Marketing mix modeling
MAR-kuh-ting miks MOD-ul-ing
A statistical approach to measuring the impact of each marketing channel on business outcomes. Uses aggregate data, not individual tracking.
Marketing mix modeling uses statistical analysis to determine how each marketing channel contributes to business outcomes. It does not track individual user journeys. It correlates aggregate spending patterns with aggregate outcome patterns.
MMM has made a comeback as privacy regulations (GDPR, cookie deprecation) make individual-level tracking harder. You do not need cookies or user IDs. You need historical data on what you spent on each channel and what happened to pipeline and revenue. The model identifies correlations.
MMM works best at scale. You need 12-24 months of historical data across multiple channels with enough variation in spending to detect patterns. It cannot tell you which specific ad someone clicked. It can tell you that increasing LinkedIn spend by $50k correlates with an additional $200k in pipeline. For strategic budget allocation decisions, that is what you need.
Examples
MMM reveals channel effectiveness.
The model analyzes 18 months of data. Finding: each $1 spent on content marketing generates $8 in pipeline (over 12 months). Each $1 on paid search generates $4 in pipeline (same period). Each $1 on display ads generates $1.50. The CMO shifts budget from display to content.
MMM captures offline channels.
Multi-touch attribution cannot track conference attendance. MMM can. The model shows that months with conference participation have 25% more pipeline than months without, controlling for other spending. Conferences are worth the investment but multi-touch could not prove it.
MMM limitations.
The model says podcasts have zero impact. But the company only started podcast sponsorships three months ago. MMM needs more data to detect the signal. The team continues podcast investment based on qualitative evidence while waiting for enough data for the model to measure it.
In practice
Read more on the blog
Frequently asked questions
How is marketing mix modeling different from attribution?
Attribution tracks individual user journeys and assigns credit to specific touchpoints. MMM uses aggregate statistical analysis to correlate spending patterns with outcomes. Attribution tells you which ad a specific person clicked. MMM tells you which channel investments drive the most pipeline overall.
Do you need MMM if you have multi-touch attribution?
They complement each other. MTA is better for tactical decisions (which campaign to optimize). MMM is better for strategic decisions (how to allocate budget across channels). MTA cannot measure offline channels. MMM can. Most mature marketing organizations use both.
Related terms
An attribution model that distributes credit across multiple marketing touchpoints in the buyer journey. More accurate but more complex.
An attribution model that gives 100% of the credit for a conversion to the first marketing interaction. Simple but incomplete.
The marketing function that creates awareness and interest in your product. Fills the top and middle of the funnel with qualified prospects.
Revenue generated per dollar spent on advertising. A ROAS of 5:1 means every $1 in ad spend generated $5 in revenue.
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