Most dashboards assume that Spend today → Revenue today (or tomorrow). If nothing converts quickly, the channel “didn’t work”.
That assumption quietly destroys Brand spend, Content marketing, Influencer campaigns, and B2B demand generation. In reality, marketing impact is spread over time. Ignoring lag leads to premature cuts and over-investment in short-term channels.
"How long does it take for this channel to pay back?"
"Which channels have delayed but durable impact?"
"When should we expect revenue after spend increases?"
"Are we cutting channels before they mature?"
"How much future revenue is already “in motion”?"
A Distributed Lag Model treats spend as a signal that echoes forward in time.
Instead of asking “What revenue happened on the same day as spend?”, it asks: “How much of today’s revenue was caused by spend from the past X days or weeks?”
The model assigns weights across time lags, learns decay curves per channel, and separates fast-response from slow-burn impact. Think of it as: “Revenue has memory.”
The Problem
Brand spend doesn’t convert immediately.
What It Reveals
Delayed lift over weeks or months.
Decision Enabled
Defend brand budgets with evidence, not belief.
The Problem
Content looks unprofitable early.
What It Reveals
Long-tailed revenue accumulation.
Decision Enabled
Invest patiently instead of cycling strategies.
The Problem
Leads convert months later, attribution breaks.
What It Reveals
Lagged influence of early touchpoints.
Decision Enabled
Fund demand gen without waiting for demos.
The Problem
Spend cuts don’t show immediate revenue impact.
What It Reveals
Future revenue already committed by past spend.
Decision Enabled
Avoid overcorrecting during short-term dips.
The Problem
ROAS ignores how fast money comes back.
What It Reveals
Payback curves, not just totals.
Decision Enabled
Balance fast-return and slow-return channels.
SpendSignal uses lag models as a temporal intelligence layer across the platform.
Specifically:
This prevents misclassification of “Slow” channels as bad, or “Fast” channels as scalable forever.
Instead of “This channel has low ROAS this month”, you see a Payback Curve:
The decision insight: "Cutting this now kills revenue two months from today."
No. Attribution windows are rules (e.g., 28 days). Lag models are learned from data, discovering the true shape of the delay.
No. It works on aggregated time series, allowing you to measure delayed impact even in privacy-restricted environments.
Yes—and they usually do. That’s the point. TV might lag 8 weeks, while Search lags 2 hours.