Validation Layer

Trust your model because you tested it — not because someone told you to

Your MMM makes confident recommendations. But confident isn't the same as correct. Prescient's Validation Layer lets you pressure-test model configurations against various data inputs — so you can choose the one that actually predicts your business.

Book a demo 30-min call · No commitment
200+ brands
Side-by-side model comparison
Evidence over assumptions

CHOOSE YOUR VALIDATION METHODS

Incrementality tests

Geo-lift & holdout data

Post-purchase surveys

Customer-reported attribution

MTA & platform data

Multi-touch attribution signals

DEFAULT MODEL Baseline
Backtest accuracy 87.2%
MAPE 12.8%
CALIBRATED MODEL + Incrementality data
Backtest accuracy 93.6%
MAPE 6.4%

The status quo

Your MMM gives you confident recommendations. You have no way to know if they're right.

Your model says to shift $200K from YouTube to Facebook. You've got incrementality tests saying something different. Post-purchase surveys tell a third story. Your MTA platform disagrees with all of them. Too many numbers, no clear answer — so decisions grind to a halt and budget stays exactly where it is.

The result?

Paralysis

Conflicting data sources create standoffs — nobody wants to make the call

Blind faith

You trust the model's output because you can't see under the hood to question it

Real dollars

Wrong measurement means wrong budget decisions — channels get overfunded or misallocated

The paradigm shift

What if you could test your measurement before betting your budget on it?

Prescient's Validation Layer lets you configure model assumptions, calibrate with your own data, and compare accuracy across different model configurations — side by side. You choose the model you believe best predicts your reality. Not because your vendor told you to trust them.

How it works

Test, compare, choose

1

Start with your baseline model

Your existing Prescient model is already validated through backtesting, cross-validation, and scenario testing before you ever see it. But you may have additional data that could make it even more accurate for your specific business.

  • Backtested against multiple historical periods
  • Cross-validated for stability across time periods
  • Scenario-tested to confirm recommendations make business sense
BASELINE MODEL Default
Q1 prediction 91%
Q2 prediction 88%
Q3 prediction 86%
Q4 prediction 84%
Average accuracy 87.3%
CALIBRATION SOURCES

Incrementality test results

Meta geo-lift, Q3 2025

Active

Post-purchase survey data

Last 90 days

Active

MTA platform data

Not connected

Custom assumptions

Seasonality, promotions

2

Calibrate with your own data

Bring the data you've been collecting — incrementality tests, post-purchase surveys, MTA platform results, custom business assumptions. Configure alternative models that incorporate these sources. See what changes when you weight your model toward different assumptions.

  • Incrementality & geo-lift test results
  • Post-purchase survey insights
  • MTA and platform attribution data
  • Custom seasonality and promotion assumptions
3

Compare accuracy side by side

This is where it gets interesting. Run parallel models against your historical performance and see which configuration actually predicts your reality best. Not guessing which data source to trust — seeing which one measurably improves your performance.

  • Parallel model comparison with concrete accuracy scores
  • See if calibration helped or hurt — before you act on results
  • Guardrails prevent configurations that would break model health
MODEL COMPARISON
Metric Default Calibrated
Accuracy 87.2% 93.6%
MAPE 12.8% 6.4%
Stability Good Strong
Verdict — Recommended
MODEL SELECTED
Calibrated + Incrementality

93.6%

backtest accuracy score

Meta Ads contribution +7.2% vs default
Google Ads contribution -3.1% vs default
TikTok contribution +12.8% vs default
4

Choose based on evidence and act with confidence

Select the model that best predicts your business. Then use it to track performance, forecast outcomes, and allocate budget — knowing the measurement foundation under your decisions was pressure-tested against reality, not assumed to be right.

  • Take bigger swings knowing the model actually captures your reality
  • End standoffs between conflicting data sources
  • Get finance to trust the numbers — because you can show them the receipts

The calibration paradox

More data doesn't always mean better accuracy

Most marketing teams assume that incorporating incrementality tests, surveys, or other data sources will automatically improve their model. Prescient's research tells a different story: sometimes that data degrades model accuracy instead of improving it.

Test conditions don't always reflect real marketing environments. Survey responses carry self-report bias. Platform attribution has its own incentives. Without testing whether each source actually improves prediction accuracy, you're calibrating on hope.

WITHOUT VALIDATION LAYER

You invest in an incrementality test, calibrate your model with the data, and assume accuracy improved. You shift budget based on new recommendations. Three months later, performance is worse and you don't know why.

WITH VALIDATION LAYER

You invest in an incrementality test, calibrate a parallel model with the data, and compare accuracy scores against your baseline. You see that this particular test actually reduced prediction accuracy by 4%. You keep the baseline model and save your budget from a bad recommendation.

Social proof

Trusted by 200+ brands making data-driven decisions

200+

Brands validated

Daily

Model updates

$2.2B+

Ad spend measured

93%+

Average backtest accuracy

“We trust Prescient implicitly with our media strategy. Their insights into measurement and optimization are invaluable, and their dedication to learning our business ensures that every recommendation helps us scale effectively at the top of the funnel.”

Taylor Hastings
Taylor Hastings
Director of Omnichannel Marketing, WSS

“With Prescient's guidance, we implemented MMM to explore and expand into new TOF channels, including podcasts. The daily insights at the campaign level gave us confidence to scale effectively and achieve measurable growth.”

Ian Blair
Ian Blair
CEO, Laundry Sauce

“The support we get from Prescient goes above and beyond. They're not only a resource but a strategic partner who genuinely cares about our success, helping us integrate MMM insights into our workflow smoothly.”

Omar Yassin
Omar Yassin
Head of Data Science, Jack Archer

“Our founder was blown away when I walked him through the platform… It's amazing. So so useful.”

Hans P. Harris
Hans P. Harris
Director of Growth, BrüMate

“There is no other tool out there that can help me validate TV.”

Alex Diesbach
Alex Diesbach
VP of Digital Marketing, Saatva
Book a demo Read all case studies

Why Prescient

Not all validation is created equal

Typical MMM vendor In-house / open-source
Side-by-side model comparison Manual effort
Calibration with external data Sometimes
Accuracy scores you can see Requires DS team
Guardrails on model health
Daily model updates Monthly / quarterly Depends on team
Requires data science team No No Yes
See Validation Layer in action

FAQ

Common questions

Model validation tests whether your MMM's predictions actually match real business outcomes. It includes backtesting against historical data, cross-validation across time periods, holdout testing on unseen data, and scenario testing to confirm recommendations make business sense. The goal is to prove the model works before you trust it with real decisions.

Calibration adjusts your model's parameters using external data like incrementality tests or surveys to improve alignment with known performance. Validation tests whether those adjustments actually improved accuracy by comparing predictions to real outcomes. Think of calibration as tuning the instrument and validation as checking if it plays the right notes. You need both — calibration without validation is tuning based on hope.

Validation Layer supports calibration with incrementality test results (geo-lift, holdout experiments), post-purchase survey data (KnoCommerce, Fairing, etc.), MTA platform outputs, and custom business assumptions about seasonality, promotions, and channel dynamics. You can incorporate any trusted data source and compare the calibrated model's accuracy against your baseline.

Yes, and this is exactly why Validation Layer exists. Prescient's research shows that incorporating external data can sometimes degrade model accuracy — especially if test data was compromised by external factors or doesn't reflect your actual marketing environment. Validation Layer lets you test whether calibration helped or hurt before you act on the results, preventing you from making budget decisions based on a worse model.

Every model makes assumptions about your business — how channels interact, how fast effects decay, how seasonality works. Those assumptions may not capture your unique dynamics. Without testing them against your real outcomes, you're trusting a model that might not understand your business well enough to guide million-dollar decisions. Validation Layer lets you test and customize those assumptions based on evidence.

No. Validation Layer is built for marketing leaders, not data scientists. You configure calibration sources, run comparisons, and see accuracy scores — all through the Prescient platform. Guardrails prevent configurations that would break model health, so you get the freedom to customize without the risk. Your Customer Success Manager can also help you think through what to test first.

Still have questions?

Book a 30-minute call. We'll walk through how Validation Layer works with your specific data sources and measurement setup.

Book a demo

Learn more

Go deeper on validation

Why validation is critical for brands using an MMM
Marketing Measurement · Jan 13

Why validation is critical for brands using an MMM

Read article
Model validation in marketing mix modeling: What marketing leaders need to know
Marketing Measurement · Jan 12

Model validation in marketing mix modeling: What marketing leaders need to know

Read article
Calibration vs. validation: Understanding the difference
POV · Jan 7

Calibration vs. validation: Understanding the difference

Read article

200+

Brands measured

Daily

Model updates

$2.2B+

Ad spend measured

1–2 wks

Time to value

Stop guessing. Start testing.

In your demo, we'll show you how Validation Layer works with your data sources — and how to pressure-test your measurement before making your next big budget decision.

Book your demo

30-minute call · Custom validation walkthrough · No commitment required