1. The Measurement Models Most B2B Teams Know
Most B2B marketers have been trained on attribution models and funnel frameworks like the SiriusDecisions waterfall. These models were built to track lead progression through predefined stages and assign credit for conversions. Over time, they’ve been helpful for showing basic conversion rates and highlighting which campaigns “were associated with” the most leads. (I hate saying “created” because marketing was never that deterministic, even though the funnel and our attribution models suggested it is.)
But attribution has limits. It was designed for a simpler buying journey, one where you could track someone from click to conversion. Today’s B2B journeys aren’t that simple. They involve multiple touches, unknown decision-makers, dark social channels, and delayed conversion paths. And most importantly, attribution explains the past. It doesn’t make future recommendations.
2. When I First Met MMM at Adobe
When I was at Adobe, our team had already invested heavily in attribution models. Modified first-touch, influenced, and even as advanced as algorithmic. We thought we had it covered.
Then came the request: “Our B2C team has been having success with media mix modeling. You B2B marketers should try it.”
At first, we were skeptical. We had our funnel data. We had dashboards. We had our models. Why bring in another model?
But the more we worked with MMM, the more it exposed what our existing models missed. It could:
- Help us simulate budget shifts before making them
- Forecast results from different budget totals
- Estimate ROI and contribution to the entire target, not just to the deals with CRM relationships to leads
- Include channels that didn’t create lead records in the CRM
We stopped thinking of MMM as another attribution model and started seeing it as a strategic planning tool.
3. So What Is Media Mix Modeling?
Media Mix Modeling (MMM) is a statistical approach that helps you understand how your marketing investments drive outcomes like pipeline, sales, or revenue.
At its core, an MMM looks at your time-series data, weekly or monthly totals of spend and results, and identifies which channels statistically correlate with success. It does this without requiring user-level tracking or attribution tagging.
MMM models include features like:
- Lag effects: Some channels (like brand or PR) drive impact weeks or months later.
- Time decay: Effectiveness of a campaign can fade over time.
- Diminishing returns: Spending more doesn’t always mean earning more. MMM helps identify the point of saturation.
As mentioned earlier, one of the biggest strengths of MMM is that it works on aggregate data, so it can include channels and variables that attribution can’t. For example:
- Brand awareness campaigns
- PR and earned media
- Outbound sales and SDR outreach
- Even macroeconomic indicators or seasonality
And once the model is built, it’s not just an analytics tool. It becomes a budget simulator, helping marketers test “what if” scenarios and forecast the revenue impact of different spend allocations.
Instead of saying “this worked last quarter,” MMM helps you ask, “what’s likely to work next quarter?”’
Gartner describes MMMs as, “advanced statistical techniques [applied] to aggregated data to quantify the holistic impact of marketing and optimize business outcomes such as sales or lead generation. MMM solutions acquire and normalize marketing data, build advanced statistical models based on that data, measure marketing performance, and deliver recommendations to improve spending effectiveness and efficiency.” (Magic Quadrant for Marketing Mix Modeling Solutions, 19 November 2024.)
4. What MMM Can Do That Other Models Can’t
Unlike attribution models that only credit deals to associated leads or contacts, MMMs:
- 📢 Measure all marketing efforts, including non-gated channels like brand, PR, events, and dark social
- 📉 Reveal saturation points, showing where additional spend becomes inefficient
- 🎯 Evaluate marketing’s impact on all SQOs or closed-won revenue, not just those tied to a tracked lead source
This gives you a true picture of marketing’s contribution across the entire funnel, not just the pieces that pass through a tracked form.
5. MMM vs. Attribution: A Quick Comparison
6. What MMM Can’t Do
MMM isn’t perfect. And it’s not meant to replace all forms of measurement.
Some limitations:
- It won’t tell you which specific campaign or message resonated most
- It needs 18–24 months of structured, time-stamped data
- If not explained well, the math can feel like a black box
- It traditionally came with a high price tag (this may have been the main reason it wasn’t used, as only the largest CPG brands could afford them early on. But that’s coming down now. We price ours similar to attribution software solutions.)
7. Why B2B Needs MMM Now
B2B buying cycles are complex. Buyers are anonymous, journeys are non-linear, and many channels leave no trace. Attribution systems, which rely on deterministic credit assignment, fall apart in this environment.
MMM fills that gap:
- ✅ It measures impact, not just clicks
- ✅ It helps you forecast, not just explain
- ✅ It includes channels attribution ignores
Most importantly, it helps B2B teams answer the question: “If I had more budget (or less), what should I do?”
That’s the power of moving from proving the past to planning the future.
8. Closing Thought: Attribution Looks Back. MMM Looks Ahead.
B2B teams need both models. Attribution is useful for channel diagnostics and campaign performance. But when it’s time to build a budget, defend a plan, or shift investments, you need a model that simulates the future.
That’s what Media Mix Modeling does. And it’s why I left Adobe to build one designed specifically for B2B.
If you haven’t explored MMM yet, now’s the time.