Good MMM AI Prompts Come From Good MMM Analysts
How expert MMM analysts are shaping the future of AI-driven marketing mix modeling and why your AI’s accuracy depends on more than just LLMs
Key takeaway
AI can build marketing mix models quickly but not reliably, so Aryma Labs keeps the model core human-built and uses AI peripherally to generate insights and run budget optimization. As LLMs become commodities, what separates strong MMM vendors is the domain expertise embedded in prompts. Good MMM analysts define the guardrails, edge cases, statistical checks, and business logic, making the analyst the real force multiplier.
Can AI build MMM models in mins?
Yes.
But would it be accurate and reliable?
Not all.
AI maturity still has a long way to get to that level of accuracy.
Peripheral Agentic MMM
At Aryma Labs, the core nucleus 'The MMM model' is still built by us (humans). But we leverage AI innovatively to generate insights, disseminate insights, perform deterministic tasks like budget optimization. We call it the Peripheral Agentic MMM.
What makes our AI solutions so accurate
Recently we demo-ed our products MMM Synapse, Nebula, Singularity and the yet to be publicly released 'PPT add-in' (this is gonna be real game changer) to a group of CMOs, Brand and Media managers. Stay tuned for this release.
One of the CMO was so impressed that he wanted to explore strategic investments in us !!
While we were flattered at the offer, he asked us what makes your AI solutions so accurate, so on point and so rich in insights?
Well the answer is - Our MMM Knowledge.
Our Moat is not the LLM
Yes, our Moat is not the LLM. Soon all LLMs will become a commodity.
In terms of AI products/solutions, what will separate good MMM vendors from mediocre ones is not which advance LLM they use, but on what data the intelligence layer was trained on and plus the prompt engineering skills.
LLM is only one part, the other part is 'How well do you prompt?'
How well do you prompt?
Consider the following scenarios:
- Sales and media spends trend together
- There are multiple overlapping promotions, strong seasonality
- Saturation curves demonstrating diminishing returns.
AI cannot magically know how to connect the dots and form a coherent accurate narrative.
These overarching rules must be explicitly told to AI in the form of Prompt templates.
For example:
"Check the budget optimization scenario and verify whether the spends are taken out of channel that was demonstrating diminishing returns"
As you can notice, the above is a MMM skill. There is a link between budget optimization and saturation curves.
Related product
MMMGPT
A RAG-based AI trained on a decade of marketing mix modeling, answering with sourced, grounded responses.
Good MMM Prompts Come From Good MMM Analysts
At Aryma Labs, both Ridhima and I have taken a new role "Chief Prompt Supervisor". Our excellent MMM analysts provide prompts grounded in MMM knowledge. We then supervise them and edit it to account for edge cases and additional logics.
The quality of an MMM AI system is not determined only by the LLM.
It is determined by the quality of the domain expertise embedded into the prompts, workflows, diagnostics and reasoning chains.
The better the MMM analyst behind the system, the better the AI performs.
Good MMM Analysts - The force multiplier of AI
A lot of people think AI itself is the force multiplier. That is only partially true.
In MMM, AI becomes powerful only when guided by strong MMM analysts.
Because good MMM analysts define:
- The guardrails
- The edge cases
- The statistical checks
- The business constraints
- The causal assumptions
- The operational logic behind the model
In many ways, the first force multiplier is not AI.
It is the MMM analyst who shapes the intelligence behind the AI.
Interested in our AI solutions ?
Check out our website here - https://www.aryma.ai/ and feel free to schedule a demo.
Thanks for reading.
For help with MMM, Causal Marketing Experiments and Experimentation, get in touch with us.
We also build some pretty cool AI products to aid Marketing Measurements. Check out our products page to know more -
Frequently asked
Questions, answered
Can AI build accurate MMM models on its own?+
The post says AI can build MMM models in minutes, but they would not be reliably accurate because AI maturity still needs significant advancement. Aryma Labs keeps the MMM model core human-built and uses AI only for peripheral tasks.
Why do the authors say LLMs will become a commodity?+
The post argues that as all LLMs become commodities, the choice of LLM will not separate good MMM vendors from mediocre ones. What matters is the domain expertise and MMM knowledge embedded in the prompts and the data the intelligence layer was trained on.
What role do MMM analysts play in AI-driven MMM?+
The post says good MMM analysts define the guardrails, edge cases, statistical checks, business constraints, causal assumptions, and operational logic, making the analyst, not the AI, the primary force multiplier.
Aryma Labs is a marketing mix modeling consultancy founded in 2019. Aryma AI is its Gen AI division, applying agents to the periphery of MMM while keeping the statistical core human-led.
Explore the Aryma AI suite
Gen AI products for marketing mix modeling, built on a human-led statistical core. Explore the suite, or talk to the team.
More from Aryma
Keep reading
Peripheral Agentic MMM
A deep dive into how AI is reshaping Marketing Mix Modeling (MMM), yet not replacing the foundation of MMM
Why LLM Observability Matters: How We Measure Every Query Inside MMMGPT
How Aryma Labs uses Langfuse to trace every MMMGPT query end to end, turning AI debugging from guesswork into evidence.
The Art of Subtraction: Training AI Agents in Marketing Mix Modeling with 'Via Negativa'
How exclusionary prompts and restraints are reshaping the future of smarter, more nuanced MMM AI - lessons from Aryma Labs