Most performance analysis assumes that if revenue went up after a campaign, the campaign worked, and if it went down, something broke.
Reality is messier: Seasonality shifts, market demand changes, and external events distort results. Traditional dashboards confuse timing with causality. This model fixes that by mathematically proving what the baseline would have been.
"Did this campaign actually cause incremental revenue?"
"What would revenue have been without this spend?"
"How confident can we be in this uplift?"
"Was the impact temporary or sustained?"
BSTS builds a synthetic version of reality where the campaign never happened.
It does this by:
The difference between Actual Revenue and Predicted No-Campaign Revenue is your Causal Impact.
The Problem
You can’t turn campaigns on/off cleanly.
What It Reveals
What revenue would have occurred anyway.
Decision Enabled
Continue, pause, or scale with confidence.
The Problem
Brand campaigns don’t convert immediately.
What It Reveals
Delayed and diffuse impact over time.
Decision Enabled
Justify long-term brand spend.
The Problem
Retrospective analysis is biased by timing.
What It Reveals
Whether uplift exceeded expected baseline.
Decision Enabled
Replicate or kill similar campaigns.
The Problem
Market events distort performance signals.
What It Reveals
Structural vs campaign-driven change.
Decision Enabled
Avoid false positives or panic cuts.
SpendSignal uses BSTS as a causal validation layer, not a standalone report.
Specifically, it powers:
It strengthens trust by quantifying uncertainty alongside impact.
You see a baseline revenue curve (the "no marketing" world), the actual observed revenue, and a shaded confidence interval.
The key takeaway isn't just the chart, it's the probability statement: "There’s an 87% probability this campaign created $1.2M in incremental revenue."
It answers a different question: causality, not credit. Attribution tells you where to assign value; BSTS tells you if value was created at all.
No. It works on aggregated time series, making it privacy-safe and platform-agnostic.
Outputs include explicit probability bands, not misleading point estimates.