controlled, geo-based holdout experiments to prove true incremental liftCalibrate MMM against experimental results for a triangulated viewProvide planning guidance for the team to build into media plans

APPROACH B Pangolin: Automated, decision-first MMM Model incrementality and contribution profit directly through Bayesian MMMUpdate continuously as new data arrives, without dedicated experiment cyclesGenerate a specific budget recommendation and route it for human approval

The Pangolin Advantage

Where Pangolin is different

No experiment cycles required to get started Controlled geo experiments are a rigorous method, but they take planning time and dedicated markets to hold out. Pangolin's Bayesian MMM produces continuously updating contribution estimates without that setup overhead.

Built for growth-stage budgets Measured's incrementality testing has historically carried enterprise-level pricing; Pangolin is built to be accessible without that scale of commitment.

Profit, not just lift Recommendations are optimised for contribution profit specifically, rather than proving incremental lift as an end in itself.

From model to approved action Pangolin generates a specific budget recommendation directly from the model, rather than planning guidance that still needs to be built into a media plan by the team.

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 continuously updating Bayesian MMM without running dedicated controlled experiments ✓Budget and timelines don't support enterprise-scale measurement programmes ✓You want AI-generated budget recommendations, not planning guidance to build into a media plan yourself ✓Contribution profit, not just proven lift, needs to drive the decision

Consider Measured if: ○Causal, experimentally-proven incrementality is a requirement, not just a preference ○You have the budget and planning cycles to support geo-based holdout testing ○Your organisation is enterprise-scale with dedicated measurement resourcing ○Executive stakeholders specifically require experimental proof alongside modelled results

Pangolin vs Measured FAQ

Pangolin vs Measured Comparison FAQ

How does Pangolin compare with Measured?

Measured proves incremental lift through controlled, geo-based experiments and test-calibrated MMM, aimed at enterprise measurement programmes. Pangolin automates Bayesian MMM into a continuously updating, AI-generated budget recommendation, built for growth-stage DTC brands.

Does Pangolin run controlled experiments like Measured?

No. Pangolin's contribution modelling is based on Bayesian MMM applied to your existing marketing and business data, rather than dedicated geo-based holdout experiments.

Is experimental proof more reliable than MMM?

Controlled experiments are widely considered the causal gold standard, but they require planning time, dedicated holdout markets and ongoing resourcing. Bayesian MMM offers a continuously updating estimate without that overhead, which is why many measurement programmes use both methods at different stages of maturity.

Which platform is better for DTC brands?

Growth-stage DTC brands wanting fast, automated modelling without an enterprise measurement programme tend to be a better fit for Pangolin. Enterprise organisations requiring experimentally-proven incrementality, with the resourcing to support it, may be better served by Measured.

What should businesses consider when choosing between the two?

Consider whether experimental proof of incrementality is a genuine requirement or a nice-to-have, the resourcing and timelines available to support ongoing testing, and whether you want a system that generates an approvable budget recommendation directly or planning guidance to build into a media plan yourself.

Continuous Bayesian MMM, without the enterprise measurement programme

See what automated modelling reveals about your own channel mix.

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