Ultimate Buyer's Guide to MMM: How to Choose the Best Platform in 2025

We’ve all been there – you’re trying to understand how one Marketing Mix Modeling (MMM) platform differs from another, but it’s frustratingly difficult.

Part of the confusion lies in terminology. One vendor calls itself an Incrementality platform, while another talks about being an AI-powered MMM platform. One vendor positions itself as an MMM SaaS, and one is a Causal MMM tool. Some others are simply Marketing Mix Modeling platforms.

Different vendors emphasize different aspects of MMM or discuss the same topic with different words. But they all seem to promise improvement in ROI. Or sales.

How should a marketer considering buying a Marketing Mix Modeling tool or platform make sense of all this?

You’ve arrived at the right place! In this article, we will summarize what really matters when you’re considering buying MMM. In the end it's actually very simple - you should look for a Marketing Mix Modeling platform that:

• Helps you drive more sales
• Produces high quality results
• Is easy to test and start with

For each of these three topics, we have identified two types of features:

✅ Basic features: These help you identify Marketing Mix Modeling platforms, which have progressed one step further from legacy MMMs of the 2010s.

💎 Next Gen features: These help you identify most advanced Marketing Mix Modeling platforms with leading edge MMM technology in the market – we’ll call them Next Gen MMMs.

Let’s dive in!

1. Choose an MMM Platform That Helps You Drive More Sales

Your first objective is to ensure that the Marketing Mix Modeling solution has optimization capabilities that help you drive more sales. That’s ultimately what MMM is for, right? Marketing Mix Modeling should help you deliver tangible improvements to your business.

Based on our research there’s three primary levers how MMM can increase your sales: 1. Optimize campaigns, 2. Optimize budget allocation across advertising channels, 3. Optimize budget pacing. For ecom / DTC business, these three levers together can drive +6.5% sales increase without changing the total media budget, as illustrated below. Read the full study.

✅ Advertising channel-level optimization (Basic feature)

The most basic feature all MMMs should have (but all don’t have) is advertising channel-level optimization. Advertising channel in Sellforte-language refers to the level that is one step deeper into the ad platforms – some companies also call them “tactics” or “campaign groups”. These include for example, Meta Prospecting, Meta Retargeting, Meta Awareness, Meta Advantage+. Or Google Performance Max, Google Branded Search, and so on.

Basic cross-channel optimization of ad spend should be done on advertising channel level - it is not enough to optimize on the level of ad platforms (“Google vs Meta”). Advertising channels within the ad platforms have very different dynamics, and different diminishing return effects that need to be accounted for. Below is an example of advertising channel optimization output:

💎 Campaign-level optimization (Next Gen feature)

Next Gen Marketing Mix Models go one level deeper into the advertising channels – they provide campaign-level budget recommendations. Contrary to attribution, MMM-based campaign recommendations can be powerful, because they are based on understanding the true incremental sales impact of each campaign. In fact, we have found campaign optimization to be the lever with largest improvement potential: it can unlock +2.9% more sales for eCom/DTC businesses .

Campaign-level optimization with Next Gen MMMs is exciting, because it enables performance marketing teams to finally transition from biased attribution -based ROAS metrics, to incrementality-based optimization. Below is an example of Sellforte’s campaign dashboard.

💎 Optimal weekly budget pacing (Next Gen feature)

Optimal weekly budget pacing is often an overlooked improvement lever, because marketers have not traditionally had tools that provide realistic week-level budget recommendations that take business-specific demand seasonalities and promotional dynamics into account. However, in our research optimizing weekly budget levels can unlock +2.0% more sales for eCom/DTC businesses .

Budget pacing ensures that advertising spend is distributed optimally across weeks, preventing for example overinvestment when demand is naturally low and underinvestment when there’s high level of natural demand. Below is an illustration of what optimization outlook can look like for this lever:

❌ What to avoid?

Proceed with caution if you're evaluating an MMM platform that...

• Only provides ad platform-level (”Google vs Meta”) optimization
• Doesn’t provide campaign-level budget recommendations
• Doesn’t provide budget recommendation for each week

2. Choose an MMM Platform That Produces High Quality Results

When you’re confident that the platform you’re evaluating has the right optimization capabilities, it’s time to check whether you can trust the Marketing Mix Modeling results enough to start acting based on the recommendations.

I’ll be honest with you: there’s a lot of Marketing Mix Models generating low quality, non-sensical, modeling results in the market. Why? Generating MMM results is easier than ever before, for example with open-source modeling libraries like Meridian and Robyn. Today, it can even be done by a non-technical person without knowledge of statistics or econometrics, just by following simple tutorials. At the same time, the challenge of building MMMs that produce high quality results has remained, because MMM datasets are noisy and can have strong multicollinearity effects that need to be managed, as Google Research pointed out already in 2017 .

Luckily, there’s a few things you can easily check from any MMM vendor, that make it likely that the MMM produces high quality results.

✅ Statistical model validation (Basic feature)

This is obvious, so let’s keep it short: Your Marketing Mix Modeling platform should deliver basic statistical model validation metrics. However, anyone who has studied statistics knows that it’s possible to manipulate statistical measures (e.g., overfitting to maximize R2), which is why you should read on.

✅ Bayesian approach (Basic feature)

Bayesian Marketing Mix Modeling is today the golden standard for Marketing Mix Modeling, originally popularized by Google in 2017 (see e.g., link ). Bayesian modeling approach has several strengths over legacy models, such as the ability to incorporate prior knowledge, more robust estimates with limited data, better handling of uncertainty, and better ability to manage challenges related to overfitting.

💎 MMM is calibrated with informative priors (Next Gen feature)

While Bayesian approach is already used by 80%+ of Marketing Mix Modeling vendors and practitioners today, model calibration is only only used extensively by the most advanced MMM platforms. Model calibration started slowly gaining popularity in 2023, after Igor Skokan et al. launched their ground-breaking article in Harvard Business Review .

Model calibration is an approach for radically improving the quality of modeling results, by complementing traditional modeling data with other information about media effectiveness, such as experiment results or ROI benchmarks based on attribution data. Instead of using non-informative priors, e.g., “ROI can be anything between 0 and 100”, model calibration uses informative priors to narrow down the likely ROI range already before modeling, e.g., “ROI is between 4 and 5”.

Model calibration significantly improves the ROI estimates for individual channels, but also improves the model’s stability as a whole. But how can we produce informative priors that indicate the ROI is likely within a certain range? This brings us to the next topic.

💎 MMM has in-built Causal Attribution Model for generating informative priors (Next Gen feature)

The main assumption in model calibration is that you have strong evidence, which justifies using prior distributions that limit the range of possible ROI estimates. This makes model calibration tricky - you need to be REALLY SURE that the priors given to the model are based on solid evidence.

Most advanced MMM solutions achieve this by integrating a Causal Attribution Model into the modeling workflow. Causal Attribution Model estimates informative priors (i.e., likely ROI distributions) for each channel based on a broad set data. For example, Sellforte’s in-built Causal Attribution Model ingests

• Experiment data: Conversion Lift Tests, Geo Lift Experiments, and other incrementality tests.
• Attribution data: Google Analytics 4 last-click and DDA, Ad platform attribution, and Multi-Touch-Attribution data.

Using this data, Sellforte’s Causal Attribution Model produces informative prior distributions that provide a solid starting for the Bayesian model to estimate final ROI for each channel.

To be clear, none of the beforementioned data can be used directly as priors in the model. Especially attribution data is highly biased, and requires processing before it can contribute to the formation of the prior distribution. This is why Sellforte’s Causal Attribution Model has specialized processing for each type of input data. For example, Sellforte has specialized processing of Geo Lift experiments to truly understand each experiment’s uncertainty intervals and estimated lift.

💎 Advanced model validation based on model outputs (Next Gen feature)

Next Gen MMMs combine statistical model validation with validating outputs of the Marketing Mix Modeling. In a nutshell, these methods try to understand whether the modeling outputs make sense when compared to other data, even if the model statistically might seem ok.

Validation against experiments. Do the results align with experiment results? As an example, Sellforte Geo Lift Experiment analysis module that compares MMM results to a Geo Lift Experiment you conducted.

Validation against incrementality factors. Next Gen MMM platforms show MMM results for each channel next to ROAS reported by Google Analytics 4 last-click and Ad platform attribution. This enables incrementality factor validation, i.e. are the ratios of MMM ROI vs GA4 last-click ROAS in the range of what you typically expect.

Forecast accuracy. Next Gen MMM platforms provide forecast for the total sales of the company, split into base sales forecast, promotion-driven sales forecast, and media-driven sales forecast. This forecast can be compared to actualized sales. Be careful though, individual datapoints with high or low forecast accuracy does not always imply high or low model quality. In predicting the future, there’s lots of events outside the model that can influence the actual sales, ranging from world politics to inventory shortages.

❌ What to avoid?

Proceed with caution if you're evaluating an MMM platform that...

• Only focuses on statistical model validation
• Uses uninformative priors in the Bayesian modeling
• Doesn’t use attribution data and experiment data in model calibration
• Lacks a credible Causal Attribution Model for creating informative prior distributions

3. Choose an MMM platform that is easy to test & start with

We at Sellforte are passionate about making MMMs easy to use, and especially easy and fast to start with – but without compromising quality of MMM results.

✅ Data connectors (Basic feature)

Data connectors to eCom platforms, analytics platforms, ad platforms, and so on, are the basic building blocks of making Marketing Mix Model onboarding fast and easy. In a typical eCom / DTC business onboarding at Sellforte, customer spends 15-30 minutes connecting data. You can check this easily: Can you find a view like this from the platform:

💎 Delivers high quality results in 1-2 weeks (Next Gen feature)

Time from connecting data to having MMM results is a controversial topic. It is very easy to deliver results in 1-2 hours if you compromise on quality. We at Sellforte have decided to never compromise on results quality. We have currently reached a level of automation, where we can deliver results in 1-2 weeks for an eCom / DTC business, while at the same guaranteeing that the quality drivers discussed in the previous section are in place. You can use this as a benchmark in your evaluation.

💎 Provides a Free Trial (Next Gen feature)

This is easy to check! Next Gen MMM vendors have started offering free trials, where you

• Connect data in 15-30mins
• Get access to MMM dashboard and optimization tools for limited time
• Get a shareable report, which summarizes MMM -identified improvement potential and key recommendations

❌ What to avoid?

Proceed with caution if you're evaluating an MMM platform that...

• Doesn’t have data connectors
• Talks about manual data cleaning
• Can’t offer a free trial

4. Other considerations

Continuous updates. Marketing Mix Modeling platform should provide you with continuous updates. For example, in eCom/DTC and performance marketing use-cases the standard today is daily model updates, which is pre-requisite for conducting tactical campaign optimization and continuous tracking of campaign ROI.

Build or Buy. If you are in a position where you have the capabilities and financial muscle to invest millions of dollars to build a Marketing Mix Modeling platform described in this article, as well as patience to wait for its development, consider yourself lucky - you are in a unique situation! While building an own MMM platform gives you the ultimate freedom and flexibility, most companies decide to purchase subscription to an MMM platform from a specialized vendor, after evaluating the cost and effort of building and operating an MMM platform themselves.

Consulting projects or SaaS. Since Sellforte was founded in 2017, we've witnessed a major transition from consultant-delivered MMM PowerPoints to modern SaaS tools. If you're still considering MMM consulting projects in 2025, you should ensure you have a strong rationale and conviction why your consulting partner can deliver superior service against modern MMM SaaS vendors.

Conclusions

After reading this article, I hope it’s easier to start comparing different MMM platforms against each other and find one which fits your needs. To summarize, you should find an MMM platform that

• Helps you drive more sales
• Produces high quality results
• Is easy to test and start with

If you wish to see this in action at Sellforte, start your free trial.