Most marketing forecasts report one number: Expected revenue. That number is usually the average.
But averages are dangerous because they hide downside risk, overstate certainty, and fail leaders when conditions change. Executives don’t make decisions on averages. They make them on risk boundaries.
"What’s the worst-case outcome of this plan?"
"How bad could ROAS get if demand softens?"
"What does success look like at the 90th percentile?"
"Which channels are fragile vs resilient?"
"How asymmetric is the risk?"
Traditional regression answers: “On average, what happens?”
Quantile Regression answers: “What happens at different points of the outcome distribution?”
Instead of one curve, you get many: Lower quantiles (downside risk), Median (typical outcome), and Upper quantiles (upside potential). Think of it as: “Mapping the terrain, not just the midpoint.”
The Problem
Budgets are planned assuming normal conditions.
What It Reveals
Revenue and ROAS floors.
Decision Enabled
Protect cash before it’s threatened.
The Problem
Some channels collapse faster under stress.
What It Reveals
Which channels degrade sharply in bad scenarios.
Decision Enabled
Avoid over-exposure to fragile spend.
The Problem
Forecasts lack credibility under scrutiny.
What It Reveals
Explicit best-, base-, and worst-case bands.
Decision Enabled
Make defensible commitments.
The Problem
Some channels have limited downside but big upside.
What It Reveals
Favorable risk-reward profiles.
Decision Enabled
Invest where upside outweighs risk.
The Problem
“What-if” scenarios feel speculative.
What It Reveals
Probabilistic bounds, not guesses.
Decision Enabled
Choose strategies aligned with risk appetite.
SpendSignal uses Quantile Regression to add a risk dimension to every recommendation.
Specifically:
This prevents the classic failure mode: “The plan worked… except when it mattered.”
Instead of “Expected ROAS: 2.8x”, you see:
The decision insight: "This channel has limited downside and meaningful upside."
It’s forecasting *with risk awareness*, not point estimates.
No. It complements them by modeling outcome asymmetry.
Only if risk doesn’t matter—which it always does.