The Rise of Agentic MMM (Marketing Mix Modeling): How AI Is Transforming Media Optimization

In the 2020s, Marketing Mix Modeling (MMM) and Incrementality Testing arrived in force to help marketers measure the ROI and incremental revenue impact of media. Marketers got dashboards filled with data to analyze the historical activities. They also got complex tools to optimize the future spend allocation.

Marketers got better insights to optimize media spend, but their workload also increased. Marketers had to become experts in measurement.

All of this is about to change.

In 2025 October, Sellforte launched the era of Agentic MMM by announcing a group of AI Agents using Sellforte's Marketing Mix Modeling, enabling marketers to optimize their spend allocation faster, more easily and with higher more rigor. In this article, we'll discuss what Agentic MMM is, and how it is changing how media is being measured and optimized.

What is Agentic Marketing Mix Modeling (MMM)?

In Agentic Marketing Mix Modeling (MMM), marketers get access to specialized AI Agents, powered by MMM, to get answers to questions about media measurement and media spend optimization.

As an example, a marketer might ask an Agentic MMM solution to provide optimized media spend allocation for the next month or quarter. In a few seconds, marketer gets a reply from the agent that summarizes an optimal media spend allocation that is based on the results of the underlying Marketing Mix Model.

1. Marketing Mix Modeling's superpower is to measure media ROI and predict how changes in media spend affect revenue forecasts.
2. AI Agents' superpower is to automate workflows so that they can help marketers in specific tasks, such as media planning, revenue forecasting or media buying.

To be clear, Agentic MMM is not about changing how the MMM analysis itself is done - we don't have AIs yet that can do the modeling part with the same rigor and quality than we can (this might change thein the future.). The cost of hallucinated answers is way too high in media optimization to take the risk at this moment.

Instead, the Agents use insights generated by MMM, such Marginal Incremental ROAS (miROAS), and tools enabled by MMM, such as Media Optimizer, in the backend to answer marketers' questions.

What does Agentic Marketing Mix Modeling (MMM) Look Like in Practice?

Interactions with AI Agents happen in a chat interface. Below is an example of Sellforte's AI interface, that can be accessed by everyone in the public Sellforte demo:

Using the chat-interface, marketers can ask any questions that the underlying AI Agents are trained to answer. Marketers might ask questions about budget allocation, sales forecasts, or optimal bid levels for campaigns and ad sets.

Below is an example of a question and reply that might happen between a marketer and AI. This example is based on Sellforte AI.

What are examples of AI Agents in MMM?

Let’s look at a few examples of AI Agents that already exist in the market today. Because the number of Agentic MMM solutions is very limited, we'll use the three AI agents announced by Sellforte in their Agentic MMM launch October as examples. They are illustrated below.

Media Planner Agent helps with all questions related to media spend optimization. It is a super smart analyst that builds precise recommendations for how to allocate budgets across channels, campaigns, and even ad sets. It helps you find an optimal spend allocation in annual or quarterly budget planning, but also provides hyper-granular bid value recommendations for each campaign & ad set. 

Media Buyer Agent helps you execute the plans you are building with the Media Planner Agent. It will push the bid value changes directly Google, Meta, TikTok, and other advertising platforms. You can define how the Media Buyer Agent operates. It can operate in full self-drive mode, automating your media buying against certain objectives, such as maximizing revenue within a specific media budget. It can also operate in an assisted-driving mode, where you are approving the actions it wants to take.

Experiments Agent supporting both the Agents above. Experiments Agent is your team’s ever-curious data scientist. It recommends, designs and analyses incrementality tests for you. It also tries to uncover the hidden experiments you have accidentally already done in the history, trying to minimize the need for new tests.

Agentic and non-agentic optimization workflows compared

But how is marketers' life practically influenced by Agentic MMM? Let's next compare how marketers' workflows differ when using agents, (Agentic workflows), and when not using agents (non-agentic workflows).

Let's assume you want to find an answer to this question: What is the optimal budget allocation if get 10% extra budget for the next quarter?

Here's what an Agentic workflow looks like:

1. Open Sellforte AI
2. Ask the question
3. Review the answer

Here's what a traditional work flow would look like:

1. Open Sellforte Optimizer
2. Create a new scenario
3. Configure the scenario: Optimization mode, Time period, and 6 other configs
4. Press "Create"
5. Assign constraints, such as keep email and SMS spend fixed (can't be scale)
6. Press "Optimize"
7. Analyze results

You can see how much faster and shorter the agentic workflow is. Another observation s that the time required by the agentic workflow is very similar for an experienced and non-experienced user. However, the traditional workflow can be very slow to execute for a non-experienced user, because optimization and scenario planning tools tend to have larger number of selections to choose from.

Benefits of Agentic MMM to marketers

Agentic MMM isn’t just about automation for the sake of convenience. It directly addresses the real-world pain points marketers face today. Here’s how it helps:

1. Faster access to insights: With the interaction model based on natural language, Agentic MMM enables faster access to insights. No dashboards, you can just access the question you need answered.

2. Higher media ROI and more profits with better optimization: Faster access to insights is already a step towards budgets that are more optimally allocated. But AI Agents can do more. They can fully automate media buying or operated in an assisted-drive mode where you approve the optimization decision.

3. Automated workflows save time: AI agents save massive amounts of time from marketers with workflows that are automated. 

4. Human creativity unlocked: When routine optimization becomes autonomous, marketing teams can spend more time on strategy, storytelling, and experimentation — the things humans still do best.

Conclusions

For years, marketing teams have been buried under data piecing together insights from countless dashboards, reports, and spreadsheets. Then, they’ve had to turn those insights into actions by hand. 

Agentic MMM changes that. 

By combining the insights from Marketing Mix Modeling with the power of AI, marketers get marketers the clarity and confidence to make smarter decisions faster. You’ll know exactly where to invest, when to act, and how to turn every marketing dollar into measurable growth. 

For digital marketers, eCommerce operators, and retail growth teams, this means faster time to insights, more optimized budgets and streamlined budget optimization workflows.

The challenge isn’t whether Agentic MMM will happen. It’s how quickly you’re ready to adapt your organization to use it.

Ready to start your journey to Agentic MMM? Book a demo with Sellforte today.

Authors

Lauri Potka is the Chief Operating Officer at Sellforte, with over 15 years of experience in Marketing Mix Modeling, marketing measurement, and media spend optimization. Before joining Sellforte, he worked as a management consultant at the Boston Consulting Group, advising some of the world’s largest advertisers on data-driven marketing optimization. Follow Lauri in LinkedIn, where he is one of the leading voices in MMM and marketing measurement.