For Heads of Growth & Growth Managers

The last pound you spent isn't the problem. The next one might be.

Pangolin shows marginal return by channel, refreshed continuously, so you know where the next pound of budget works hardest before you commit it.

Traditional Average ROAS View Blended 4.2x Performance Hides channel saturation. You keep spending on top-performing platforms even after marginal returns drop below break-even.

Pangolin Marginal Return View Incremental Saturation Curve Pinpoint the exact tipping point where scaling ad spend yields zero net-new customer acquisitions.

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The Saturation Tipping Point

Stop defending reports that don't add up

Growth gets harder in a specific, predictable way: every additional pound of spend on a given channel tends to return less than the pound before it. The problem isn't knowing this in general - it's knowing exactly where each of your channels currently sits on that curve, and whether it's near the flat part or still climbing.

The Bayesian Methodology

Continuous calibration across every channel

01Decay & Adstock ModellingEnsure the model understands when spend actually influences buying patterns over several days, rather than forcing immediate attribution bounds.

02Platform De-duplicationDetermine the true incrementality of campaigns when Google Shopping and Meta Retargeting are simultaneously displaying assets to the same users.

03Scenario Planning SandboxRun simulated budget shifts safely. See predicted return curve impacts before reallocating actual marketing capital.

Engineered for Modern Growth

A More Responsive Signal

01Versus average ROASAn average blends the first pound spent with the last. Marginal return tells you about the next one specifically.

02Versus static assumptionsThe curve moves as your spend, creative, and market conditions change, so the read stays current rather than decaying.

03Versus quarterly waitContinuous updates mean the signal shows up as it happens, not once a quarter after budget has already been misallocated.

Impact Overview

What it means commercially

✓Know before you commit next month's budget whether a channel still has headroom.

✓Move spend into an underfunded channel before a saturated one keeps absorbing it.

✓Make the 'should we launch a new channel' decision with a specific number for what the current mix is leaving on the table.

Growth Clarity

Frequently Asked Questions

How does this differ from just watching ROAS trend down?

ROAS trending down often lags behind saturation - marginal return is a leading signal, not a lagging one.

Does this help with launching a brand new channel?

Yes - Pangolin's Bayesian approach is built to give a usable, honestly-uncertain read on newer channels with limited history, and sharpens as more data comes in.

Do I need a data science team to act on this?

No - you see marginal return and a recommended next move; the modelling runs in the background.

How much historical data do you need to give a reliable saturation read on a channel?

We recommend at least 12 months of daily spend and revenue history for a stable read. With less, the model will still show a curve, but the confidence bounds around it will be wider, and Pangolin makes that uncertainty visible rather than hiding it behind a single number.

What happens to the curve when creative or seasonality changes?

The read updates continuously as new spend and revenue data comes in, so a creative refresh or a seasonal shift moves the curve rather than leaving you working from a stale one.

Does this replace our weekly or monthly performance reporting?

No, it sits alongside your existing reporting as the marginal-return read specifically for budget allocation decisions, not a replacement for platform or BI-level performance reporting.

See what Pangolin would recommend for your media mix

Book a Pangolin demo - 30 minutes, on your own channel mix and numbers.