MMM that updates every week, not every year

Continuous Bayesian Marketing Mix Modelling engineered specifically for high-growth DTC brands. Turn fragmented attribution data into automated weekly budget allocation recommendations.

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Channel Revenue Contribution (Weekly Attribution)

Meta Ads £142,500

Google Shopping £95,200

TikTok Branding £51,000

Baseline (Organic) £50,800

98.2% R² Predictive Power +/- 4.1% Mean Absolute Error

The broken status quo

The problem with traditional MMM

Classic marketing mix models were built for enterprise consumer brands with TV budgets. For digital-first DTC brands, they are completely broken.

01 Takes 3 to 6 months to build By the time consultancy teams compile your spreadsheets and clean the data, the market has changed, your creatives have decayed, and the model is useless. 3-6 months is too long to get up and running.

02 Decisions based on stale data Relying on quarterly or annual updates forces marketing leads to fly blind on weekly budget updates. You're driving forward by staring in the rearview mirror.

03 Always ends in a slide deck Currently you receive a static, 80-page PDF report with historical observations. We provide dynamic, actionable software that integrates with your tech stack, delivers future plans, then actions.

How it works

Pangolin's Continuous Pipeline

Our Bayesian engine processes multi-channel adspend, baseline variables, and sales data in real time to generate accurate budget suggestions weekly.

1. Data Ingestion & Sync Automated APIs compile daily ad spend, baseline seasonality factors, and shopify revenue.

2. Model Bayesian Engine Applies prior probability distributions dynamically, accounting for decay, adstock and delay.

3. Contribution Attribution Output Disentangles organic baseline sales from incremental revenue driven by marketing channels.

4. Scenario Simulation Workspace Run simulated risk profiles and budget shifts in our virtual workspace with direct outcomes.

5. Recommend Marginal ROAS Finds the exact saturation curve points where next-dollar investments maximize efficiency.

6. Execution Active Deployment Direct integration updates in-platform budgets safely without manual dashboard migration.

The alternative paradigm

Why this is different

Our approach replaces standard broken attribution templates and massive human-guided consultancy costs.

Platform Attribution The Double-Counting Trap

Meta and Google both claim 100% of the same sale. Multi-touch attribution fails completely in a post-iOS14 world because it tracks cookies, not math.

✗ Double-counts the same sale across Meta and Google ✗ Measures clicks and views, not incremental revenue ✗ Relies on broken data tracking policies

Traditional MMM Too Expensive, Too Slow to Matter

Annual consultancies deliver retrospective insights. Great for analyzing last year's performance, useless for scaling tomorrow morning's campaigns.

✗ Takes 3 to 6 months to build and deliver ✗ Ends in a static slide deck, not a working system ✗ Months of lag and high consultancy fees

Pangolin Continuous MMM Precision & Speed Combined

Automated pipelines deliver new outputs weekly. True incremental contribution tracking with 95% Bayesian credible intervals built into software.

✓ Incremental contribution output weekly ✓ Recommendations flow straight into budget execution ✓ No user-level cookies or pixel tracking

Commercial outcomes

What it means commercially

By modeling incremental revenue objectively, Pangolin aligns the marketing team to financial realities.

Identify Saturating Channels Identify exactly where Meta and Google stop returning incremental revenue. Maximize budgets to marginal efficiency, stopping overallocation.

Always-on Real-time Model No more multi-month modeling downtime. Bayesian parameters update weekly, letting you adapt rapidly as customer acquisition costs fluctuate.

Rigorous Confidence Ranges Every recommendation includes transparent risk ranges. See the minimum and maximum expected outcomes at 95% credibility levels.

Interactive What-If Scenarios Simulate scaling spend by 50% or testing new channels like TV or influencers before committing any real dollar budget.

Math over guesswork

Continuous Bayesian Validation

Our models don't just produce points on a chart. We continuously validate predictions against actual transactional data to establish high-confidence limits.

95% Credible Interval Coverage

Incremental ROAS Credibility Curve

Low ROAS Limit (1.2x) Median Estimate (2.4x) High ROAS Limit (3.6x)

Ready to grow your profits with math?

Join the world's most analytical DTC brands and turn your marketing department into a highly optimized revenue machine.

Free consultation for brands spending >500k / month.