Top Marketing Measurement and Optimization Tools in 2025

In 2025, marketing teams have access to a variety of tools to measure and optimize their campaigns. From sophisticated Marketing Mix Modeling (MMM) techniques to various attribution models, marketers now have more approaches to choose from than ever before. Below, we explore five essential marketing measurement and optimization tools for 2025, discussing how they work and their key features:

• Marketing Mix Modeling
• Geo Lift Tests
• Conversion Lift Tests
• Last-click attribution
• Ad Platform attribution

1. Marketing Mix Modeling

What is Marketing Mix Modeling?

Marketing Mix Modeling (MMM) is a time-series analysis technique used to determine the impact of various marketing activities on a business outcome, typically revenue. By assessing the revenue contribution of each marketing channel and campaign, MMM enables the calculation of Return on Investment (ROI), helping marketers measure the efficiency of their advertising spend. To evaluate marketing’s influence on revenue, MMM relies on time-series data, incorporating sales data, data on promotions, data on marketing activities, and data on other relevant factors that affect business performance.

To get a basic understanding of how MMM functions, consider the following example:

Identifying Meta’s impact from the first sales peak: The model detects a revenue increase occurring simultaneously with Meta advertising. If no other variables explain the rise in sales, the model attributes the additional revenue generated during this peak to Meta’s advertising efforts.

Assessing Google’s effect from the second sales peak: During a subsequent and larger sales peak, the model observes that the business has also launched Google advertising in addition to Meta. Since it has already measured Meta’s effectiveness from prior data, it can now estimate the additional sales contribution driven by Google.

By analyzing multiple instances where sales and ad spend vary across different channels, the model can quantify the contribution of each channel to overall revenue. Most MMMs today are based on Bayesian modeling approach. For a deeper dive into Marketing Mix Modeling, explore our comprehensive Marketing Mix Modeling Guide .

What are Next Gen MMMs?

Next Gen Marketing Mix Models, like Sellforte, have a decisive advantage over Traditional MMMs in three areas:

• High Quality Modeling Results: By combining traditional MMM dataset with incrementality tests and attribution data, Next Gen MMMs provide significantly more robust modeling results than traditional MMMs.
• Rapid Onboarding: By leveraging data connectors, Next Gen MMMs can be onboarded in days or weeks, instead of months.
• Campaing-level spend recommendations. Next Gen MMMs provide campaign-level spend recommendations, instead of operating on the "Google vs Meta"-level.

For guidance how to identify a Next Gen MMM, you can read the Buyer's Guide to MMM platforms .

MMM Use-Cases: How Are Advertisers Using MMM?

Marketing Mix Modeling is used as the primary tool for:

• Measuring ROI of each channel and campaign (single-source of truth)
• Optimizing strategic budget decisions: Optimal budget for each channel, brand, country etc.
• Optimizing Tactical budget decisions: Optimal spend level for each campaign, optimal budget pacing across weeks

Why should MMM be used for all these use-cases? Because MMM is the method that unifies all other methods discussed later in this post: It combines data from incrementality tests, data from attribution tools, and traditional MMM data. This makes it the most robust method for estimating the true incremental revenue impact and ROI of all marketing channels and campaigns, and thus also the most robust method for optimization.

That said, other measurement methods discussed in this post are also important - they help contribute to high quality MMM results!

Top Marketing Mix Modeling Tools Used by Advertisers

To understand what MMM tools look like, let's take two examples among market leaders, one MMM SaaS tool, and one Build-It-Yourself MMM tool. For a long list of MMM tools, visit the MMM tool directory .

Sellforte (MMM SaaS)

Sellforte is a Next Gen Marketing Mix Modeling platform for Retailers, eCommerce businesses and DTC brands:

• Drive +6.5% more sales: Sellforte typically unlocks for eCom / DTC brands ( read the full research )
• Onboarding is fast & easy: Users spend 15-30min connecting their data with in-built data connectors, and MMM results are available in 1-2 weeks.
• High Quality MMM results: Sellforte uses Bayesian Marketing Mix Models with in-built causal attribution model that enables model calibration with experiments and attribution data.
• Campaign-level results: Get ROI and recommended budget for each campaign.
• Geo lift analysis is integrated into the platform, for validating MMM results, and calibrating the MMM.

Sellforte offers a free trial for qualifying eCom / DTC brands and retailers to test the platform.

Meridian (Build-it-yourself MMM)

Meridian empowers organizations to create tailored Marketing Mix Models (MMM). Like Sellforte, it employs a Bayesian MMM methodology, integrating advancements from Google Research to offer a robust toolkit for skilled analysts and data scientists.

With Meridian, users can:

• Develop models using Python
• Examine critical MMM outputs, such as Marketing ROI and diminishing return curves with plotted graphs
• Optimize media investments by simulating budget allocation scenarios in Python and visualizing the outcomes through plotted graphs

Meridian streamlines the development of Bayesian MMMs, eliminating the need for data scientists to manually implement fundamental components like adstock effects and diminishing returns.

This solution is ideal for organizations with experienced data science and software engineering teams seeking to build a highly customized MMM framework from the ground up. However, compared to a full-featured platform like Sellforte, Meridian lacks a marketer-friendly interface for conducting analysis and optimizing media spend. Additionally, it does not offer built-in integrations with advertising platforms, automated data processing pipelines, or streamlined model calibration workflows.

2. Geo-based Incrementality Tests (Geo Lift Tests)

What are Geo Lift Tests?

Geo Lift tests are a form of causal measurement where different geographic locations are exposed to marketing interventions while others act as control groups. By analyzing differences in performance, marketers can assess the true impact of a marketing channel. Unlike A/B testing, which focuses on individual users, Geo Lift tests evaluate marketing effectiveness at a macro level.

Geo Lift tests focus on isolating the incremental sales impact of one channel at a time. This can make well-designed Geo Tests perform well in providing a point-estimate for a channel's incrementality.

However, Geo Tests have three challenges that prevent them from being a primary measurement tool:

• One-off estimates - no continuous measurement: Geo Lift Tests provide incrementality estimate at a point in time.
• Not relevant for cross-channel measurement: Geo Lift Tests can be expensive if one aspires for high-level of statistical significance. This makes Geo Lift Tests unpractical for continuous cross-channel measurement
• Wide confidence intervals: Geo Lift Tests can have wide confidence intervals

How should you use Geo Lift Tests?

Geo Lift Tests can be used to calibrate Marketing Mix Models. Next Gen MMMs use Geo Lift Tests (in addition to other experiments) as one data source when estimating marketing ROI. If you're interested to learn how model calibration works, take a look at this in-depth article .

Geo Lift Tests can be used to validate Marketing Mix Models, by comparing experiment outputs to MMM outputs.

Geo Lift Tests outputs can be used to calculate Incrementality Factors in Causal Attribution. Causal attribution is a method for correcting attributed ROAS to reflect true incrementality of each channel. Causal Attribution provides marketers Incremental ROAS, which is much closer to a channel's true ROI than raw ROAS. While MMM is the preferred source for Incrementality Factors, Geo Lift tests can help a performance marketing get started on incrementality measurements. To learn more about Causal attribution, you can read this overview tutorial to Causal Attribution.

Top Geo Lift Testing tools used by advertisers

Here's two Geo Lift Testing tools one can use to get started.

Sellforte GeoLift Experiments

Sellforte's GeoLift Experiment tool enables marketers to analyze Geography-based Lift Experiments. It provides marketers with experiment's result, such as estimated incremental sales impact by the experimented channel, and its confidence intervals. Sellforte's Geo Lift Experiment tool is integrated into Sellforte's Marketing Mix Modeling platform, enabling easy MMM calibration with Geo Lift Tests.

Haus.io

Haus.io is a 2021-founded company, which provide a platform for conducting and analyzing Geo Lift tests.

3. Conversion Lift tests

What are Conversion Lift Tests?

A conversion lift test is an experimental approach that helps businesses determine how much of their conversion activity is directly influenced by advertising. It works by splitting an audience into two groups:

• Test Group: Exposed to the advertisement.
• Control Group: Not shown the advertisement (or shown a placebo ad).

By comparing the performance between these groups, marketers can isolate the incremental impact of their campaigns beyond what would have happened organically.

While Conversion Lift Tests have limitations, they have some resemblance to randomized controlled trials (RCTs), which are widely regarded as one of the most reliable ways to determine causal effects.

How should you use Conversion Lift Tests?

Conversion Lift Tests can be used to calibrate Marketing Mix Models. Next Gen MMMs can use Conversion Lift Tests as one data input for estimating ROI of a channel.

Conversion Lift Tests can be used to validate Marketing Mix Models. Incrementality estimates from Conversion Lift Tests can be compared to MMM's incrementality estimates to validate the MMM.

Conversion Lift Tests can be used to calculate Incrementality Factors in Causal Attribution. While MMM is the preferred source for Incrementality Factors for Causal Attribution, Conversion Lift Test can also be use to for calculating Incremental ROAS in Causal Attribution to have a more accurate understanding of channel's ROI.

Examples of Conversion Lift Test tools

Conversion Lift Tests are offered by all major ad platforms:

• Meta Conversion Lift Test: Link
• Google Conversion Lift: Link
• TikTok Split Testing: Link

4. Last-Click Attribution

What is Last-Click Attribution?

Last-click attribution assigns 100% of the credit for a conversion to the final touchpoint before a purchase. Last-click is by far the most popular cross-channel attribution method used by marketers, mainly because it's so simple to understand and easy take into use.

While widely used, last-click attribution has significant shortcomings, which makes it unusable as the primary measurement method. Due to it's simple click-based last-touch measurement approach, it severely underestimates the ROI of some channels and over-estimates the ROI of others. As an example, our research found that the real effectiveness of Meta channels can be 1.6x-9x higher than what last-click is reporting.

Are there other cross-channel attribution methods?

Other attribution methods include first-click attribution, linear attribution, time-decay models, and data-driven attribution. However, they are all rule- and click-based attribution models, which suffer from the same flaws than last-click, unable to measure the true incremental sales impact of each channel. The picture below summarize the attribution methods, and how they weigh channels differently.

How should you use Last-Click Attribution?

While last-click reported ROAS is highly biased to favouring some channels, the data can be valuable for marketing tams.

Last-click ROAS data can be used in Marketing Mix Model calibration, when it is interpreted and processed appropriately.

Last-click ROAS can be used in Causal Attribution. If last-click ROAS is corrected with appropriate incrementality factors, causal attribution can provide an estimate for a channel's incremental ROAS.

Top Last-click Attribution tools used by advertisers

Google Analytics 4 is by far the most used tool for last-click attribution, but there's are others as well, such as Adobe Analytics.

5. Ad Platform Attribution

What is Ad Platform Attribution?

Ad Platform Attribution refers to the measurement of marketing impact using attribution data measured by advertising platforms. Ad Platform Attribution is based on setting up a tracking pixel, such as Meta Pixel, to a website, enabling the Ad Platform to see conversions on the website. User can typically set up various settings for conversion tracking, such as what is the time window for attribution.

Ad Platform attribution suffers from self-attribution bias, where platforms overestimate their contribution to conversions, often driven purely by the fact that it is technically not feasible for the ad platform to evaluate its performance against other ad platforms. Additionally, Ad Platform ROAS can't be used to compare campaigns within the ad platform, because the share of truly incremental conversions differs widely depending on the type of campaign. As an example, retargeting campaigns typically capture lots of conversion that would have occurred even without marketing, but prospecting campaigns to new customers can be highly incremental.

How should you use Ad Platform Attribution?

While often inflated, Ad Platform attribution data can be very valuable for marketers.

Ad Platform -reported ROAS data can be used in Marketing Mix Model calibration, when processed appropriately.

Ad Platform -reported ROAS data can be used in Causal Attribution. If corrected with appropriate incrementality factors, causal attribution can provide an estimate for a channel's incremental ROAS, based on ad platform attributed ROAS.

Top Ad Platform Attribution tools used by advertisers

Ad Platform attribution is available from most Ad Platforms, such as Google Ads, Facebook Ads, TikTok Ads.

Conclusions

In 2025, businesses have access to a diverse range of marketing measurement and optimization tools. Here's how marketers should use them:

• Marketing Mix Modeling: Single-source of truth for marketing ROI. Main tool for tactical and strategic budget optimization decisions .
• Geo Lift Tests and Conversion Lift Tests: Use them to increase the robustness of MMM via model calibration, and later to validate the MMM.
• Attribution data from Last-click and Ad Platforms: Use them to increase the accuract of MMM with model calibration

Ready to see all of this in action at Sellforte? Start your Free Trial

Further Reading

• What is Marketing Mix Modeling? A Complete Guide. Link

• Calibrating Marketing Mix Models with Experiments and Attribution data. Link

• From Last-click to Marketing Mix Modeling (MMM): Unlock +6.5% more sales. Link

• How to measure Meta correctly? GA4 vs. MTA vs. MMM. Link

• Advertising response curves: What are they and why do you need them? Link

• How Much Ad Spend Is Needed for Marketing Mix Modeling? Link

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.