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Your data contains healthy spend variance, isolated channels, and sufficient baseline controls. Proceed directly to model architecture with confidence that your data is statistically capable of yielding high-precision results.
Pre-MMM Data Readiness Audit
Learn whether your data can carry an MMM before you pay for one.
Most Media Mix Models Are Doomed Before a Single Line of Code Is Written.
An MMM can only recover what your historical data already contains. Run a rigorous, 17-point Pre-MMM Data Readiness Audit to verify if your data can actually support a model—before you pay for one.
- Zero Modeling Required: Every check is completely answerable up-front, diagnosing hidden data gaps before you spend a dollar on consulting or platform fees.
- Identify Structural Flaws: Instantly flag flat channel spend, systemic tracking gaps, and channels moving in lockstep that destroy model accuracy.
- Clear, Actionable Blueprint: Receive a definitive readiness score (1 to 17) with a clear roadmap of exactly what to fix before commissioning any model.
Readiness scoreIllustrative example
11 / 17Fixable gaps
- Spend variance (CV)0.18Pass
- Channel correlation (max pairwise)0.84Flag
- Geo units available34 DMAsPass
- Clean history depth14 monthsFlag
The $100k MMM Mistake: Modeling over Empty Signal
“Most marketing measurement engagements that disappoint were statistically doomed before they started. A model cannot invent variance that isn't there.”
Brands routinely spend six figures and wait months for an MMM, only to receive wide, untrustworthy confidence intervals and unusable allocation recommendations. The reason isn't usually the algorithm—it's the underlying data.
If your channel spends are flat, if your campaigns are perfectly locked in lockstep with price promotions, or if your clean history is too short to account for baseline seasonality, an MMM has nothing to learn from.
Our Pre-MMM Data Readiness Audit acts as an objective pre-commission gate. By evaluating two critical pillars—the mathematical properties of your spend/outcome data and the availability of essential supporting inputs—you map out exactly what is fixable today and protect your analytics budget from structural failure.
Inside the 17-Point Pre-MMM Diagnostic Gate
Phase 01 · Signal & IdentificationChecks 01–06
01. Spend Variance Check: Has each channel's spend moved across the historical window at several distinct levels? Flat spend gives a model nothing to learn from. Threshold: Coefficient of variation (SD/Mean) ≥ 0.15. Flag below 0.10.
02. Independent Variation Across Channels: Do your channels move independently week-to-week, or rise and fall in lockstep? If two channels move together, the model cannot separate their joint effects. Threshold: Pairwise correlation < 0.8 (ideal < 0.6). Variance Inflation Factor (VIF) < 5.
03. Channel Concentration (HHI): Does a single ad channel dominate your mix? When one channel holds 80% of spend, smaller channels lack the variance needed to estimate reliably. Threshold: No channel > 40% of spend. Hard warning if spend-share Herfindahl index (HHI) > 0.25.
04. Natural Experiments in History: Does your data contain dark weeks, pauses, or large step changes? These on/off signals are the most identifying assets an MMM has. Threshold: At least one major on/off event or a 30-50% step change per major channel.
05. Statistical Power & MDE: Given your real outcome noise, is the study even powered to detect effects large enough to shift decisions? Threshold: Simulate on the actual design matrix to ensure the Minimum Detectable Effect (MDE) sits below your decision threshold.
06. Per-Channel Materiality Floor: Is an ad channel too small relative to your overall baseline outcome noise? Threshold: Flag channels representing < 2% to 5% of spend, or where the signal-to-noise ratio drops below 1.
Phase 02 · Confounding & ControlChecks 07–10
07. Media × Non-Media Confounding: Are your media flights perfectly tied to non-media drivers (e.g., every TV burst running exactly during a price promo)? If so, you lose the ability to isolate media lift. Threshold: Absolute correlation between flighting and promos < 0.6. Require 20-30% of flight weeks with no promo overlap.
08. Demand-Seasonality Confound: Does your media calendar mirror organic demand seasonality so closely that spend and season cannot be separated? Threshold: Absolute correlation between total media and demand-seasonality index < 0.7. Counter-seasonal spend is required.
09. Control-Variable Coverage: Are major non-media drivers actually present as hard data columns? Omitted variables bias every downstream ROI. Threshold: Explicit, historical data columns for pricing, promos, distribution, seasonality, competitor shifts, and macro variables.
10. Target Regime Shifts & Systemic Shocks: Has your business model, pricing strategy, or product lineup fundamentally changed during the history? Threshold: Documented change timeline overlaid on the data window to apply explicit break indicators or parameter truncation.
Phase 03 · Foundation, History & ScopeChecks 11–15
11. Data Quality & Completeness: Is spend, click, and conversion data perfectly logged and reconciled to finance? Threshold: Zero unexplained gaps. Aligned timing, consistent currencies, and uniform time zones.
12. Historical Depth & Stability: Do you have enough regime-consistent history to support your parameter count? Each channel demands roughly 4 parameters (effect, adstock, and two for saturation). Threshold: ≥ 15 months of consistent data. An observation-to-parameter ratio of roughly 10-to-1 or better.
13. KPI Definition & Scope: Is the modeled target outcome consistently defined? Mixing DTC and wholesale revenue corrupts downstream media coefficients. Threshold: One uniform, stable outcome definition across the entire data window.
14. Aggregation Trap (Execution vs. Data Granularity): Flattening localized daily spend bursts into national weekly aggregates introduces massive estimation bias. Threshold: Model resolution must match execution resolution. Flag when daily localized bursts are flattened into broad national records.
15. Disaggregated & Geo Data Availability: Does usable regional, store, or DMA-level data exist to rescue an underpowered national series? Threshold: Regional or geo data containing roughly 20 to 50+ usable cross-sectional units.
Phase 04 · Prior Knowledge & CalibrationChecks 16–17
16. Priors for Long-Carryover Channels: For slow-decay media (TV, OOH, CTV), do you have documented tests on half-life or decay shape? Adstock is notoriously difficult to isolate from noisy observational data alone. Threshold: A documented test or empirical prior on half-life and peak timing for every slow-decay channel.
17. Experiment & Incrementality Availability: Do you have recent, clean geo or lift tests to anchor your model? Calibration to tests is the modern gold standard. Threshold: At least one clean incrementality test on a material channel available to anchor Bayesian priors.
Reading Your Data Readiness Score
Fixable Gaps
Critical signal blocks are missing. You likely have overlapping channel flights or missing control variables. Close your Phase 01 and Phase 02 gaps first—repairing these structural data columns is significantly cheaper than buying a confused model.
Not Yet Ready
Your data cannot currently support a reliable Media Mix Model. Rely on independent geo-incrementality testing for immediate answers, implement structural data tracking fixes, and revisit your readiness next quarter.
Learn If Your Data Can Carry An MMM Before You Pay For One
Don't waste budget on a blind econometric modeling engagement. Let our data science architects run your spend data and outcome historical files through the 17-point readiness engine. Learn exactly where your data signal is strong, where it's missing, and get a definitive go/no-go roadmap.
Want the 17 checks as a PDF first?
Free Guide
The Pre-MMM Data Readiness Audit
17 checks to run before you commission an MMM, so you know your data can carry one before you pay.