Every platform claims credit for the same sale. Your budget still has to decide where the next £ goes.
Pangolin gives you an independent view of channel contribution across the full mix, and turns it into budget recommendations you can defend to the board.
A different methodology for a different question
Pangolin is not another attribution dashboard. It is a mathematical model designed to answer the budget question, not the click question.
MTA tracks clicks and cookies, the exact mechanism that causes platforms to double-count. Pangolin measures incremental revenue instead, a different question entirely.
Each platform is graded on its own self-reported numbers. Pangolin is the independent read that doesn't have a stake in which channel looks good.
A deck delivered 3-6 months after the fact can't inform this month's budget conversation. This updates continuously.
Board-Ready Recommendations
Stop defending reports that don't add up
Sum the revenue metrics from your active ad platforms, and it will comfortably surpass the net sales processed by your bank. Pangolin provides real, mathematically valid evidence to back up your budget shifts.
The commercial outcomes of a modelled answer
Pangolin gives you a single source of truth that finance, agencies, and leadership can all align on. It is the evidence layer that turns attribution into budget decisions.
Frequently Asked Questions
Does this replace our active marketing agencies?
No. It provides you and your agencies with a clear, unbiased dataset to optimize performance against. Take back strategic leverage with robust math.
How quickly can I see initial insights?
Our onboarding maps direct APIs rapidly. Initial model structures and historical lift baselines are calculated in under 14 working days.
Can I run 'what if' scenarios?
Yes. Our virtual sandbox tracks saturation margins dynamically. Drag the slider to predict channel performance drop-offs at higher scale.
How does this help me defend a budget to the board?
Every recommendation comes with the transactional data and confidence bounds behind it, rather than a single point estimate. You're presenting a modelled range backed by evidence, not a platform's self-reported number that a board member can challenge with a different dashboard.
We already have a multi-touch attribution (MTA) tool. Do we need this as well?
MTA tracks individual user journeys through clicks and cookies, which is exactly the mechanism that causes platforms to double-count the same sale. Pangolin models incremental revenue from spend and transactional data instead, so it answers a different question, true net lift rather than who touched the customer last, and the two are typically run alongside each other rather than one replacing the other.
Build a marketing plan you can defend
Free consultation for brands spending >500k / month.