Bayesian MMM Comparison

Pangolin vs Sellforte

Sellforte and Pangolin are both built around Bayesian Marketing Mix Modelling with AI-driven budget recommendations and human-approved execution - which makes this a closer comparison than most. The meaningful differences are in optimisation target, granularity and which segment of DTC and ecommerce brands each platform is built for.

SELLFORTE: ENTERPRISE GRANULARITY Bayesian MMM at campaign and ad-set level calibrated with experiments. Highly specialized for large multi-market operations with dedicated Media Agents. Multi-market miROAS

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PANGOLIN: PROFIT-FIRST FOCUS Continuous Bayesian MMM centered entirely around contribution profit saturation and automated cash optimization recommendations for growth DTC. Contribution Profit MMM

See how Pangolin compares using your own data

At a Glance

Who's each platform for?

Pangolin is best suited for: AUTOMATED DECISIONS & PROFIT OPTIMIZATION Growth-stage DTC and ecommerce brands that want automated Bayesian MMM optimised specifically for contribution profit, without the calibration and campaign/ad-set complexity built for large, multi-market retail operations.

Sellforte is best suited for: ENTERPRISE MULTI-MARKET CALIBRATION Enterprise and mid-market ecommerce, DTC and retail brands, particularly those operating across multiple markets or channels including offline, who want campaign and ad-set-level miROAS (marginal incremental ROAS) optimisation calibrated with incrementality experiments.

Head to Head

Direct Capability Mapping

Capability	Pangolin	Sellforte
1. Measurement & Data Integration
Marketing Mix Modelling	Core, Bayesian	Core, Bayesian, calibrated with geo and conversion lift tests
Granularity	Channel-level contribution and saturation	Campaign and ad-set level (miROAS)
Multi-market modelling	Not a current focus	Core strength - separate models per market at consistent methodology
Offline media measurement	Not a current focus	Available on Enterprise plans
2. Budget & Optimization
Optimisation target	Contribution profit	Marginal incremental ROAS (miROAS)
Budget optimisation	AI-generated recommendations, core feature	AI-generated recommendations via Media Planner Agent
Autonomous execution (human-approved)	Yes	Yes, via Media Buyer Agent with configurable guardrails
Experiment design	Not a current focus	Automated via Experiments Agent
3. Commercial Measurement
Revenue optimisation	Yes	Yes
Contribution profit optimisation	Core feature	Available on Enterprise plans as a secondary target KPI
4. Product Experience & Onboarding
Target segment	Growth-stage DTC and ecommerce brands	Enterprise and mid-market ecommerce, DTC and retail, including $1bn+ revenue businesses
Plan structure	Single, growth-stage-focused offering	Tiered by business type (eCommerce, DTC, Retail) and scale (Standard/Enterprise)
Pricing basis	Positioned for growth-stage budgets	Scales with media spend under management

Foundational Beliefs

Two contrasting approaches to measurement

APPROACH A: SELLFORTE Enterprise-grade granularity Model Bayesian MMM at campaign and ad-set level, calibrated with geo and conversion lift experimentsOptimise for marginal incremental ROAS across markets and channelsExecute approved bid and budget changes directly via AI agents, with configurable guardrails

APPROACH B: PANGOLIN Focused, profit-first MMM Model incremental contribution and saturation through Bayesian MMM at the channel levelFocus 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

Optimised for profit, not just incremental ROAS Sellforte's miROAS is a genuine advance, but it's still a revenue-based metric. Pangolin's recommendations optimise contribution profit directly, accounting for margin.

Built for growth-stage brands specifically Sellforte frames itself against lighter 'SMB' tools and fits enterprise calibration. Pangolin is purpose-built for growth DTC segments those enterprise tools bypass.

Simpler by design Ad-set granularity and multi-market models are powerful, but they bring massive configuration complexity that single-market, digital-first DTC brands do not 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: ✓You're a growth-stage DTC or ecommerce brand operating primarily in one market ✓Contribution profit, not incremental ROAS, is the number that should drive your budget ✓You want a focused platform without campaign/ad-set-level configuration overhead ✓Your media mix is primarily digital, without a significant offline component

Consider Sellforte if: ○You operate across multiple markets or a genuinely omnichannel mix including offline media ○Campaign and ad-set-level bid optimisation is a requirement, not just channel-level budget guidance ○You want your MMM calibrated directly against geo and conversion lift experiments ○You're operating at enterprise scale, where Sellforte's Enterprise plan features are built to add value

Pangolin vs Sellforte FAQ

Pangolin vs Sellforte Comparison FAQ

How does Pangolin compare with Sellforte?

Both platforms use Bayesian MMM with AI-generated recommendations and human-approved execution, making them methodologically similar. The main differences are optimisation target (Pangolin: contribution profit; Sellforte: marginal incremental ROAS), granularity (channel-level versus campaign/ad-set level), and target segment (growth-stage DTC versus enterprise and multi-market retail).

Does Pangolin offer campaign and ad-set-level optimisation like Sellforte?

Pangolin's current focus is channel-level contribution and saturation modelling. Brands needing campaign or ad-set-level bid recommendations specifically should evaluate Sellforte's granularity against their requirements.

Which platform is better for DTC brands?

Growth-stage, single-market DTC brands wanting a focused, profit-optimised platform tend to fit Pangolin. DTC brands operating at larger scale, across multiple markets, or with a genuinely omnichannel mix may be better served by Sellforte's Enterprise or DTC plan tiers.

Does Sellforte's calibration against experiments make it more accurate than Pangolin?

Calibrating a model against geo and conversion lift experiments is a genuine way to strengthen confidence in its outputs. Whether that added rigour is necessary depends on the complexity of your media mix and how much your team can act on the additional granularity it produces.

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.

Built specifically for growth-stage DTC brands optimising for profit

See what a focused, profit-first Bayesian MMM approach shows for your own channel mix.

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