Most teams notice problems after they’ve settled in. The real issue is that the moment of change is lost in noise, leading to post-hoc explanations and late fixes.
Without timing clarity, every diagnosis is suspect.
"When did performance materially change?"
"Was this shift sudden or gradual?"
"Did the change align with a campaign, budget move, or external event?"
"Are we reacting to noise or a real structural break?"
"Which metric changed first?"
Change Point Detection scans time series for Sudden shifts in level, Changes in trend slope, and Variance explosions.
It doesn’t assume everything evolves smoothly. It asks: “Did the system behave differently after this point?”
Think of it as: “Finding the crack in the dam—not the flood.”
The Problem
Performance erodes quietly.
What It Reveals
Exact moment response changed.
Decision Enabled
Refresh creatives before scaling waste.
The Problem
Performance shifts without explanation.
What It Reveals
Structural break aligned with platform updates.
Decision Enabled
Adjust strategy instead of blaming execution.
The Problem
Budget increases don’t clearly show effect.
What It Reveals
Whether spend changes altered performance regime.
Decision Enabled
Keep or revert allocation confidently.
The Problem
External events distort metrics.
What It Reveals
Timing of market-driven vs campaign-driven shifts.
Decision Enabled
Avoid misattributing blame or credit.
The Problem
Teams react only after KPIs crater.
What It Reveals
Leading change points before full impact.
Decision Enabled
Intervene while options still exist.
SpendSignal uses Change Point Detection as a diagnostic trigger layer.
Specifically:
This ensures SpendSignal doesn’t just explain outcomes—it timestamps causes.
Instead of “ROAS declined over Q2”, you see:
The decision insight: "The problem started before we noticed—and here’s why."
No. It detects structural change, not random outliers.
Yes—and that’s usually the truth.
It tells you *when*. Other models explain *why*.