Measured Alternative

Looking for a Measured alternative? Most growth-stage brands don't need a full experimentation programme

Measured proves incremental lift through controlled, geo-based experiments, calibrated MMM and a media plan optimiser - a rigorous approach built for enterprise measurement programmes. Many growth-stage DTC brands looking for a Measured alternative want that same rigour applied faster and more affordably, without running dedicated holdout experiments.

HEAVY EXPERIMENT WINDOWS Requires constant operational overhead. Teams must structure holdout regions, halt campaigns, and suffer slow iteration loops. Geo-HoldoutsPlanning Time

➔AUTOMATED

AUTONOMOUS BAYESIAN MMM Pangolin automates the data science pipelines, outputting continuous, profit-optimized recommendations without stopping your campaigns. Continuous Refresh

See how Pangolin compares using your own data

The Drivers

Why businesses look for a Measured alternative

Teams typically start looking for a Measured alternative for one of a few reasons:

01 Enterprise pricing doesn't fit the budget Measured's incrementality testing has historically carried pricing in the tens of thousands of pounds per year, with custom MMM pricing on top.

02 Experiment cycles take time Geo-based holdout tests require dedicated markets and planning windows, which slows time to insight compared with continuously updating models.

03 The output is planning guidance, not decisions Measured's Media Plan Optimizer informs a media plan; turning that into a specific, approved budget change is left to the team.

04 Resourcing requirements Designing, running and reading controlled geo experiments takes analyst time and in-house measurement expertise that many growth-stage teams don't have spare.

Landscape Overview

Different types of Measured alternative

TYPE 1 Attribution-first platforms Tools like Triple Whale, centred on multi-touch attribution and broader ecommerce analytics with an AI assistant layered on top.

TYPE 2 Attribution-plus-MMM platforms Tools like Northbeam, combining multi-touch attribution with an MMM layer.

TYPE 3 Enterprise platforms Platforms like Sellforte, offering campaign and ad-set-level miROAS optimisation calibrated with experiments.

TYPE 4 Self-serve MMM toolkits Platforms like Cassandra and Forvio, offering configurable MMM and incrementality testing for teams to run themselves.

TYPE 5 Decision-first MMM Pangolin's category: Bayesian MMM that generates specific, AI-recommended budget decisions directly, without requiring dedicated teams.

Pangolin Position

Where Pangolin fits

Pangolin is an automated marketing measurement platform built around Bayesian Marketing Mix Modelling. It models channel contribution, saturation and contribution profit continuously from your existing marketing and business data, then generates a specific AI-recommended budget reallocation that a human approves before it goes live. It's built for growth-stage DTC and ecommerce brands who want continuously updating measurement without committing to an enterprise-scale experimentation programme.

Direct Comparison

How Pangolin compares to Measured

Capability	Pangolin	Measured
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
Continuous model refresh	Core, automatic	Available, though experiment cycles add planning time
Budget optimisation	AI-generated recommendations, core feature	Available via the Media Plan Optimizer
Autonomous execution	Yes, core to the product (human-approved)	Not a core feature; output is planning guidance
Contribution profit focus	Core feature	Not a primary focus
Pricing model	Positioned for growth-stage budgets	Enterprise; starts in tens of thousands per year

The Core Difference

Pangolin vs Measured

Measured's core strength deserves genuine respect: controlled, geo-based experiments are the causal gold standard in marketing measurement, isolating what would have happened without a campaign rather than inferring it statistically. For enterprise organisations that need experimentally-proven answers for high-stakes decisions, that's real rigour. Pangolin takes a different, more accessible route - continuously updating Bayesian MMM that produces a specific budget recommendation without the planning cycles and dedicated resourcing that controlled experimentation requires.

When Pangolin is the right choice ✓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

When another solution may be better ○If causal, experimentally-proven incrementality is a hard requirement, Measured's geo-based testing approach is purpose-built for it ○If you have the budget and planning cycles to support ongoing experimentation, Measured's test-calibrated MMM adds real rigour ○If executive stakeholders specifically require experimental proof alongside modelled results, Measured's approach directly answers that ○If your organisation is enterprise-scale with dedicated measurement resourcing already in place, the overhead of running experiments is less of a barrier

Platform Interface

From data to a better budget decision

Data➔ Measurement➔ Insights➔ Recommendations➔ Better budget decisions

Live Bayesian MMM Output A live view of channel contribution, saturation and contribution profit, with AI-generated budget recommendations ready for approval.

Meta AdsActive saturation limitsStable

Google SearchOver-claiming creditReduce

TikTok VideoHigh marginal profit curveScale

Live Action Recommendations · No dedicated analyst required · Optimised for contribution profit · One click to approve

Common Inquiries

Frequently Asked Questions

Why would a brand look for a Measured alternative?

Common reasons include enterprise pricing that doesn't fit a growth-stage budget, the planning time required for controlled experiments, and wanting a system that generates an approvable budget recommendation rather than planning guidance.

Is Pangolin a direct replacement for Measured?

Not for every use case. Pangolin offers continuously updating Bayesian MMM without controlled experiments. Brands specifically requiring experimentally-proven causal incrementality would still need an approach like Measured's.

Does Pangolin run geo-based experiments like Measured?

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

What type of alternative should I choose?

It depends on whether experimental proof is a genuine requirement. Growth-stage brands wanting fast, automated modelling without an enterprise measurement programme tend to fit Pangolin; enterprise organisations requiring experimentally-proven incrementality may still need Measured.

How does pricing compare?

Measured's incrementality testing has historically started in the tens of thousands of pounds per year, with custom enterprise MMM pricing on top. Pangolin is positioned for growth-stage budgets without that scale of commitment.

A Measured alternative built for growth-stage budgets, not enterprise experimentation programmes

See what continuously updating Bayesian MMM shows for your own channel mix.

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