Bayesian MMM Comparison

Pangolin vs Northbeam

Northbeam and Pangolin are both built to give DTC brands a clearer view of channel performance than platform reporting alone. The difference is what happens with that view once you have it - Northbeam is an attribution-first platform with MMM added on top; Pangolin is an MMM-first platform that turns its model directly into an approved, executable budget plan.

NORTHBEAM: ATTRIBUTION-FIRST Deep click-based tracking with machine learning attribution. Helps trace complex conversion paths but leaves the hard job of budget scaling up to manual strategists. MTA-Driven

➔vs

PANGOLIN: DECISION-FIRST MMM Autonomous Bayesian MMM that operates on continuous data loop. Directly outputs contribution-profit-optimised budget plans ready for human approval. 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 Bayesian MMM and AI-generated budget recommendations, without needing a dedicated data analyst to interpret the output before acting on it.

Northbeam is best suited for: Deep multi-channel attribution DTC brands spending significantly on paid media across three or more channels, with an in-house growth operator, media buyer or analyst who wants deep, self-serve attribution modelling and is comfortable working inside the platform to extract insight.

Head to Head

Direct Capability Mapping

Capability	Pangolin	Northbeam
1. Measurement Foundations
Marketing Mix Modelling	Core, Bayesian, MMM-first	Available (MMM+), layered alongside attribution
Multi-touch attribution	Not the primary method	Core method, with multiple models available
Incrementality framing	Built into the contribution model	Available via MMM+
Channel contribution	Core output	Available
2. Optimisation
Budget optimisation	AI-generated recommendations, core feature	Available via strategist-supported plans on higher tiers
Scenario planning	Built in	Limited/available on higher tiers
Saturation modelling	Core output	Available within MMM+
Autonomous execution	Yes (human-approved)	Not a core feature; Apex feeds signal back to ad platforms rather than executing budget changes
3. Commercial Measurement
Revenue optimisation	Yes	Yes
Contribution profit optimisation	Core feature	Not a primary focus
4. Product Experience & Audience Fit
Automation level	High - model to recommendation to approval	Moderate - platform provides data/insight; action is manual
Data requirements	Designed for growth-stage brands' existing data	Best suited to brands with meaningful spend and volume
Time to insight	Fast, continuous	Can take 30-60 days to reach full value, per reviews
Ease of use for non-analysts	Designed for marketing leaders directly	Assumes familiarity with attribution and MMM concepts

Foundational Beliefs

Two contrasting approaches to measurement

Approach A Northbeam: Attribution-first focus Assign credit across touchpoints using machine learningLayer in MMM+ for a broader view of historical mixReport performance details for the team to interpret and act on manually

Approach B Pangolin: Decision-first MMM Measure incrementality and contribution profit directly through Bayesian MMMModel saturation and marginal returns per channel continuouslyGenerate a specific budget recommendation and route it for human approval

The Pangolin Advantage

Where Pangolin is different

Automated Bayesian MMM, not a bolt-on MMM is Pangolin's foundation rather than an additional layer over an attribution model. This direct design handles seasonality, natural baselines, and complex marketing rhythms natively.

Profit, not just revenue Recommendations are optimised for contribution profit, accounting for margin. Pangolin focuses on scaling actual cash margins rather than chasing vanity ROAS numbers.

From insight to action, automatically Budget recommendations are generated directly by the model and move to human approval. No pivot tables or expert translation needed.

Built for growth-stage teams Designed to be useful without a dedicated media strategist or data analyst on staff. Easily operated directly by founders, marketing leads, or business owners.

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 Bayesian MMM as your core measurement method ✓You want budget recommendations generated automatically ✓Contribution profit needs to drive the decision ✓You don't have a dedicated data analyst to operate the platform daily

Consider Northbeam if: ○You want deep, self-serve multi-touch attribution ○You have an in-house analyst who wants to work inside the platform ○Feeding signal back into ad platform delivery (via Apex) is a priority ○Your team is already fluent in attribution and MMM concepts

Pangolin vs Northbeam FAQ

Pangolin vs Northbeam Comparison FAQ

How does Pangolin compare with Northbeam?

Both platforms aim to give DTC brands a clearer view of channel performance. Northbeam is attribution-first with MMM added as a layer; Pangolin is MMM-first with AI-generated budget recommendations built directly into the model.

Does Pangolin provide MMM?

Yes. Bayesian Marketing Mix Modelling is the core of Pangolin's measurement approach.

Which platform is better for DTC brands?

It depends on team structure. Brands with a dedicated analyst may prefer Northbeam's custom model environments. Brands wanting automated modelling and actionable recommendations tend to be a better fit for Pangolin.

What should brands consider when choosing between the two?

Consider whether you want a platform to operate manually (Northbeam) or an automated system that generates a specific, approvable recommendation for you (Pangolin).

Can I use both types of measurement together?

Some brands do run attribution and MMM in parallel during a transition period, though most eventually consolidate around one system to avoid conflicting signals.

See what your budget looks like modelled the Pangolin way

Compare your own channel data against a Bayesian MMM approach built for growth-stage DTC brands.

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