Real-Time MMM Platform for Daily Marketing Insights

Q: Why can't I just use platform dashboards for daily marketing decisions?

Platform dashboards show CTR, CPC, and conversion data in real time, but they can't tell you whether those signals represent true incremental demand or platform noise. PlatformSense doesn't replace the econometric foundation of your MMM. It augments it by adding daily responsiveness while preserving the causal rigor that makes model-based decisions defensible to Finance.

Q: Does PlatformSense replace my MMM or require a full model rebuild?

No. PlatformSense augments your existing Agile MMM rather than replacing it. Long-run response curves remain stable, built on 1–3 years of historical data. The modifier layer adjusts daily effective elasticity through interaction terms that capture only forward-looking impact. No historical data is remodeled. The model gets more responsive without losing its statistical foundation.

Q: How does PlatformSense prevent overreaction to short-term noise?

Modifiers are mathematically bounded, so PlatformSense responds to genuine efficiency shifts, not daily fluctuations. The model answers two questions simultaneously: how does this channel generally perform, and how is it performing today compared to the baseline. The long-run response curves stay stable. Only the daily layer moves. Speed without sacrificing rigor.

90 days

average lag before traditional MMMs reflect market changes in your recommendations.

222%

rise in customer acquisition costs over 8 years - measurement lag compounds wasted spend.

Source: https://www.businesswire.com/news/...

$245B

in global ad spend lost annually to ineffective formats and programmatic inefficiencies.

Source: https://www.get-ryze.ai/blog/cost-bad-ad...

Speed vs. Rigor: The Impossible Choice PlatformSense Ends

Platform dashboards update by the second. Your MMM updates every quarter. Teams end up trusting neither, making budget calls on gut instinct.

PLATFORM DASHBOARDS

Fast, but not causal

Meta and Google show you CTR jumps, CPC drops, and conversion lifts in real time. But they can't tell you whether that drove incremental revenue or would have happened anyway. You're optimizing within a platform, not across your business.

TRADITIONAL MMM

Rigorous, but always lagging

Even weekly re-estimation can't solve the core problem: new signals get averaged into months of historical data. A creative that starts underperforming today won't appear in the model coefficients for weeks. By then, you've burned the budget. PlatformSense solves this without touching the long-run curves.

The Responsiveness of a Dashboard. The Rigor of an Econometric Model.

By augmenting Agile MMM response curves with daily time-varying modifiers, PlatformSense keeps the model grounded in reality, every single day.

Daily channel efficiency signals

Know which channels and tactics are over- or underperforming against their baseline today, not in the next quarterly report. Model recommendations update within 24 hours of a meaningful platform shift.

Creative fatigue detection

Continuously track CTR trends and engagement patterns at the tactic level. Catch performance erosion before it becomes material, and proactively rotate creative, not reactively.

Flash sale and event response

Detect efficiency shifts during Black Friday windows, product drops, or PR moments on Day 2 instead of Week 4. Scale winning combinations while the window is still open.

Noise-resistant, bounded intelligence

PlatformSense doesn't chase every daily fluctuation. Modifiers are mathematically bounded, so the model responds to genuine efficiency shifts rather than noise. The long-run response curves stay stable. Only the daily layer moves.

How PlatformSense Works

A three-step process that bridges raw platform data and causal insight, without discarding the statistical foundation that makes MMMs valuable.

Real-time platform data ingestion

PlatformSense pulls key effectiveness signals directly from your ad platforms: click-through rates, conversion rates, impression share, cost-per-click, and platform-reported revenue per impression. These are the same signals your team already watches; now, they feed the model.

Time-varying effectiveness modifiers

Each platform signal is indexed against its baseline and weighted by an alpha value that determines its influence on channel elasticity. If Meta CTR rises 15% above baseline, a positive modifier is calculated. If CPMs spike due to competitive pressure, a negative modifier is applied. These modifiers are bounded by a sigmoid function to filter out noise from the genuine signal.

Applied to stable long-term response curves

Rather than re-estimating the entire model, PlatformSense adjusts the daily elasticity used for budget recommendations, mROAS scoring, forecasting, and curve analysis. Long-term response curves are built on 1–3 years of data to ensure statistical rigor, while the modifier layer handles day-level variance. The model answers two questions simultaneously: "How does this channel generally perform?" and "How is it performing today vs. baseline?"

Where PlatformSense Changes the Decision Before It's Made

Competitive pressure eroding your paid efficiency

When a competitor increases spending or enters your auction, CPMs rise, and your ad reaches fewer incremental consumers. Traditional MMMs may not identify these changes for weeks. PlatformSense detects real-time shifts in cost per click and impression share, allowing you to cap or reallocate budgets before efficiency declines.

Detect auction pressure within 24 hours. Reallocate before overspend accumulates.

Creative fatigue before it costs you

Assets don't perform uniformly over time. Traditional MMMs can't detect gradual degradation until it's severe enough to appear in aggregated metrics, weeks later.

Track creative-level CTR and CVR daily. Rotate proactively, not reactively.

Seasonal momentum and engagement surges

PR moments, sponsorships, and seasonal demand shifts create short periods of increased brand intent. The key is to detect this lift early enough to amplify it, rather than waiting until it returns to the historical average. PlatformSense continuously tracks changes in CVR and engagement signals, ensuring your model captures momentum while it remains actionable.

Detect elevated engagement in real time. Capitalize on momentum, not historical averages.

PlatformSense is the Speed Layer. Not a Standalone Feature.

Together with Agile MMM, the Trust Engine, and Long-Term Impact modeling, PlatformSense turns measurement into a continuous operating system rather than a quarterly report.

FOUNDATION

Agile Marketing Mix

Two-stage architecture separating auction dynamics from consumer response. The statistically rigorous base that all other layers build on.

SPEED LAYER

PlatformSense

Daily time-varying modifiers applied to stable elasticity curves. Responsiveness without overfitting. Intelligence without noise.

VALIDATOR

Trust Engine

Experiment-model feedback loop that continuously validates incrementality. Every geo-lift test improves the model's accuracy over time.

LONG VIEW

Long-Term Impact

52-week brand multiplier that captures equity accumulation, ensuring your strategy balances short-term performance with compounding brand value.

John Wallace

CEO — LiftLab

All MMMs implicitly assume that all impressions are created equal. Most marketers instinctively know this is wrong, so they often override MMM recommendations. PlatformSense solves this, incorporating real-time signals to discern impression quality as it actually varies.

Frequently Asked Questions

What is PlatformSense, and what problem does it solve?

Most marketing mix models refresh quarterly, so efficiency shifts in your Meta, Google, or TikTok campaigns may not be reflected in your planning recommendations for weeks or months. PlatformSense adds a daily signal layer that applies live platform effectiveness data to your stable Agile MMM response curves, so your model reflects what's actually happening today rather than 90 days ago.

Why can't I just use platform dashboards for daily marketing decisions?

Does PlatformSense replace my MMM or require a full model rebuild?

How does PlatformSense prevent overreaction to short-term noise?

How is PlatformSense different from running your MMM more frequently?

Running an MMM more frequently re-estimates the entire model with new data, introducing instability as short-term noise gets averaged into long-run coefficients. PlatformSense separates the two layers entirely. The stable 1–3-year response curves do not change due to daily fluctuations. Only the daily modifier layer updates. You get the responsiveness of daily measurement without the instability of daily re-estimation.

How does PlatformSense affect mROAS, forecasting, and scenario planning outputs?

PlatformSense updates the effective elasticity used in mROAS scoring daily, so when you run a scenario in the Scenario Planner, it reflects today's channel efficiency rather than last quarter's average. Budget recommendations sharpen. Forecast ranges narrow. The entire decision layer compounds precision rather than averaging it away. Every Monday, your team acts on a model that knows what happened yesterday

Know If Your Ads Are Working Today.

See PlatformSense in action and find out which channels in your current mix are already drifting from their historical efficiency baselines.

Schedule a Demo

DAILY MARKETING MIX MODELING INTELLIGENCE

Your MMM Shouldn't Take Weeks To Tell You What Happened Yesterday.