Most marketing decisions are made using averages (Average ROAS, CPA, etc.). Averages hide the most important truth in marketing economics: Returns are nonlinear.
The first dollars work extremely well. Mid-range dollars work okay. Later dollars quietly do nothing—or worse. Linear models lie by smoothing this reality away.
"Where does diminishing return begin for each channel?"
"Which channels still have headroom?"
"At what spend level does ROAS collapse?"
"Is performance dropping because of saturation or execution?"
"How much budget can we safely scale before returns flatten?"
A GAM doesn’t force spend to behave in a straight line. Instead, it:
Think of it as: “Let the data draw the curve.”
The Problem
Performance slowly erodes with scale.
What It Reveals
Exact inflection point where returns flatten.
Decision Enabled
Stop scaling before waste begins.
The Problem
Teams keep spending because ROAS is still “okay.”
What It Reveals
Spend ranges where incremental return ≈ zero.
Decision Enabled
Cut without fear.
The Problem
Teams either over-scale or under-scale.
What It Reveals
Where marginal ROI is still positive.
Decision Enabled
Scale with precision, not hope.
The Problem
Is performance dropping because of fatigue or saturation?
What It Reveals
Whether curve shape changed or just level.
Decision Enabled
Refresh creatives vs reallocate budget.
The Problem
ROAS comparisons ignore scale effects.
What It Reveals
True headroom across channels.
Decision Enabled
Rebalance spend toward higher marginal return.
SpendSignal uses GAMs as the mathematical engine behind spend elasticity.
Specifically:
This is what turns “cut Meta” into “Cut Meta after ₹X per day.”
Instead of “Facebook ROAS = 2.4x”, you see:
The decision insight: "Keep Facebook capped at ₹10–12L and move excess to YouTube."
No. GAMs balance flexibility with statistical discipline to avoid overfitting.
Yes—and they almost always do. Some flatten quickly (Display), others barely flatten (Brand Search).
It replaces blind trust in averages with marginal reality.