Inspect the system
Access model specifications, assumptions, code, configuration and a reproducible execution process.
Transparent measurement Marketing measurement,
Marketing measurement,
built to be questioned .
A human-led, AI-orchestrated approach to MMM that your team can inspect, challenge, trust and own.
Humanjudgment
Agentorchestration
Statistics enginecomputation
UnderstandChallengeTrustOwn
The trust deficit
Big decisions require strong conviction.
The black-box problem
Outputs are visible. Assumptions are not.
MMMs can influence major budget decisions, but teams often hesitate to act on their recommendations and continue relying on familiar measures such as last-click attribution.
Many MMM outputs rely on complex dashboards and technical language, while the underlying assumptions remain difficult to inspect and question.
What teams need to inspect
- Assumptions shaping the result
- Evidence supporting each estimate
- Where the model is uncertain
- Why a recommendation was produced
- Where another method is needed
Transparency builds trust
Trust is earned after the system has been questioned.
Question the assumptions
Express priors in business terms and see how strongly they influence the final result.
Bring in business context
Introduce domain knowledge, known events, operational changes and market context.
Understand uncertainty
Separate strong findings from directional or assumption-dependent findings—and see what evidence would help.
How Transparent MMM earns trust.
Transparency by architecture
Human judgment, AI orchestration and statistical computation are intentionally separated.
AI opens the model
The agent helps people navigate the workflow, question assumptions, interpret outputs and understand limitations.
Assumptions are declared
Important beliefs and initial assumptions are recorded before fitting, not hidden inside the process.
Scope is stated honestly
The model answers only what the available data can support—and explains when another method is needed.
Humans lead. The agent orchestrates. The statistics engine computes.
Human leadership
Judgment, priors and business context
Human sends
Intent, priors and judgment calls
Agent returns
Tailored explanations, challenges, insights and alternatives
Conversation, routing and interpretation
Guides the workflow, calls the right tools and explains what the results mean.
Agent sends
Structured tool calls with appropriate arguments
Engine returns
Computed results for interpretation and communication
03 · Statistics engine
Tested, deterministic code.
No number is ever produced by the LLM. Every result comes from versioned statistical code.
Data audit and exploration
Reads and checks your raw data
Prior configuration
Translates business assumptions into model priors
Collinearity checker
Flags channels that cannot be separated
Bayesian model fit
The core statistical engine
Visualization tool
Renders findings with confidence included
Media optimizer
Suggests spend shifts at the same total budget
Every tool ships with a test suite. Fixed inputs, configuration, seeds and versions produce reproducible results.
Your choice of AI harness.
Transparent MMM is packaged independently of the interface, so the same measurement workflow can meet your team in Claude, ChatGPT or another compatible AI environment.
- The system works through the same conversational interface your team already uses.
- Interactive and adapted to each user’s communication preferences, role and familiarity with MMM.
- It retains conversational context across follow-up questions.
Mid-conversation, with computed output and a real follow-up question.
Approved product capture Same analysis.
Different harness.
Different harness.
Use the same dataset, output and follow-up question shown in Claude.
Why are the Display and Search estimates imprecise?
Reserved for the corresponding Transparent MMM run in ChatGPT.
Same product Different AI harnesses.
Transparent MMM Packaged to travel.
Skill
Workflow, questioning and interpretation.
CLI
Stable, repeatable execution.
MCP
Standard access to the statistical tools.
The AI interface can change. The measurement system does not. No dashboard · No portal · No single-vendor lock-in
MMM is a wide-angle lens—not a microscope.
An important part of the measurement stack—not a standalone tool.
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Provides a broad view of how media channels contribute to business outcomes.
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Its ability to identify an effect depends on the quality, variation and structure of the available data.
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Estimates channel effects more reliably when spend varies meaningfully over time. Channels with limited variation are harder to identify.
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When two or more channels move in lockstep, MMM may not be able to separate their individual effects reliably.
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Some limitations are inherent to the available data and the identification problem—not necessarily failures of the model or vendor.
The limitation is not the problem. Hiding the limitation is.
Honest about what the data can and cannot support.
When data can measure a channel
The system reports the result along with:
- Uncertainty ranges
- Model diagnostics
- Sensitivity to assumptions
- Explanation of how strongly data supports the results
When the data cannot identify an effect reliably
- State the limitation
- Explain the reason
- Recommend alternatives like a lift test, geo-experiment or on-off test
False confidence is more dangerous than ignorance. A trustworthy system must say: “Do not rely on this estimate yet.”
AI changed the case for owning MMM.
It changed the economics—not just the tooling.
Illustrative relative effort required to build an MMM system
Before AI
With AI
42% Less effort
AI and open-source tools made MMM algorithms more accessible
Open-source Bayesian tools and AI-assisted workflows have reduced the time, cost and specialised effort required to implement an MMM system.
Business context and judgment remain the differentiating factors
The most important inputs—priors, channel behaviour, control variables and interpretation—still depend on deep business context.
A stronger case for a customized internal MMM
When the differentiating knowledge sits inside the organisation, owning a tailored system can create more value than relying on a standardised, externally controlled platform.
A working end-to-end measurement workflow.
Transparent MMM combines an adaptive AI interface with deterministic statistical tools to guide and explain the complete measurement workflow. It adapts its communication to each user’s role and familiarity with MMM.
The workflow includes
- 01 Data ingestion and validation
- 02 Variable mapping
- 03 Prior definition
- 04 Ad-stock and saturation configuration
- 05 Collinearity checks
- 06 Bayesian MMM fitting Dedicated machine
- 07 Parameter extraction
- 08 Convergence and diagnostic checks
- 09 Stability testing
- 10 Contribution and ROI analysis
- 11 Scenario and budget analysis
- 12 Plain-language interpretation
Bring us the measurement question your team is wrestling with.
We’ll shape the conversation around your channels, data and decisions—not a canned product tour.
- See how assumptions become inspectable
- Walk through uncertainty in business terms
- Explore where MMM fits—and where another method is better
Start with your context A useful demo begins with the decision you need to make.
Share the channels, data or measurement question that matters most. We’ll use it to shape the conversation.
The proposal
Build this for your business, with your team.
We’ll work alongside your team to build a tailored agentic MMM system, integrated with your data and decision process—with validation and knowledge transfer built in.