Three MMM engagements with three different vendors over the last eighteen months has produced enough comparison data that I can speak about the category honestly. The pricing is variable in ways the vendors do not advertise. The quality is variable in ways that are not visible at the procurement stage. The time-to-value is uniformly slower than promised.

The pricing reality

The published pricing of the major MMM vendors clusters in three bands. The premium consultancy band runs from $150,000 to $400,000 for an initial engagement, with quarterly refresh subscriptions in the $30,000 to $80,000 range. The mid-market platform band runs from $40,000 to $90,000 annually, including initial setup and ongoing refresh. The self-serve band runs from $15,000 to $30,000 annually, with most of the analytical work done by the customer.

What is not advertised is the data preparation cost on the customer side. All three bands assume that the customer can provide clean, well-structured marketing spend and outcome data. Most customers cannot, and the preparation work runs from $20,000 to $100,000 depending on data infrastructure maturity.

Where the quality diverges

The premium consultancies produce thorough, customised analysis with extensive scenario modelling and strategic recommendations. The analysis is usually rigorous. The recommendations are usually well-supported.

The mid-market platforms produce competent analysis that is less customised. The model assumptions are standardised. The output is workable for most categories but can miss the nuances of unusual marketing mixes or unusual industries.

The self-serve products produce analysis that is only as good as the customer’s analytical capability. Sophisticated customers extract real value. Less sophisticated customers produce analysis that is technically valid but operationally misleading.

Where the analysis broke down

In each of the three engagements, the analysis missed something important. In the premium engagement, the model overweighted a channel that had recently undergone a measurement change, and the recommendation was based on a comparison that was not apples-to-apples. In the mid-market engagement, the standard assumptions did not handle a specific marketing event well and the analysis was confounded by the event for several months. In the self-serve engagement, the analyst did not have the depth to question the model output and accepted a finding that contradicted the operational evidence.

None of these failures are unique to MMM. They are the standard failure modes of complex modelling work applied to imperfect data. The vendors that handle the failures best are the ones that build in explicit review and revision cycles.

The time-to-value gap

All three vendors quoted initial results within 8 to 12 weeks. All three took longer. The premium engagement took 18 weeks. The mid-market engagement took 14 weeks. The self-serve engagement took 22 weeks because the analyst needed time to learn the platform and the data.

The pattern suggests that time-to-value should be planned at 1.5x to 2x the vendor quote.

What I would do differently

If I were starting fresh, I would invest more in data preparation before engaging any MMM vendor. The data quality determines the analysis quality regardless of vendor. The data preparation is the part the customer owns and the part where most engagements stumble.

I would also build a deeper internal capability to challenge the vendor’s findings. The most useful MMM engagements are ones where the customer team can intelligently question the analysis. Without that capability, the vendor’s output is taken at face value and the value is diminished.

The vendor selection conversation

Pick the band that matches your spend level and your analytical capability. Below $5M annual marketing spend, the premium consultancy economics do not work. Above $20M, the mid-market platforms may not handle the complexity adequately. Match the vendor to the scale.

And budget for the data preparation. It is the part that gets missed and it is the part that matters most.