Marketing Measurement’s foray into MCPs – An unclear bet?
Everyone is talking about MCPs in the Marketing Measurement world. Recently, several measurement vendors have also started exposing their MMM and incrementality platforms through MCP servers.
It is not some massive technical breakthrough. But it does provide benefits under certain assumptions.
📌 Why the MCP play?
If you have seen Breaking Bad, you know one thing: distribution is everything 🙂. In other domains too distribution is everything.
Most measurement vendors are betting that usage of Claude, ChatGPT and similar AI interfaces will explode in the coming years. They are not wrong.
But the big question is :
– What fraction of those people will actually use those AI chatbots to ask about MMM and Incrementality experiments?
– And whether those interfaces will do justice to the requirements of MMM?
📌 What MCP can and can’t do
MCP solves a connectivity problem. It allows Claude or ChatGPT to access your measurement platform through a standardized interface. Useful? Yes.
But MMM never really suffered from a connectivity problem. MMM suffers from a visualization problem, interpretation problem and implementation problem.
In MMM, seeing is believing.
When a marketer asks: “Why did Paid Search contribution decline?”
The answer is rarely a paragraph of text. The answer is usually a combination of:
– Contribution charts
– Saturation curves
– Comparison of previous spend share effect share vs now
The visual itself often carries more information than the explanation (at least for a seasoned veteran).
A generic MCP call may retrieve the data.
– But can it automatically identify the most relevant charts needed to support the conclusion?
– Can it know when a saturation curve matters more than a contribution chart?
That is a much harder problem.
As MMM outputs become richer, the amount of context required also explodes: This quickly leads to token maxing and inflated AI costs.
Which raises another practical question:
Will organizations really want teams spending all day inside premium AI subscriptions for MMM workflows, when a purpose built SaaS +AI platform may provide a cleaner experience?
📌 Why we didn’t take the MCP route for MMM Synapse
MMM Synapse is not just a connector between AI and MMM. It is a memory and reasoning layer specifically designed for MMM. When a user asks a question, MMM Synapse does not simply retrieve data.
It retrieves:
– Relevant outputs and supporting charts
– Historical discussions, business context and previous recommendations
And reasons about them like a seasoned MMM practitioner. It is after all trained on 10+ years of our consulting notes.
In MMM, evidence matters as much as the answer.
In many ways: MCP = Access Layer; MMM Synapse = Understanding Layer
The future of MMM is not simply connecting models to AI.
Because ultimately, MMM is not just about generating answers. It is about generating confidence.
