Synapse · Signature Methodology

Causal Impact Modeling

Measurement that makes Brand accountable to the business: continuous, causal and privacy-safe.

What it is

Measure the impact, not just the path.

CIM is Synapse’s machine-learning measurement methodology. It quantifies the incremental impact of media: the sales and actions advertising actually caused, not simply touched.

It creates one causal read across every channel, campaign, creative and audience in the total media portfolio.

No PII, tags or cookies

Privacy-safe measurement with nothing at the user level and nothing to deprecate.

Always on

Refreshed with the campaign, not the quarter, so learning can shape in-flight decisions.

Total portfolio

Paid, owned and earned media evaluated together, rather than in isolated platforms.

Decision-level depth

Granular reads down to creative, geography and audience, where teams can act.

Why now

Granular enough to act. Causal enough to trust.

Mix modeling (MMM)

Directionally useful, but often too slow and too broad for in-flight digital decisions.

Multi-touch attribution (MTA)

Fast and detailed, but built on cookie and device signals that privacy changes have dismantled.

Causal Impact Modeling

Keeps the granularity, restores causality and requires none of the user-level tracking.

How it works

From aggregated data to confident action.

01

Ingest

Aggregated media, site and business data flows in. Nothing at the user level.

02

Model

The B-SUR engine scores what every part of the media plan drives, alone and in combination.

03

Act

Incrementality reads feed budget shifts, forecasts and scenario plans at every decision level.

Inside the engine

B-SUR: a proprietary blend of two proven statistical techniques.

Seemingly Unrelated Regressions

Every customer response gets its own model, with all responses solved together rather than one at a time. This captures how the same media works across outcomes and catches credit a single model can misplace.

Hierarchical Bayesian

The model starts from common-sense cause and effect. Only the direction is assumed, never the size. Real data determines the magnitude, so outputs make marketing sense before anyone acts on them.

Carryover effects

Saturation

Cross-channel synergies

Share of voice

What you get

One consistent read from investment to outcome.

  • What each channel and tactic truly contributed, and how they work together.
  • Response curves that show where the next dollar works hardest.
  • Direct comparisons against what media platforms report.
  • One consistent read across every response KPI, from engagement through conversion.

Non-linear response curve

Non-linear response curve Response follows an S-curve. Linear assumptions overestimate returns at scale. Spend Response
Causal response (CIM) Linear assumption

Response follows an S-curve. Linear assumptions overestimate returns at scale.

Proof in the numbers

Same budget. Smarter mix. Better results.

On average, across active programs:

+18%

conversion lift at a flat budget, from rebalancing the media mix.

35%

of lower-funnel search spend identified as non-incremental and freed for reallocation.

40%

gap between platform-reported performance and true causal impact, in both directions.

Inside Synapse Insight Studio

Causal intelligence, connected to action.

CIM is available as a module inside Generator’s AI-native intelligence platform, with AI-assisted insights and recommended actions.

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