
Peripheral Agentic MMM
A deep dive into how AI is reshaping Marketing Mix Modeling (MMM), yet not replacing the foundation of MMM
Deep dives on marketing mix modeling, AI agents, and how we think about measurement, straight from the Aryma AI team.
Marketing mix modeling estimates how much each channel contributes to sales, so budget can follow evidence rather than habit. If you are new to the method, start with our guide to marketing mix modeling. Our practitioner guides then cover the methods around it: how marketing attribution models assign credit and where they break, how incrementality testing measures what advertising caused, and how media planning turns a budget into a schedule you can measure. The rest of the posts cover AI agents in measurement and the products we build around a human-led model.

A deep dive into how AI is reshaping Marketing Mix Modeling (MMM), yet not replacing the foundation of MMM

Most AI assistants are designed to answer questions. But an AI agent participating in human conversations must also know when to remain silent.

How Aryma Labs uses Langfuse to trace every MMMGPT query end to end, turning AI debugging from guesswork into evidence.

How exclusionary prompts and restraints are reshaping the future of smarter, more nuanced MMM AI - lessons from Aryma Labs

It is not about the model architecture or model alone, it is all about the data and domain knowledge

How 10x engineers and the rise of tools like Claude Code could stifle creativity and why isolation might be the key to true innovation

Not all AI claims are equal - here’s how to tell if a vendor is solving real problems or just repackaging buzzwords.

From Excel add-ins to AI-driven insights, Aryma Labs is redefining exploratory data analysis, without forcing users to abandon what works.

How specialized domain intelligence and institutional memory redefine marketing strategy

From organizational memory decay to siloed insights and unrealistic budget plans-here’s how Aryma Labs is redefining marketing measurement with AI driven innovation.

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
A RAG chatbot retrieves your own documents before it answers. Inside the architecture, the seven ways retrieval fails, and how to evaluate one properly.
Incrementality testing explained in full: geo lift design, holdout sizing, the lift and iROAS formulas, statistical power, and how results calibrate an MMM.
A practical guide to marketing attribution: how each model allocates credit, which ones Google removed, where multi-touch breaks, and when MMM is better.
Marketing effectiveness is a measurement system, not a single metric. A practitioner guide to the methods, metrics, worked maths and the decisions they drive.
What is media planning? A practical guide to the media planning process, reach and frequency maths, budget allocation methods and proving the plan worked.