What are the disadvantages of MMM?

by
Brian Plant
| Last Updated:
September 8, 2024
Here are the key disadvantages of Media Mix Modeling (MMM):
Data requirements:
MMM requires a large amount of high-quality historical data, typically at least 2 years worth.
This can be challenging for small businesses or startups with limited historical data.
Data collection and quality issues:
It's often difficult to collect all the aggregated data needed for an MMM analysis.
Incomplete or inconsistent data can limit the model's effectiveness.
Limited granularity:
MMM often operates at an aggregate level, analyzing channel effectiveness rather than individual campaigns or creatives.
This makes it challenging to identify specific elements within a channel that drive performance.
Difficulty with newer media channels:
MMM can be less effective at measuring the impact of emerging or less-established media channels due to lack of historical data.
Budget requirements:
MMM only starts making sense when a company reaches a certain scale and media budget size.
You need to create variability in your marketing mix to see the impact of each channel, which requires a larger budget.
Limitations in B2B contexts:
MMM can be trickier to implement in B2B environments compared to B2C.
It assumes a homogeneous population, which doesn't account for different B2B roles and personas.
Model assumptions and complexity:
MMM relies on various assumptions and can be complex to implement, often requiring specialized expertise.
Slow speed and inflexibility (for traditional MMM):
Traditional MMM can be slow to produce results and inflexible to changes in business questions or data environments.
These disadvantages highlight that while MMM can be a powerful tool, it has limitations and may not be suitable for all businesses or marketing contexts.