Overview
To ensure your MNTN campaigns can be reliably measured through Marketing Mix Models (MMM) and incrementality testing, they should be structured with measurement in mind.
These best practices help generate cleaner signals, stronger lift detection, and more actionable insights.
Why it matters
Why it matters
Measurement-friendly campaign design helps you:
Generate cleaner signals for MMM and incrementality analysis
Improve lift detectability across test and control groups
Reduce noise caused by overlapping variables or unstable delivery
Capture delayed conversion behavior from Connected TV (CTV) exposure
Produce more actionable insights for future optimization
Run Campaigns Long Enough to Build a Strong Signal
Prioritize longer, continuous campaign flights instead of short bursts.
Short campaigns often fail to generate enough measurable signal for reliable analysis. MMM relies on time-series variation to estimate impact over time. Incrementality experiments also require enough exposure time to observe causal lift and post-exposure behavior.
How to:
Run tests for at least 6–8 weeks, when possible.
Include a Post-Treatment Observation Window
Plan for a post-campaign observation window before reallocating budgets or launching major replacement campaigns.
CTV impact is not always immediate. Customers may convert days or weeks after ad exposure. Without a post-treatment observation period, MMM and incrementality models may under-credit campaign performance.
How to:
Include 2–4 weeks of post-treatment data collection.
Avoid replacing MNTN with another major channel immediately after a test.
Concentrate Spend for Stronger Signal Detection
Favor fewer, higher-intensity test markets over broad, low-volume distribution.
Measurement models rely on detectable variation across markets and time periods. Spreading budgets too thin across too many regions can reduce signal strength and make lift harder to measure. It’s usually better to test boldly in fewer regions than quietly across many.
How to:
Use strongly correlated test and control market pairs.
Increase spend meaningfully in test regions, typically by 30–50% or more.
Maintain Stable Test Conditions
Keep business-as-usual conditions as stable as possible during active tests.
Clean incrementality experiments require isolating the impact of MNTN. Major changes to pricing, creative, or channels can introduce noise and reduce measurement reliability.
How to:
Avoid major creative refreshes mid-flight.
Minimize pricing or promotional changes during testing windows.
Avoid launching large overlapping campaigns that could influence results.
Document unavoidable changes for your modeling teams.
Align on Measurement Goals Early
Before launch, align on whether the campaign’s primary goal is optimization, MMM analysis, or incrementality testing.
Campaigns and audiences that are optimized for short-term performance may not always be structured for downstream measurement. Clear, early goal alignment helps prevent mid-flight changes that can weaken measurement quality.