How to Prepare Your Data for MMM

Don’t let your data health be the bottleneck for MMM success

From Annual Reporting to Real-Time, Modern Marketing Mix Modeling

Traditional MMM was a static, top-down budgeting tool. You’d run a model once or twice per year using monthly data, mostly covering TV, radio, and offline spend. There was no app data. No granular channel-level breakdowns. No clickstreams. You were lucky to get consistent spend and revenue across a few major channels.

Today, it’s different. Modern MMM ingests millions of rows of marketing and product data—daily, even hourly, across an intricate web of media sources. It tracks with ease:

• Paid and organic impressions
• Clicks, conversions, and revenue
• Events across web, app, and subscription funnels
• Platform-specific nuances such as SKAN postbacks or subscription lag

This granularity enables unprecedented precision in understanding channel performance, but it also means that any inconsistencies or breaks in your data, even subtle ones, get amplified.

You’re Never Fully Onboarded

A common question we get is “How long does it take to onboard with MMM?”

The truth is that you’re never fully onboarded.

MMM isn’t a dashboard you plug in and forget. It’s a living, evolving model, recalibrated continuously, updated as marketing strategies evolve, refined as new data becomes available. A good MMM isn’t static; it improves over time through:

• Testing different model specifications
• Introducing new data sources
• Running marketing and incrementality experiments
• Calibrating against external signals and market trends

If you’re chasing perfection, you’ll never get there. But a good MMM paints the bigger picture of the media mix combination delivering truly incremental results. Last-touch attribution, while intuitive, is known to brush over how upper- and mid-funnel channels impact bottom-funnel conversions—misrepresenting what’s truly driving growth.

Some Common MMM Data Problems We See

MMM Data Problem 1: Spend Data Issues

• Data spread across multiple platforms
• Missing mobile web splits (iOS vs. Android)
• MMP cost data pipeline errors, especially for mobile web. Certain MMPs try to allocate cost against attribution logic—and can get it wrong.

When spend data is incomplete or mislabeled, MMM can’t accurately tie marketing investment to outcomes, undermining the model’s core value proposition.

MMM Data Problem 2: Taxonomy Drift

• Event names that change over time (e.g., “purchase_event” → “checkout_complete”), breaking continuity
• Inconsistent channel naming conventions—for example, TikTok campaigns named differently across ad accounts
• UTM parameters that are messy, duplicated, or missing

These inconsistencies fragment your dataset, making it hard for MMM to track performance trends over time. You can fix this by maintaining consistent naming across platforms and campaigns, documenting any changes so your MMM provider can account for them.

MMM Data Problem 3: Revenue Tracking Gaps

• Subscription events not properly tracked—especially for App Store revenue, where fees, lag, and platform differences muddy the waters
• Tracking that doesn’t distinguish between web and app conversions or capture cancellations/refunds
• Revenue tracking not set up correctly in the first place

Without accurate revenue data, MMM cannot correctly attribute performance to marketing efforts, making ROI calculations unreliable. You can fix this by feeding revenue data directly from source-of-truth systems—backend or subscription platforms as well as MMPs—and accounting for platform feeds and refund behavior when possible.

This Is All Manageable—If Flagged

Here’s a key takeaway: Broken data isn’t a deal-breaker. But unacknowledged broken data? That’s what breaks the model.

We’ve built interpolation and Bayesian smoothing into our pipeline. If you’re missing data for a week or two? No problem—as long as we know about it.

But if the data drops without going to zero—say due to a failed tracking implementation or platform outage—and it’s not flagged, the model may falsely attribute the dip to a marketing input, skewing the results.

The solution? Communicate the gaps so the model can adjust or ignore that window.

MMM Is a Data Collaboration Project

MMM can’t just be owned by marketing. It needs input from:

• Data teams, to ensure correct schema, pipeline reliability, and tracking standards
• Product or engineering, to integrate product usage and event data
• The MMM provider, to model, interpret, and adjust based on experimental feedback
• The business, to input context (e.g., market disruptions, outages, seasonality)

Cross-functional collaboration isn’t just a nice-to-have, it’s essential for MMM success. When data teams flag pipeline issues early, product teams surface feature launches that might impact engagement, and business leaders communicate market shifts, the model can account for these variables rather than misattributing the effects to marketing channels.

We’ve seen many cases where external events outages dramatically impact app engagement but aren’t surfaced during onboarding. The model shows unexpected dips in performance. Only after manually adding in external event data do model results make sense. MMM isn’t just ingesting numbers, it’s interpreting behavior. And behavior without context is just noise.

Understanding the State of Your Data

Marketers love the insight that MMM can bring. but few realize how much their own systems are the limiting factor. If your naming is inconsistent, events are misfiring, or revenue tracking is spotty, the model will reflect that back to you—truthfully, sometimes painfully.

This isn’t a reason not to pursue MMM. Quite the opposite: It’s an opportunity to get control of your measurement foundation. Many organizations discover that the process of preparing for MMM reveals data gaps they didn’t know existed, leading to improvements that benefit all analytics efforts, not just modeling.

But this requires a mindset shift:

• Be honest about your data maturity.
• Work cross-functionally to fill the gaps.
• Treat MMM like a product, not a project.
• Don’t let perfection get in the way of good enough.

What Does an Ideal MMM Setup Look Like?

We can build models from almost anything: S3 buckets, MMP APIs, spreadsheets. But here’s the gold standard:

• 2–3 years of historical data—sufficient history to capture seasonality, trends, and year-over-year patterns
• Data housed in centralized platforms like BigQuery or Redshift—enabling efficient querying and reducing pipeline fragmentation
• Event-level cohort data—enabling MMM to track user value over time and attribute long-term outcomes to acquisition sources
• Spend broken out by platform, geo, and format—providing the granularity needed for actionable optimization recommendations
• Revenue tracking (net of store fees where possible)—ensuring that ROI calculations reflect true business value
• Support for UA, UE, and brand data streams—capturing the full spectrum of marketing activities
• External factors (e.g., influencer spikes, outages, PR events) tracked in logs—providing context that prevents false attribution

MMM Isn’t Too Slow, But Your Data Might Be

The notion that MMM is slow, clunky, or hard to work with is rooted in outdated workflows—or poor data management. Modern MMM, built on automated pipelines and real-time infrastructure, can be fast, flexible, and deeply informative. But the real challenge isn’t technical; it’s organizational readiness.

MMM doesn’t fail because of models. It fails because of messy inputs and misaligned expectations.

The path forward is straightforward:

1. Be honest about your data maturity.
2. Work cross-functionally to fill gaps.
3. Treat MMM as an evolving product rather than a one-time project.

If you approach MMM onboarding with this mindset—honest about your data, collaborative with your partners, and open to iteration—MMM can be the most powerful measurement tool in your stack.

Ready to assess your data’s MMM readiness? Contact our team to explore how Kochava’s MMM can work with your current data infrastructure. For information about the new MMM data validator tool within Kochava, refer to this post.

MMM Data Health FAQ