How do enterprises use Marketing Mix Modeling (MMM) to allocate budgets across TV, online, and print ads?

Key takeaways

• Large companies use marketing mix modeling (MMM) to decide how to split budget across TV, online, and print based on what drives incremental lift, not what a platform claims.
• The MMM outputs that matter are response curves and marginal ROI. They show where each channel hits diminishing returns and where the next dollar should go.
• Hourly reporting helps teams run the plan day to day (pacing, issues, data quality). MMM sets the budget direction for the next planning cycle.

‍

Enterprises and large corporations use marketing mix modeling (MMM) to make budget trade-offs across TV, online, and print based on incremental impact and marginal returns, not platform attribution dashboards.

MMM is built for planning. It uses aggregated data (not user-level tracking), accounts for non-marketing factors, and stays usable even when identity signals degrade. 

What marketing mix modeling is

Marketing mix modeling is a statistical method that estimates how much each marketing input contributed to a business outcome over time, while controlling for other drivers (seasonality, price, promotions, distribution, macro factors). 

In plain terms, MMM answers:

• “What did TV contribute?”
• “What did online contribute?”
• “What did print contribute?”
• “If we move budget, what changes?”

Google’s Meridian describes MMM as a technique to guide budget planning decisions using aggregated data and non-marketing factors.

Why enterprises use MMM for budget allocation now

Enterprise teams are rebalancing measurement because privacy changes and signal loss make user-level attribution less reliable across channels. That is one reason MMM has seen a resurgence. 

Also, MMM works well for mixed media plans where TV and print influence demand indirectly and later. Aggregated, cross-channel measurement is the point. 

How large corporations run MMM in practice

1) Start with a decision, not a model

They define the decision first:

• Set next quarter’s split across TV, online, and print
• Find where each channel saturates
• Decide what to cut with lowest downside

If the output cannot drive a budget meeting, it is a science project.

2) Standardize the data (MMM inputs)

Most enterprise MMM runs on aggregated time-series. The exact cadence varies, but the structure is consistent: outcomes, channel inputs, and controls. 

Typical inputs:

• Outcome (KPI): revenue, units, signups, qualified leads, pipeline
• TV: spend plus delivery proxies (GRPs, reach, frequency) when available
• Online: spend split by meaningful channels (search, paid social, video, display, retail media)
• Print: spend plus insertion and timing data (drop dates), circulation proxies when available
• Controls: price, promos, distribution, holidays, macro indicators, competitor activity (when available)

3) Model lag and diminishing returns

Two effects are non-negotiable for budget allocation:

• Lag (adstock): media can affect outcomes beyond the week it ran
• Diminishing returns (saturation): incremental lift flattens as spend rises

Modern MMM tools bake these in. Meta’s Robyn explicitly outputs diminishing returns curves and includes adstock transformations. 

4) Validate with “ground truth” when possible

Strong teams do not treat MMM as perfect truth. They calibrate with incrementality evidence when feasible.

Robyn explicitly supports “ground-truth calibration” to account for causation. 

5) Turn results into response curves and scenarios

Executives do not want coefficients. They want:

• Response curves per channel
• Marginal ROI at current spend
• Scenario planning with constraints

Google’s Meridian ships scenario planning and “interactive budget optimization” as a first-class workflow. 

How MMM drives allocation across TV, online, and print

Enterprises move budgets based on marginal returns, not average ROI.

Average ROI asks: “How did this channel do over the whole period?”
Marginal ROI asks: “What happens if we add or remove budget now?”

That is what budget allocation requires.

TV: quantify carryover, then fund until it saturates

MMM helps teams estimate:

• how long TV effects persist (carryover)
• the spend level where TV flattens (saturation)
• cross-channel lift (TV often increases branded search and direct traffic)

In practice, TV decisions usually look like:

• hold a baseline TV presence for reach goals
• increase or decrease based on marginal ROI versus digital channels
• separate linear TV and CTV when data supports it (because their response curves can differ)

Online: split into real channels, not “digital”

Enterprises do not model “online” as a single bucket if they care about decisions.

Common splits:

• paid search (brand vs non-brand)
• paid social
• online video
• display and programmatic
• retail media

MMM is used to set budget ranges per channel. Platforms then optimize within those ranges.

Print: keep it only if it has measurable incremental lift or a clear brand role

Print can be harder to model because data is noisier and variation is lower. MMM still helps answer:

• does print show incremental lift after controls?
• does it work in specific geos or windows?
• is it mainly brand support?

In many large organizations, print becomes:

• a constrained minimum (brand decision), or
• a targeted lever in the markets where it shows lift

How Violet’s Hourly Report fits without confusing MMM

MMM is for planning and allocation. It is not an hourly tool.

Large teams still monitor execution at high frequency because problems happen fast:

• spend pacing and delivery issues
• tracking breaks
• creative fatigue
• sudden demand shocks

Hourly monitoring protects performance and data quality. Then it rolls up into clean aggregates that MMM can use for planning.

That is the clean separation:

• Hourly visibility: operate and catch issues early
• MMM: allocate budgets across TV, online, and print using incremental impact and marginal returns

The constraints enterprises add (the part most blog posts skip)

MMM outputs are optimized under constraints like:

• TV upfront commitments
• minimum reach or brand presence requirements
• geo coverage rules
  
  
• risk limits (do not shift more than X% in one quarter)
  
  

Scenario planning exists because real budgets are constrained budgets. Meridian’s Scenario Planner is designed around this idea of scenario-based budget decisions. 

Common MMM mistakes (and how enterprises avoid them)

1. Modeling “online” as one line item
   It hides the real response curves.=
2. Missing controls
   Then marketing gets credit for price cuts, promos, or seasonality.
3. Treating MMM like a one-time project
   MMM is most valuable as a repeatable planning cycle.
4. Optimizing on average ROI
   Budgeting is about marginal ROI and saturation.
5. Skipping validation
   Calibration or reality checks reduce wrong decisions and internal debate. 

Bottom line

Large corporations use Marketing Mix Modeling (MMM) to allocate budgets across TV, online, and print by:

• Using aggregated data and controls to isolate incremental impact
• Modeling lag and diminishing returns to find saturation points
• Converting results into response curves and scenario-based budget decisions

That is what makes MMM useful. It turns “what happened” into “what to fund next.”

Struggling with turning marketing investment into measurable P&L impact? Let’s have a chat about how we can make that happen.

‍

FAQs

What is the difference between marketing mix modeling and media mix modeling?

In practice, they are often used interchangeably. “Media mix modeling” usually focuses on paid media channels, while “marketing mix modeling” can include broader levers like pricing and promotions.

Does MMM work without cookies or user-level tracking?

Yes. Modern MMM is built on aggregated data and is positioned as privacy-safe because it does not rely on user-level identifiers.

Why do MMM results focus on diminishing returns?

Because budget allocation depends on marginal impact. Saturation curves show where additional spend stops producing meaningful incremental lift. Robyn explicitly produces diminishing returns curves for actionable decision-making.

What is adstock in MMM?

Adstock models the carryover effect of advertising over time. Meridian documents adstock as a decay function over a lag window.

How do enterprises use MMM outputs in budget meetings?

They use response curves and scenario planning. Tools like Meridian’s Scenario Planner are built to automate analysis reports and support interactive budget optimization for budget decisions.

Can MMM incorporate incrementality tests?

Yes. Enterprises use tests as calibration or validation. Meridian positions combining experiment results with MMM and then running optimization scenarios. Robyn also supports ground-truth calibration.