Can a Media Mix Model provide reliable guidance at the ad or creative level?

Media Mix Models (MMMs) have historically operated at the strategic layer. They are designed to explain aggregate performance patterns across channels, objectives, and time - not to evaluate individual ads in isolation.

That limitation is not a weakness of MMMs. It reflects a statistical reality: most creative-level changes do not produce signals that are large or independent enough to be cleanly separated from normal variation in revenue and demand.

So the question is not whether an MMM can precisely evaluate individual creatives.

In most cases, it cannot - and attempting to do so introduces instability rather than clarity.

The more practical question is this:

Can a modern MMM, when designed with the right scope and combined with appropriate supporting signals, provide reliable, directional guidance at the ad level without overstating what the data can support?

That is the problem addressed here.

Why pure MMM does not extend cleanly to individual creatives

MMMs are top-down models. They rely on aggregated inputs and are built to detect patterns that are visible at that level of aggregation. For most brands, the contribution of a single creative is:

- small relative to overall revenue

- tightly correlated with other creatives in the same campaign

- influenced by shared budgets, targeting, auctions, and timing

Three constraints are especially important.

1. Parameter growth relative to signal

Introducing hundreds or thousands of creatives dramatically increases the number of parameters a model must estimate, often without enough independent variation to support stable outputs.

2. High correlation within platforms

Creatives within a platform tend to move together. Shared delivery dynamics make it difficult to distinguish the relative role of individual ads using aggregated outcome data alone.

3. Cadence mismatch

Many MMMs have historically refreshed on monthly or quarterly cycles. Creative performance changes more quickly, which means insights can arrive too late to guide day-to-day decisions.

For these reasons, applying a pure MMM directly at the ad level is generally not statistically reliable.

Why creative-level guidance still matters

Creative remains one of the most important levers performance teams actively manage. Decisions still need to be made about:

- which ads to rotate or scale

- which concepts warrant additional budget

- which creatives are supporting demand versus primarily capturing existing intent

Without full-funnel context, these decisions are easy to misread:

- A prospecting video may reduce site CVR while contributing to broader demand.

- An upper-funnel creative may appear inefficient in click-based views while influencing downstream revenue.

- Two creatives may look similar in-platform, yet differ once cross-channel effects are considered.

The challenge is not whether creative decisions should be informed by measurement - but how to do so without implying a level of precision the data cannot support.

Fospha’s approach: separating measurement from allocation

Fospha addresses this by clearly separating where measurement is most reliable from where allocation and prioritization are more appropriate.

1. MMM at the level it is strongest

Fospha’s MMM focuses on cross-publisher, full-funnel measurement at the campaign type or objective level, across platforms and markets.

At this level, there is typically sufficient variation to produce outputs that are:

- stable over time

- comparable across channels

- suitable for planning and budget decisions

This establishes the full-funnel frame teams use to interpret performance.

2. Switching signal for finer-grained views

Below that level, the model does not attempt to extend MMM beyond its natural resolution.

Instead, the signal changes.

Publishers have the strongest visibility into engagement, delivery, and auction dynamics within their own platforms. Fospha uses these intra-platform signals to allocate campaign-level MMM outputs down to ads.

This produces ad-level views that are:

- grounded in cross-channel, full-funnel measurement

- informed by platform-native signals

- consistent enough over short operating windows to support prioritization

These views are designed for decision support, not precise estimation of individual ad effects.

A hybrid approach by design

This is an intentional hybrid model.

-- MMM provides cross-channel, full-funnel measurement at the level where aggregated outcome data is most informative.

- Platform signals provide resolution within channels at the level where those signals are most reliable.

Each layer uses the signal best suited to the question being asked.

This avoids two common limitations:

- top-down models that stop at channel averages

- bottom-up metrics that optimize locally without broader context

The takeaway

MMMs are not creative testing tools, and they should not be positioned as such.

But when designed with sufficient frequency, scoped to the right level, and combined with appropriate platform-level signals, they can provide useful, defensible guidance for creative decisions, without overstating certainty.

One final point is worth addressing. Many modern measurement approaches extend beyond MMM aggregation by leaning more heavily on lower-level signals—whether in-platform performance metrics or user-level behaviours. These signals can be useful, but when used without a stable full-funnel frame, they introduce predictable risks:

- They bias decision-making toward demand capture, because lower-funnel and user-observable behaviours are easier to measure and respond to.

- They optimize locally rather than globally, improving in-platform or journey-level metrics without clarifying overall business impact.

- They increase volatility in creative and budget decisions, as small changes in granular signals can drive frequent re-ranking without meaningful strategic signal.

Fospha’s approach keeps granular views anchored to a consistent, cross-channel understanding of performance, so ad-level signals inform decisions without pulling budgets toward what is most visible rather than what is most effective. The aim is not more data, but better decisions that drive better business outcomes.

‍

‍