Bayesian MMM Comparison

Pangolin vs Rockerbox

Rockerbox and Pangolin both combine attribution and Marketing Mix Modelling, but they're built for different scales and different levels of automation. Rockerbox is an enterprise measurement platform built for complex, omnichannel media mixes including offline. Pangolin is built for growth-stage DTC brands who want the model to generate the budget decision, not just the data behind it.

ROCKERBOX: ENTERPRISE INFRASTRUCTURE Multi-method platform spanning MTA, MMM, and geo-testing. Highly robust for large diversified budgets, but requires analyst oversight and manual allocation planning. Omnichannel & Offline MTA

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PANGOLIN: DECISION-FIRST AUTOMATION Continuous Bayesian MMM centered around contribution profit and automated action loop recommendations. Instantly gives growth-stage DTC teams cash-optimized spend answers. Continuous Bayesian MMM

See how Pangolin compares using your own data

At a Glance

Who's each platform for?

Pangolin is best suited for: AUTOMATED DECISIONS & GROWTH BUDGETS Growth-stage DTC and ecommerce brands running primarily digital channels, who want automated Bayesian MMM and AI-generated budget recommendations without an enterprise contract or a dedicated analytics team.

Rockerbox is best suited for: OMNICHANNEL PORTFOLIOS & OFFLINE SPEND Enterprise brands running a genuinely diversified media mix, including TV, podcasts, direct mail or other offline channels alongside digital, with the analytics resources to run and act on multi-touch attribution, MMM and incrementality testing directly.

Head to Head

Direct Capability Mapping

Capability	Pangolin	Rockerbox
1. Measurement & Data Integration
Marketing Mix Modelling	Core, Bayesian, MMM-first	Available as one of three core methods
Multi-touch attribution	Not the primary method	Core method
Incrementality testing	Built into the contribution model	Native, geo-based experimentation capability
Offline/TV/CTV measurement	Not a current focus	Core strength, purpose-built for omnichannel including offline
2. Budget & Optimization
Budget optimisation	AI-generated recommendations, core feature	Available, typically interpreted and actioned by the brand's own team
Scenario planning	Built in	Available
Saturation modelling	Core output	Available via MMM module
Autonomous execution	Yes (with human approval)	Not a core feature
3. Commercial Measurement
Revenue optimisation	Yes	Yes
Contribution profit optimisation	Core feature	Not a primary focus
4. Product Experience & Onboarding
Automation level	High - model to recommendation to approval	Moderate - platforms provide multi-method measurement; action is largely manual
Data requirements	Designed for growth-stage brands' existing digital data	Best suited to brands with significant, diversified ad spend and data volume
Pricing model	Positioned for growth-stage budgets	Enterprise, custom, scaling with spend and data volume
Ease of use for non-analysts	Designed for marketing leaders directly	Requires analytics resource to operate day to day

Foundational Beliefs

Two contrasting approaches to measurement

APPROACH A: ROCKERBOX Enterprise measurement infrastructure Combine MTA, MMM and incrementality testing into one platformCover digital and offline channels, including TV and direct mailProvide the measurement infrastructure for the brand's own team to interpret and act on

APPROACH B: PANGOLIN Automated, decision-first MMM Model incrementality and contribution profit directly through Bayesian MMMFocus on the digital channel mix most growth-stage DTC brands actually runGenerate a specific budget recommendation and route it for human approval

The Pangolin Advantage

Where Pangolin is different

Built for growth-stage budgets Rockerbox is priced and positioned for brands with significant, diversified ad spend; Pangolin is built to be accessible without an enterprise sales process or a large analytics team.

Automated from model to decision Rather than providing measurement infrastructure for your team to interpret, Pangolin generates a specific, approvable budget recommendation directly from the model.

Profit, not just attribution volume Recommendations are optimised for contribution profit, not simply channel-level attribution or top-line revenue calculations.

Simpler by design Rockerbox's strength in covering complex omnichannel offline media is valuable, but it brings more measurement methods and complex metrics to manage than digital-first brands need.

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: ✓Your media mix is primarily digital (Meta, Google, TikTok and similar channels) ✓You want an automated system that generates the budget recommendation, not just the measurement ✓You want to avoid an enterprise sales process and custom pricing tied to spend volume ✓Contribution profit needs to drive the decision, not just channel attribution

Consider Rockerbox if: ○You run a genuinely diversified media mix including TV, podcasts or direct mail ○You have the analytics resources to run multi-touch attribution, MMM and incrementality testing directly ○You're an enterprise brand for whom custom, spend-based pricing is not a barrier ○Native, geo-based incrementality testing is a specific requirement

Pangolin vs Rockerbox FAQ

Pangolin vs Rockerbox Comparison FAQ

How does Pangolin compare with Rockerbox?

Rockerbox is an enterprise measurement platform built for complex, omnichannel media mixes including offline, using MTA, MMM and incrementality testing together. Pangolin is built for growth-stage DTC brands running primarily digital channels, with automated Bayesian MMM and AI-generated budget recommendations.

Does Pangolin provide MMM?

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

Does Pangolin handle TV or offline channels?

Not currently. Rockerbox's strength in offline and TV measurement is a genuine differentiator for brands that need it.

What should brands consider when choosing between the two?

Consider your media mix (digital-only versus genuinely omnichannel), whether you have in-house analytics resource to operate a measurement platform directly, and whether you want a system that generates the budget decision automatically or one that provides the infrastructure for your team to build that decision themselves.

Does Pangolin replace our existing analytics stack, or sit alongside it?

Pangolin sits alongside your existing tools. We don't ask you to rip out GA4, your ad platform dashboards, or your CRM. We ingest data from them and turn it into a single incremental-revenue view your team can act on.

How long until we see our first output?

Once your data sources are connected, initial model outputs are typically available within days, not the 3 to 6 months a traditional MMM consultancy takes. Full confidence intervals stabilise over the following few weekly refreshes as the model sees more data.

Enterprise-grade modelling, without the enterprise overhead

See what automated Bayesian MMM looks like for your own channel mix.

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