Best Incrementality Testing Tools in 2026: In-depth Vendor Comparison

Quick Summary: Best Incrementality Testing Tools in 2026

This research compares seven vendor-backed incrementality testing tools (Sellforte, Lifesight, Measured, Haus, Recast, LiftLab, and Analytic Partners) and one open-source library (Meridian GeoX) across 32 criteria in seven categories. Claude and ChatGPT each scored the tools using publicly available information on September 8–9, 2026.

Sellforte received the highest average total score at 25.0 out of 32, followed by Lifesight at 18.0 and Measured at 15.0. Results differed by category: Haus received the highest assigned score for experiment recommendations and insights, while Sellforte received the highest assigned scores in the other six categories.

The table shows the average scores assigned by Claude and ChatGPT, with the maximum available points for each category. Use the criteria relevant to your team to guide questions for vendors, and see the evaluation and scoring methodology for evidence standards and research limitations.

Research takeaways by vendor

These takeaways describe the average scores assigned by Claude and ChatGPT within this eight-tool comparison.

• Sellforte — 25.0 / 32. Received the highest total score and the highest assigned scores in six categories: geo test analysis, A/B test analysis for owned media, conversion lift test analysis, unified experiment library, MMM integration, and enterprise-grade platform. Sellforte is best for advertisers looking for a full-scale enterprise-grade incrementality testing platform that covers all incrementality test types and collects them into a unified experiment library that is integrated into an MMM. Sellforte is particularly strong in retail and ecommerce.

• Lifesight — 18.0 / 32. Received the second-highest total score and the second-highest assigned scores for geo test analysis (3.8 / 6) and unified experiment library (1.8 / 3). Tied with Measured for the second-highest MMM integration score (2.3 / 3).

• Measured — 15.0 / 32. Received the third-highest total score. Tied with Lifesight for the second-highest assigned scores in experiment recommendations and insights (4.8 / 6) and MMM integration (2.3 / 3).

• Haus — 13.3 / 32. Received the highest assigned score for experiment recommendations and insights (5.5 / 6), with full credit for recommending what to test, control and test groups, and test design, as well as AI-generated summaries and conversational AI for experiments.

• Recast — 12.3 / 32. Received the second-highest assigned score for conversion lift test analysis (1.5 / 5). Its highest-scoring category, as a proportion of available points, was experiment recommendations and insights (3.3 / 6).

• LiftLab — 12.0 / 32. Tied with Lifesight for the second-highest assigned score for enterprise-grade platform (3.8 / 5). Its next-highest category, as a proportion of available points, was experiment recommendations and insights (3.8 / 6).

• Analytic Partners — 8.5 / 32. Its highest-scoring category, as a proportion of available points, was experiment recommendations and insights (2.5 / 6). Claude and ChatGPT assigned full credit for recommending control and test groups and test design.

• Meridian GeoX — 5.8 / 32. The open-source library received its highest category score, as a proportion of available points, for experiment recommendations and insights (3.0 / 6), with full credit for recommending what to test, control and test groups, and test design.

Introduction and Table of Contents

The 32 evaluation criteria were developed from more than 700 discussions with marketers and marketing analytics professionals, enterprise RFP requirements, practitioner interviews, and desk research. They cover geo tests, owned-media A/B tests, conversion lift analysis, experiment recommendations, a unified experiment library, MMM integration, and enterprise requirements.

This article provides the criteria, scoring instructions, criterion-level averages, and vendor scorecards for the September 2026 evaluation. The detailed scoring workbook includes the individual Claude and ChatGPT evaluations, rationales, and cited sources. Use them to identify the workflows to verify in vendor demonstrations.

1. Quick summary
2. What is incrementality testing?
3. Research methodology: evaluation criteria
4. Research methodology: scoring
5. Tools included in the evaluation
6. In-depth comparison
7. Summary by vendor
   1. Sellforte
   2. Lifesight
   3. Measured
   4. Haus
   5. Recast
   6. LiftLab
   7. Analytic Partners
   8. Meridian GeoX
8. Frequently asked questions
9. Change log
10. Evaluation dates and model versions
11. Limitations and disclosures
12. Further reading
13. Authors

What is Incrementality Testing?

Incrementality testing estimates the additional sales, conversions, or revenue caused by a marketing activity. It compares outcomes under a treatment with a control or estimated counterfactual representing what would have happened without that intervention.

Marketing mix modeling (MMM) uses historical time-series data to estimate marketing effects across channels. Experiments deliberately change a marketing activity and measure the outcome. The two approaches can complement each other: experiments can test a specific decision or inform MMM calibration.

This evaluation covers three common testing workflows:

• Geo tests: compare geographic areas with different marketing treatments, using a suitable control or counterfactual analysis.
• Owned-media A/B tests: compare randomized audience or customer groups, for example when testing a catalog mailing or an email campaign.
• Platform-run conversion lift tests: compare treatment and holdout groups within an advertising platform.

Results depend on the test design, sample size, measurement quality, and assumptions. A test may estimate incremental conversions, revenue, or iROAS with an uncertainty interval; an inconclusive result is also possible. An estimate from one population, period, or spending level may not transfer directly to another. When using experiments to calibrate MMM, account for those differences and the uncertainty in translating test results into model priors.

Research Methodology: Evaluation Criteria

Each tool is evaluated against the 32 criteria below. The framework was developed through primary research, discussed in the companion guide How to Choose an Incrementality Testing Tool. The criteria printed here define the current version used for this comparison.

In the primary research, 

1. We analyzed more than 700 discussions with Sellforte customers and prospects, including marketers, marketing analytics leads, and data scientists working in advertising-heavy industries such as retail, ecommerce, DTC, travel & hospitality, and restaurants.
2. We reviewed the documentation of more than ten enterprise RFPs spelling out the requirements for incrementality testing platforms.
3. We interviewed our Customer Success and Data Science team members who get constant feedback and improvement ideas on Sellforte's own incrementality testing offering.
4. We complemented this primary research with desk research and LLM-assisted investigation to catch gaps and pressure-test our assumptions against publicly available information.

The result: 32 evaluation criteria across 7 categories.

The seven categories below define what we measured and why each matters.

Category 1. Geo Test Analysis

This category measures Geo testing capabilities. Geo tests compare outcomes across geographic areas to estimate a marketing intervention’s effect under the test’s design and assumptions. The example screenshots below illustrate a KPI summary and result charts in Sellforte.

1. KPI Summary, including iROAS, confidence intervals, and the test period:

2. Charts for key KPIs, including comparisons between test and control for the target KPI and media spend:

To start the analysis, geo testing tools provide a configuration interface (example below).

Category 2. A/B Test Analysis for Owned Media

This category measures capabilities in audience-level A/B tests, which are another broadly used incrementality test type. Examples include testing owned media such as catalogs and email.

The criteria assess evidence for an owned-media A/B-test workflow: analysis and uncertainty estimates, a results interface, a media counterfactual, and configurable measurement windows.

Category 3. Conversion Lift Test Analysis

This category measures capabilities in ingesting and analyzing Conversion Lift tests, which are experiments run within a single ad platform (Meta, Google, or TikTok). User-based conversion lift tests compare a group eligible to receive the campaign with a holdout group withheld from it. Assignment to the treatment group does not guarantee that every member sees an ad.

This category evaluates what a tool does with conversion lift results: ingesting them, making them available for analysis, connecting through APIs, comparing them with ad-platform data, and showing test progress. The research scores describe public evidence for these workflows.

The screenshot below illustrates conversion lift analysis in Sellforte. It is an example interface, not evidence that every platform or every criterion is covered.

Category 4. Experiment Recommendations & Insights

This category measures capabilities for analyzing incrementality tests with modern AI approaches, as well as recommending tests and test designs to help marketers get more value from their measurement programs.

The criteria distinguish test-planning recommendations from AI-generated summaries, conversational analysis, and MCP access to results. The screenshots below illustrate an AI summary and a conversational interface in Sellforte.

Category 5. Unified Experiment Library

This category assesses whether a platform can store geo tests, owned-media A/B tests, and conversion lift tests in a central library, and whether that library supports filtering and access controls.

A unified library stores every experiment, including geo tests, A/B tests, and conversion lift tests, in one searchable and filterable place. This prevents costly re-runs of tests already completed, makes organizational learning compound over time, and supports governance through role-based access controls. Below is an example screenshot of a unified experiment library covering Geo Tests, Conversion Lift tests and A/B tests.

Category 6. MMM Integration

This category assesses how a tool connects experiment results with MMM calibration. Experiments estimate effects for a particular design and context; MMM calibration uses that evidence with assumptions about how it transfers to the model’s scope.

The criteria distinguish prior-based calibration, an interface for connecting experiment results to MMM, and comparability between experiment-based and attribution-based priors.

Category 7. Enterprise-Grade Platform

This category measures vendors' capabilities to serve large organizations with mature IT policies. The dimensions scored include security certifications (SOC 2, ISO 27001), data residency options between US and EU regions, a choice of at least two cloud providers from AWS, GCP, and Azure, and enterprise authentication via single sign-on.

The relevance of these requirements depends on the buyer’s IT policies, procurement process, and deployment needs. A lower enterprise score does not establish that a tool is unsuitable for every organization.

Research Methodology: Scoring

Claude and ChatGPT separately scored each of the eight tools against 32 criteria. The evaluation log lists the model versions and completion dates. The detailed scoring workbook contains category and criterion-level scores, each model’s original assessments, rationales, and source URLs.

1. Score each criterion: each model used the rubric presented in this article, recording a score, rationale, source URL, and source type.
2. Average the two model scores: the criterion average is (Claude score + ChatGPT score) / 2. Keep this value unrounded for calculations.
3. Calculate category and total scores: sum the unrounded criterion averages within each category, then sum the seven category scores. Every criterion has equal weight; categories with more criteria contribute more points.
4. Format the results: tables and chart labels display one decimal. Rankings, ties, color shading, and chart positions use unrounded scores. The eight-tool average includes all eight tools. A normalized category score is its score divided by its maximum, expressed as a percentage.

For example, model scores of 1 and 0.5 produce a criterion average of 0.75, displayed as 0.8. The unrounded 0.75 is used in totals. This explains the quarter-point averages in the research and why adding displayed rounded values may not reproduce a displayed total.

Why is the scoring done by Claude and ChatGPT?

Simulating evaluation a buyer might make based on public materials. By using only vendor-provided materials on their website and technical documentation in the assessment, Claude and ChatGPT -based evaluation simulates how a buyer without prior knowledge of the vendor might assess the vendor prior to a sales call or demo meeting.

Transparent methodology and reproducible assessment. Because the evaluation instructions are published alongside this article, any reader can re-run the evaluation for any vendor and verify or challenge the results. This is a higher standard of transparency than conventional analyst-style research, where the scoring rationale is typically not disclosed.

Public documentation as the evidence base. Claude and ChatGPT were instructed to score vendors using publicly available documentation. The availability and discoverability of that documentation can affect the assigned scores.

Equal treatment of vendors. Claude and ChatGPT apply the same instructions, the same criteria, the same scoring scale, and the same source prioritization rules to every vendor in the comparison.

Instructions given to Claude and ChatGPT

The following instructions reproduce the evaluation workbook used for the September assessment.

Scoring model

The same three-level rubric applied to all 32 criteria. All 512 individual scores in the current research records use one of these values; 0.25 and 0.75 arise only after averaging the two models.

A score of zero means no public evidence was found under this methodology. It does not confirm that a capability is absent. Partial credit reflects the models’ interpretation of the evidence against the full criterion.

Source material and terminology

The instructions restrict evidence to the vendor’s website and separately hosted technical documentation. Technical documentation has first priority, product pages second, and marketing collateral third. Source restrictions support comparison under common rules, but they may exclude relevant evidence available privately or through other channels.

Instruction 6 permits specified terminology equivalences when interpreting vendor materials. These are rules for this evaluation, not a claim that ROI and iROAS are interchangeable in every business context. Buyers should confirm the numerator, cost basis, time window, and uncertainty behind any metric used in a decision.

How to reproduce the evaluation and scoring yourself with Claude or ChatGPT?

To reproduce the evaluation for any vendor in this study, you can follow the instructions in this section. They are accurate as of 9th September 2026. Reproducibility instructions may require updating as model versions change.

Step 1. Download the Evaluation Instructions Google Sheet as an Excel file (or other file format you can attach to an LLM prompt): Evaluation instructions. 

Step 2. Open Claude in Incognito mode to disconnect its memory about your previous conversations that might influence the evaluation. To achieve the same in ChatGPT, you need to disable ChatGPT memory.

Step 3. Choose a model. See specific LLM-model versions we used in the evaluation log.

Step 4. Initiate the prompt: "Evaluate [add vendor name, e.g., Sellforte], based on the instructions in the attached Excel."

Repeating the instructions may produce different results because models, search results, public documentation, and interpretation can change. The published scores are a dated research snapshot.

What biases & limitations does our scoring approach have, and how are we addressing them?

While LLM-based evaluation reduces vendor bias and promotes equal treatment of vendors and reproducibility, there are four main limitations to be aware of.

1. Availability and of documentation that each vendor has made public. A vendor with extensive and detailed public documentation will naturally score higher than one that keeps product details behind a sales call gate, even if the underlying capabilities are comparable. Each vendor can affect their own scoring through public documentation.

2. LLMs' ability to find public documentation. Even with web search enabled, Claude and ChatGPT may not find every relevant page on a vendor's website. Documentation that is not well-indexed or that sits in obscure subdomains may be missed. We tried to address this by using advanced models from two separate LLMs, and added a specific point to the instructions to search for vendor-provided technical documentation that might sometimes be under a different sub-domain.

3. LLMs' ability to interpret public documentation against the scoring criteria. Matching a product description to a specific criterion requires judgment. We reduce interpretation variance by using advanced models from two separate LLMs, providing detailed criterion descriptions, and providing explicit terminology equivalences. However, edge cases may still exist.

4. Limitations of LLM technology, including hallucinations. LLMs are known to occasionally assert things that are not based on facts. To reduce this risk, we used advanced models from two separate LLMs, asked them to provide source URL for their assessment, and asked them to provide a rationale for the score in each criterion.

Which Incrementality Testing Tools Were Evaluated?

The September 2026 comparison covers seven commercial tools offering incrementality testing within a dedicated product or a broader measurement platform, plus one open-source library. The selection is not an exhaustive list of the market.

• Sellforte
• Lifesight
• Measured
• Haus
• Recast
• LiftLab
• Analytic Partners
• Meridian GeoX

Google describes Meridian GeoX as an open-source geo-experiment solution. It is included to represent an option for teams building their own measurement workflows. Its narrower scope differs from the commercial platforms, particularly on hosted interfaces, experiment-library features, and enterprise controls.

All eight tools were assessed against the same 32 criteria. Inclusion does not establish that every tool covers all three test types or satisfies the enterprise requirements.

In-depth Comparison of Incrementality Testing Tools and Vendors

Summary by vendor

The profiles below summarize the scores assigned by Claude and ChatGPT in the September 8–9, 2026 evaluations. Each scorecard compares a tool with the average and highest scores across all eight tools. The assessment covers public evidence against 32 criteria, rather than hands-on product testing. 

1. Sellforte (research score: 25.0 out of 32)

Research overview

Sellforte received an average research score of 25.0 out of 32, the highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 8, 2026, across 32 criteria in seven categories.

Category scorecard

Sellforte: scores assigned in this researchAverage and highest scores cover all eight tools, including Sellforte. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	Sellforte	Average research score	Highest research score
1. Geo Test Analysis	4.0 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	3.5 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	4.5 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	3.3 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	2.0 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	3.0 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	4.8 / 5	2.6 / 5	4.8 / 5
Total score out of 32	25.0 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe Sellforte’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

SellforteEight-tool averageHighest research score

1. Geo Test Analysis

4.0 / 6

2. A/B Test Analysis for Owned Media

3.5 / 4

3. Conversion Lift Test Analysis

4.5 / 5

4. Experiment Recommendations & Insights

3.3 / 6

5. Unified Experiment Library

2.0 / 3

6. MMM Integration

3.0 / 3

7. Enterprise-Grade Platform

4.8 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• MMM Integration — 3.0 / 3. Its assigned score was above the eight-tool average of 1.6 / 3. Sellforte had the highest score among the eight tools in this category. User-controlled prior calibration averaged 1 / 1 (criterion 6.1); The interface connecting experiments to MMM averaged 1 / 1 (criterion 6.2); Comparability of experiment-based and attribution-based priors averaged 1 / 1 (criterion 6.3).

• Enterprise-Grade Platform — 4.8 / 5. Its assigned score was above the eight-tool average of 2.6 / 5. Sellforte had the highest score among the eight tools in this category. Independent security assurance averaged 1 / 1 (criterion 7.2); US and EU data residency averaged 1 / 1 (criterion 7.3); Dual-cloud options averaged 1 / 1 (criterion 7.4).

• Conversion Lift Test Analysis — 4.5 / 5. Its assigned score was above the eight-tool average of 1.0 / 5. Sellforte had the highest score among the eight tools in this category. API ingestion of conversion lift results averaged 1 / 1 (criterion 3.3); Daily snapshots of test progress averaged 1 / 1 (criterion 3.5); Comparison with campaign and ad set-level ad-platform data averaged 0.5 / 1 (criterion 3.4).

Where scores were lower

• Experiment Recommendations & Insights — 3.3 / 6. Its assigned score was below the eight-tool average of 3.8 / 6. Recommendations for control and test groups averaged 0 / 1 (criterion 4.2); Test design and success prediction averaged 0.25 / 1 (criterion 4.3); MCP access to experiment results averaged 1 / 1 (criterion 4.6).

• Geo Test Analysis — 4.0 / 6. Its assigned score was above the eight-tool average of 2.8 / 6. Sellforte had the highest score among the eight tools in this category. Synthetic-control geo-test analysis averaged 1 / 1 (criterion 1.1); Launching tests from the platform averaged 0.25 / 1 (criterion 1.5); Automatic test detection averaged 0.25 / 1 (criterion 1.6).

• Unified Experiment Library — 2.0 / 3. Its assigned score was above the eight-tool average of 1.0 / 3. Sellforte had the highest score among the eight tools in this category. A central library covering the three test types averaged 1 / 1 (criterion 5.1); Library filtering averaged 0.5 / 1 (criterion 5.2); Library governance averaged 0.5 / 1 (criterion 5.3).

Notable Reference Customers

Sellforte lists following companies as examples of public reference customers:

• Fashion Ecommerce: bonprix, Azzas 2154, Represent, Odlo
• Home & Furniture Ecommerce: Finnish Design Shop
• Specialty Ecommerce: FCP Euro, Smartphoto
• Grocery Retail: Lidl
• Fashion Retail: C&A, KIK
• Cosmetics Retail: Douglas
• Sport Retail: Interpsort
• Pet Retail: Fressnapf, Musti Group
• Specialty Retail: Tchibo
• Electronics Retail: Verkkokauppa.com
• Other segments: Telenor (Telecommunications), Paysafe (Payments), eBilet (part of Allegro Group, Events)

Research summary

Sellforte received the highest total score and led six of the seven categories. MMM integration was its highest-scoring category as a share of available points. Experiment recommendations and insights was its lowest-scoring category on that basis, and the only category in which its score was below the eight-tool average.

Sellforte is best for advertisers looking for a full-scale enterprise-grade incrementality testing platform that covers all incrementality test types and collects them into a unified experiment library that is integrated into an MMM. Sellforte is particularly strong in retail and ecommerce.

2. Lifesight (research score: 18.0 out of 32)

Research overview

Lifesight received an average research score of 18.0 out of 32, the second-highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 8, 2026, across 32 criteria in seven categories.

Category scorecard

Lifesight: scores assigned in this researchAverage and highest scores cover all eight tools, including Lifesight. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	Lifesight	Average research score	Highest research score
1. Geo Test Analysis	3.8 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	0.5 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	1.3 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	4.8 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	1.8 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	2.3 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	3.8 / 5	2.6 / 5	4.8 / 5
Total score out of 32	18.0 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe Lifesight’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

LifesightEight-tool averageHighest research score

1. Geo Test Analysis

3.8 / 6

2. A/B Test Analysis for Owned Media

0.5 / 4

3. Conversion Lift Test Analysis

1.3 / 5

4. Experiment Recommendations & Insights

4.8 / 6

5. Unified Experiment Library

1.8 / 3

6. MMM Integration

2.3 / 3

7. Enterprise-Grade Platform

3.8 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• Experiment Recommendations & Insights — 4.8 / 6. Its assigned score was above the eight-tool average of 3.8 / 6. Recommendations about what to test averaged 1 / 1 (criterion 4.1); Recommendations for control and test groups averaged 1 / 1 (criterion 4.2); Test design and success prediction averaged 1 / 1 (criterion 4.3).

• MMM Integration — 2.3 / 3. Its assigned score was above the eight-tool average of 1.6 / 3. User-controlled prior calibration averaged 1 / 1 (criterion 6.1); The interface connecting experiments to MMM averaged 1 / 1 (criterion 6.2); Comparability of experiment-based and attribution-based priors averaged 0.25 / 1 (criterion 6.3).

• Enterprise-Grade Platform — 3.8 / 5. Its assigned score was above the eight-tool average of 2.6 / 5. Independent security assurance averaged 1 / 1 (criterion 7.2); US and EU data residency averaged 1 / 1 (criterion 7.3); Enterprise single sign-on averaged 1 / 1 (criterion 7.5).

Where scores were lower

• A/B Test Analysis for Owned Media — 0.5 / 4. Its assigned score was below the eight-tool average of 0.9 / 4. Owned-media A/B-test analysis averaged 0.25 / 1 (criterion 2.1); The results interface averaged 0.25 / 1 (criterion 2.2); The media-counterfactual criterion averaged 0 / 1 (criterion 2.3).

• Conversion Lift Test Analysis — 1.3 / 5. Its assigned score was above the eight-tool average of 1.0 / 5. Conversion lift results ingestion averaged 0.5 / 1 (criterion 3.1); The conversion lift dashboard averaged 0.25 / 1 (criterion 3.2); Direct API ingestion averaged 0 / 1 (criterion 3.3).

• Unified Experiment Library — 1.8 / 3. Its assigned score was above the eight-tool average of 1.0 / 3. Coverage of all three experiment types averaged 0.5 / 1 (criterion 5.1); Library filtering averaged 0.25 / 1 (criterion 5.2); Library governance averaged 1 / 1 (criterion 5.3).

Research summary

Lifesight received the second-highest total score. It tied with Measured for second in experiment recommendations and insights and MMM integration, and with LiftLab for second in the enterprise category. Owned-media A/B-test analysis was its lowest-scoring category; the experiment-library score was among its lower scores as a share of available points but remained above the eight-tool average.

3. Measured (research score: 15.0 out of 32)

Research overview

Measured received an average research score of 15.0 out of 32, the third-highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 8, 2026, across 32 criteria in seven categories.

Category scorecard

Measured: scores assigned in this researchAverage and highest scores cover all eight tools, including Measured. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	Measured	Average research score	Highest research score
1. Geo Test Analysis	3.5 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	0.8 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	0.0 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	4.8 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	1.0 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	2.3 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	2.8 / 5	2.6 / 5	4.8 / 5
Total score out of 32	15.0 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe Measured’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

MeasuredEight-tool averageHighest research score

1. Geo Test Analysis

3.5 / 6

2. A/B Test Analysis for Owned Media

0.8 / 4

3. Conversion Lift Test Analysis

0.0 / 5

4. Experiment Recommendations & Insights

4.8 / 6

5. Unified Experiment Library

1.0 / 3

6. MMM Integration

2.3 / 3

7. Enterprise-Grade Platform

2.8 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• Experiment Recommendations & Insights — 4.8 / 6. Its assigned score was above the eight-tool average of 3.8 / 6. Recommendations about what to test averaged 1 / 1 (criterion 4.1); Test design and success prediction averaged 1 / 1 (criterion 4.3); MCP access to experiment results averaged 1 / 1 (criterion 4.6).

• MMM Integration — 2.3 / 3. Its assigned score was above the eight-tool average of 1.6 / 3. User-controlled prior calibration averaged 1 / 1 (criterion 6.1); The interface connecting experiments to MMM averaged 1 / 1 (criterion 6.2); Comparability of experiment-based and attribution-based priors averaged 0.25 / 1 (criterion 6.3).

• Geo Test Analysis — 3.5 / 6. Its assigned score was above the eight-tool average of 2.8 / 6. Synthetic-control geo-test analysis averaged 1 / 1 (criterion 1.1); The self-serve results interface averaged 1 / 1 (criterion 1.2); Launching tests from the platform averaged 1 / 1 (criterion 1.5).

Where scores were lower

• Conversion Lift Test Analysis — 0.0 / 5. Its assigned score was below the eight-tool average of 1.0 / 5. Conversion lift results ingestion averaged 0 / 1 (criterion 3.1); The conversion lift dashboard averaged 0 / 1 (criterion 3.2); Direct API ingestion averaged 0 / 1 (criterion 3.3).

• A/B Test Analysis for Owned Media — 0.8 / 4. Its assigned score was below the eight-tool average of 0.9 / 4. Owned-media A/B-test analysis averaged 0.5 / 1 (criterion 2.1); The results interface averaged 0.25 / 1 (criterion 2.2); A configurable post-test measurement window averaged 0 / 1 (criterion 2.4).

• Unified Experiment Library — 1.0 / 3. Using unrounded scores, it was slightly above the eight-tool average; both display as 1.0 / 3 after rounding. Coverage of all three experiment types averaged 0.5 / 1 (criterion 5.1); Library filtering averaged 0 / 1 (criterion 5.2); Library governance averaged 0.5 / 1 (criterion 5.3).

Research summary

Measured received the third-highest total score, with its strongest normalized results in experiment recommendations and insights, MMM integration, and geo-test analysis. It tied with Lifesight for second in the first two categories. Conversion lift test analysis received zero evidence credit across all five criteria, and owned-media A/B-test analysis was also below the eight-tool average.

4. Haus (research score: 13.3 out of 32)

Research overview

Haus received an average research score of 13.3 out of 32, the fourth-highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 8, 2026, across 32 criteria in seven categories.

Category scorecard

Haus: scores assigned in this researchAverage and highest scores cover all eight tools, including Haus. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	Haus	Average research score	Highest research score
1. Geo Test Analysis	3.3 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	0.5 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	0.5 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	5.5 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	0.5 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	1.0 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	2.0 / 5	2.6 / 5	4.8 / 5
Total score out of 32	13.3 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe Haus’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

HausEight-tool averageHighest research score

1. Geo Test Analysis

3.3 / 6

2. A/B Test Analysis for Owned Media

0.5 / 4

3. Conversion Lift Test Analysis

0.5 / 5

4. Experiment Recommendations & Insights

5.5 / 6

5. Unified Experiment Library

0.5 / 3

6. MMM Integration

1.0 / 3

7. Enterprise-Grade Platform

2.0 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• Experiment Recommendations & Insights — 5.5 / 6. Its assigned score was above the eight-tool average of 3.8 / 6. Haus had the highest score among the eight tools in this category. Test design and success prediction averaged 1 / 1 (criterion 4.3); AI-generated summaries averaged 1 / 1 (criterion 4.4); Conversational AI for experiments averaged 1 / 1 (criterion 4.5).

• Geo Test Analysis — 3.3 / 6. Its assigned score was above the eight-tool average of 2.8 / 6. Synthetic-control geo-test analysis averaged 1 / 1 (criterion 1.1); The self-serve results interface averaged 1 / 1 (criterion 1.2); Launching tests from the platform averaged 0.75 / 1 (criterion 1.5).

• Enterprise-Grade Platform — 2.0 / 5. Its assigned score was below the eight-tool average of 2.6 / 5. Independent security assurance averaged 1 / 1 (criterion 7.2); The enterprise-reference threshold averaged 0.75 / 1 (criterion 7.1); Enterprise single sign-on averaged 0.25 / 1 (criterion 7.5).

Where scores were lower

• Conversion Lift Test Analysis — 0.5 / 5. Its assigned score was below the eight-tool average of 1.0 / 5. Conversion lift results ingestion averaged 0.5 / 1 (criterion 3.1); The conversion lift dashboard averaged 0 / 1 (criterion 3.2); Direct API ingestion averaged 0 / 1 (criterion 3.3).

• A/B Test Analysis for Owned Media — 0.5 / 4. Its assigned score was below the eight-tool average of 0.9 / 4. Owned-media A/B-test analysis averaged 0.25 / 1 (criterion 2.1); The results interface averaged 0.25 / 1 (criterion 2.2); The media-counterfactual criterion averaged 0 / 1 (criterion 2.3).

• Unified Experiment Library — 0.5 / 3. Its assigned score was below the eight-tool average of 1.0 / 3. Coverage of all three experiment types averaged 0.5 / 1 (criterion 5.1); Library filtering averaged 0 / 1 (criterion 5.2); Library governance averaged 0 / 1 (criterion 5.3).

Research summary

Haus received the fourth-highest total score and the highest assigned score for experiment recommendations and insights. Its geo-test score also exceeded the eight-tool average. Conversion lift analysis, owned-media A/B-test analysis, and the unified experiment library were its lowest-scoring categories as a share of available points.

5. Recast (research score: 12.3 out of 32)

Research overview

Recast received an average research score of 12.3 out of 32, the fifth-highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 8, 2026, across 32 criteria in seven categories.

Category scorecard

Recast: scores assigned in this researchAverage and highest scores cover all eight tools, including Recast. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	Recast	Average research score	Highest research score
1. Geo Test Analysis	2.8 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	0.8 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	1.5 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	3.3 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	0.8 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	1.3 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	2.0 / 5	2.6 / 5	4.8 / 5
Total score out of 32	12.3 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe Recast’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

RecastEight-tool averageHighest research score

1. Geo Test Analysis

2.8 / 6

2. A/B Test Analysis for Owned Media

0.8 / 4

3. Conversion Lift Test Analysis

1.5 / 5

4. Experiment Recommendations & Insights

3.3 / 6

5. Unified Experiment Library

0.8 / 3

6. MMM Integration

1.3 / 3

7. Enterprise-Grade Platform

2.0 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• Experiment Recommendations & Insights — 3.3 / 6. Its assigned score was below the eight-tool average of 3.8 / 6. Recommendations for control and test groups averaged 1 / 1 (criterion 4.2); Test design and success prediction averaged 1 / 1 (criterion 4.3); MCP access to experiment results averaged 0.75 / 1 (criterion 4.6).

• Geo Test Analysis — 2.8 / 6. Using unrounded scores, it was slightly below the eight-tool average; both display as 2.8 / 6 after rounding. Synthetic-control geo-test analysis averaged 1 / 1 (criterion 1.1); The self-serve results interface averaged 1 / 1 (criterion 1.2); Launching tests from the platform averaged 0 / 1 (criterion 1.5).

• MMM Integration — 1.3 / 3. Its assigned score was below the eight-tool average of 1.6 / 3. User-controlled prior calibration averaged 0.75 / 1 (criterion 6.1); The interface connecting experiments to MMM averaged 0.5 / 1 (criterion 6.2); Comparability of experiment-based and attribution-based priors averaged 0 / 1 (criterion 6.3).

Where scores were lower

• A/B Test Analysis for Owned Media — 0.8 / 4. Its assigned score was below the eight-tool average of 0.9 / 4. Owned-media A/B-test analysis averaged 0.5 / 1 (criterion 2.1); The results interface averaged 0.25 / 1 (criterion 2.2); The media-counterfactual criterion averaged 0 / 1 (criterion 2.3).

• Unified Experiment Library — 0.8 / 3. Its assigned score was below the eight-tool average of 1.0 / 3. Coverage of all three experiment types averaged 0.5 / 1 (criterion 5.1); Library filtering averaged 0 / 1 (criterion 5.2); Library governance averaged 0.25 / 1 (criterion 5.3).

• Conversion Lift Test Analysis — 1.5 / 5. Its assigned score was above the eight-tool average of 1.0 / 5. Conversion lift results ingestion averaged 0.75 / 1 (criterion 3.1); The conversion lift dashboard averaged 0.75 / 1 (criterion 3.2); Daily snapshots of conversion lift progress averaged 0 / 1 (criterion 3.5).

Research summary

Recast received the fifth-highest total score. Experiment recommendations and insights was its strongest category as a share of available points, although the score was below the eight-tool average. Conversion lift analysis was among its lower normalized scores but ranked second among the eight tools, illustrating why a vendor’s own score profile and its relative position can tell different stories.

6. LiftLab (research score: 12.0 out of 32)

Research overview

LiftLab received an average research score of 12.0 out of 32, the sixth-highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 8, 2026, across 32 criteria in seven categories.

Category scorecard

LiftLab: scores assigned in this researchAverage and highest scores cover all eight tools, including LiftLab. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	LiftLab	Average research score	Highest research score
1. Geo Test Analysis	2.0 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	0.0 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	0.5 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	3.8 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	1.0 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	1.0 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	3.8 / 5	2.6 / 5	4.8 / 5
Total score out of 32	12.0 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe LiftLab’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

LiftLabEight-tool averageHighest research score

1. Geo Test Analysis

2.0 / 6

2. A/B Test Analysis for Owned Media

0.0 / 4

3. Conversion Lift Test Analysis

0.5 / 5

4. Experiment Recommendations & Insights

3.8 / 6

5. Unified Experiment Library

1.0 / 3

6. MMM Integration

1.0 / 3

7. Enterprise-Grade Platform

3.8 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• Enterprise-Grade Platform — 3.8 / 5. Its assigned score was above the eight-tool average of 2.6 / 5. Independent security assurance averaged 1 / 1 (criterion 7.2); Dual-cloud options averaged 0.5 / 1 (criterion 7.4); Enterprise single sign-on averaged 1 / 1 (criterion 7.5).

• Experiment Recommendations & Insights — 3.8 / 6. Using unrounded scores, it was slightly below the eight-tool average; both display as 3.8 / 6 after rounding. Recommendations about what to test averaged 1 / 1 (criterion 4.1); Recommendations for control and test groups averaged 1 / 1 (criterion 4.2); Test design and success prediction averaged 0.75 / 1 (criterion 4.3).

Where scores were lower

• A/B Test Analysis for Owned Media — 0.0 / 4. Its assigned score was below the eight-tool average of 0.9 / 4. Owned-media A/B-test analysis averaged 0 / 1 (criterion 2.1); The results interface averaged 0 / 1 (criterion 2.2); The media-counterfactual criterion averaged 0 / 1 (criterion 2.3).

• Conversion Lift Test Analysis — 0.5 / 5. Its assigned score was below the eight-tool average of 1.0 / 5. Conversion lift results ingestion averaged 0.5 / 1 (criterion 3.1); The conversion lift dashboard averaged 0 / 1 (criterion 3.2); Direct API ingestion averaged 0 / 1 (criterion 3.3).

Geo-test analysis, the unified experiment library, and MMM integration each received one-third of the available points. They sit between LiftLab’s two higher-scoring categories and its owned-media A/B and conversion lift categories.

Research summary

LiftLab received the sixth-highest total score and tied with Lifesight for the second-highest enterprise-category score. Enterprise requirements and experiment recommendations were its strongest normalized categories. Its owned-media A/B-test and conversion lift scores were below the eight-tool averages, based on the public evidence found under the methodology.

7. Analytic Partners (research score: 8.5 out of 32)

Research overview

Analytic Partners received an average research score of 8.5 out of 32, the seventh-highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 9, 2026, across 32 criteria in seven categories.

Category scorecard

Analytic Partners: scores assigned in this researchAverage and highest scores cover all eight tools, including Analytic Partners. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	Analytic Partners	Average research score	Highest research score
1. Geo Test Analysis	1.8 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	1.0 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	0.0 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	2.5 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	0.8 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	0.8 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	1.8 / 5	2.6 / 5	4.8 / 5
Total score out of 32	8.5 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe Analytic Partners’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

Analytic PartnersEight-tool averageHighest research score

1. Geo Test Analysis

1.8 / 6

2. A/B Test Analysis for Owned Media

1.0 / 4

3. Conversion Lift Test Analysis

0.0 / 5

4. Experiment Recommendations & Insights

2.5 / 6

5. Unified Experiment Library

0.8 / 3

6. MMM Integration

0.8 / 3

7. Enterprise-Grade Platform

1.8 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• Experiment Recommendations & Insights — 2.5 / 6. Its assigned score was below the eight-tool average of 3.8 / 6. Recommendations about what to test averaged 0.5 / 1 (criterion 4.1); Recommendations for control and test groups averaged 1 / 1 (criterion 4.2); Test design and success prediction averaged 1 / 1 (criterion 4.3).

• Enterprise-Grade Platform — 1.8 / 5. Its assigned score was below the eight-tool average of 2.6 / 5. The enterprise-reference threshold averaged 0.75 / 1 (criterion 7.1); Independent security assurance averaged 1 / 1 (criterion 7.2); Enterprise single sign-on averaged 0 / 1 (criterion 7.5).

• Geo Test Analysis — 1.8 / 6. Its assigned score was below the eight-tool average of 2.8 / 6. Synthetic-control geo-test analysis averaged 0.5 / 1 (criterion 1.1); The self-serve results interface averaged 1 / 1 (criterion 1.2); Launching tests from the platform averaged 0.25 / 1 (criterion 1.5).

Where scores were lower

• Conversion Lift Test Analysis — 0.0 / 5. Its assigned score was below the eight-tool average of 1.0 / 5. Conversion lift results ingestion averaged 0 / 1 (criterion 3.1); The conversion lift dashboard averaged 0 / 1 (criterion 3.2); Direct API ingestion averaged 0 / 1 (criterion 3.3).

• A/B Test Analysis for Owned Media — 1.0 / 4. Its assigned score was above the eight-tool average of 0.9 / 4. Owned-media A/B-test analysis averaged 0.5 / 1 (criterion 2.1); The results interface averaged 0.5 / 1 (criterion 2.2); The media-counterfactual criterion averaged 0 / 1 (criterion 2.3).

• MMM Integration — 0.8 / 3. Its assigned score was below the eight-tool average of 1.6 / 3. User-controlled prior calibration averaged 0.25 / 1 (criterion 6.1); The interface connecting experiments to MMM averaged 0.5 / 1 (criterion 6.2); Comparability of experiment-based and attribution-based priors averaged 0 / 1 (criterion 6.3).

Owned-media A/B-test analysis, the unified experiment library, and MMM integration each received 25% of the available points. The A/B-test score was nevertheless above the eight-tool average and the second-highest in that category.

Research summary

Analytic Partners received the seventh-highest total score. Experiment recommendations and insights was its strongest normalized category, while its owned-media A/B-test score ranked second among the eight tools. Conversion lift analysis received zero evidence credit across all five criteria. These are findings from the specified public-documentation assessment, not a measure of the full scope of its consulting or measurement services.

8. Meridian GeoX (research score: 5.8 out of 32)

Research overview

Meridian GeoX received an average research score of 5.8 out of 32, the eighth-highest total in this eight-tool comparison. Claude and ChatGPT each assessed its public documentation on September 8, 2026, across 32 criteria in seven categories.

Category scorecard

Meridian GeoX: scores assigned in this researchAverage and highest scores cover all eight tools, including Meridian GeoX. Calculations use each tool’s unrounded average of Claude and ChatGPT scores; displayed scores are rounded to one decimal. The total row compares complete tool totals.

Category	Meridian GeoX	Average research score	Highest research score
1. Geo Test Analysis	1.5 / 6	2.8 / 6	4.0 / 6
2. A/B Test Analysis for Owned Media	0.0 / 4	0.9 / 4	3.5 / 4
3. Conversion Lift Test Analysis	0.0 / 5	1.0 / 5	4.5 / 5
4. Experiment Recommendations & Insights	3.0 / 6	3.8 / 6	5.5 / 6
5. Unified Experiment Library	0.0 / 3	1.0 / 3	2.0 / 3
6. MMM Integration	1.3 / 3	1.6 / 3	3.0 / 3
7. Enterprise-Grade Platform	0.0 / 5	2.6 / 5	4.8 / 5
Total score out of 32	5.8 / 32	13.7 / 32	25.0 / 32

Categories contain different numbers of criteria. The higher- and lower-scoring groups below describe Meridian GeoX’s own score profile as a share of available points. A lower score within that profile can still be above the eight-tool average. See the scoring methodology for how to interpret the results.

Meridian GeoXEight-tool averageHighest research score

1. Geo Test Analysis

1.5 / 6

2. A/B Test Analysis for Owned Media

0.0 / 4

3. Conversion Lift Test Analysis

0.0 / 5

4. Experiment Recommendations & Insights

3.0 / 6

5. Unified Experiment Library

0.0 / 3

6. MMM Integration

1.3 / 3

7. Enterprise-Grade Platform

0.0 / 5

0%25%50%75%100%

Percentage of available points

Bars show the tool’s average Claude and ChatGPT score as a percentage of the category maximum. Markers show the average and highest scores across all eight tools. Positions use unrounded scores; labels show points rounded to one decimal.

Where Claude and ChatGPT assigned higher scores

• Experiment Recommendations & Insights — 3.0 / 6. Its assigned score was below the eight-tool average of 3.8 / 6. Recommendations about what to test averaged 1 / 1 (criterion 4.1); Recommendations for control and test groups averaged 1 / 1 (criterion 4.2); Test design and success prediction averaged 1 / 1 (criterion 4.3).

• MMM Integration — 1.3 / 3. Its assigned score was below the eight-tool average of 1.6 / 3. User-controlled prior calibration averaged 1 / 1 (criterion 6.1); The interface connecting experiments to MMM averaged 0.25 / 1 (criterion 6.2); Comparability of experiment-based and attribution-based priors averaged 0 / 1 (criterion 6.3).

• Geo Test Analysis — 1.5 / 6. Its assigned score was below the eight-tool average of 2.8 / 6. Synthetic-control geo-test analysis averaged 0.5 / 1 (criterion 1.1); The self-serve results interface averaged 0 / 1 (criterion 1.2); The media-spend counterfactual averaged 0.5 / 1 (criterion 1.3).

Where scores were lower

• A/B Test Analysis for Owned Media — 0.0 / 4. Its assigned score was below the eight-tool average of 0.9 / 4. Owned-media A/B-test analysis averaged 0 / 1 (criterion 2.1); The results interface averaged 0 / 1 (criterion 2.2).

• Conversion Lift Test Analysis — 0.0 / 5. Its assigned score was below the eight-tool average of 1.0 / 5. Conversion lift results ingestion averaged 0 / 1 (criterion 3.1); Direct API ingestion averaged 0 / 1 (criterion 3.3).

• Unified Experiment Library — 0.0 / 3. Its assigned score was below the eight-tool average of 1.0 / 3. Coverage of all three experiment types averaged 0 / 1 (criterion 5.1); Library governance averaged 0 / 1 (criterion 5.3).

• Enterprise-Grade Platform — 0.0 / 5. Its assigned score was below the eight-tool average of 2.6 / 5. Independent security assurance averaged 0 / 1 (criterion 7.2); US and EU data residency averaged 0 / 1 (criterion 7.3); Enterprise single sign-on averaged 0 / 1 (criterion 7.5).

All criteria in these four lower-scoring categories received zero evidence credit. Meridian GeoX is an open-source library evaluated here against the same workflow and enterprise-platform criteria as commercial tools. Those requirements do not measure the quality of its statistical methods or the controls an organization might implement around its own deployment.

Research summary

Meridian GeoX received the eighth-highest total score. Its strongest normalized category was experiment recommendations and insights, followed by MMM integration and geo-test analysis. Its narrower open-source scope matters when interpreting the four zero-scoring categories; the results should not be generalized into a judgment of the underlying methods.

Frequently Asked Questions

1. What is incrementality testing, and how does it differ from MMM?

Incrementality testing estimates the effect of a marketing intervention using treatment and control groups or a suitable counterfactual. MMM estimates marketing effects from historical time-series data. Tests can inform a specific decision and provide evidence for model calibration; neither approach makes uncertainty disappear.

2. Which types of tests does this comparison cover?

The criteria cover geo tests, A/B tests for owned media such as catalogs or email, and analysis of platform-run conversion lift tests. These workflows differ in how treatment is assigned and how results are collected. Not every test produces an iROAS estimate, and a test can be inconclusive.

3. Which tools received the highest research scores?

Sellforte received the highest average total at 25.0 / 32, followed by Lifesight at 18.0, Measured at 15.0, Haus at 13.3, Recast at 12.3, LiftLab at 12.0, Analytic Partners at 8.5, and Meridian GeoX at 5.8. Sellforte led six categories; Haus led experiment recommendations and insights. These are public-evidence research scores, not hands-on product ratings.

4. Why does a unified experiment library matter?

A library can help teams find prior experiments, compare results, and manage access across channels and regions. This evaluation separately scores coverage of the three test types, filtering, and governance. Ask vendors to demonstrate the exact test types and controls your team needs.

5. How can experiments inform MMM calibration?

Experiment estimates can inform model priors. Their relevance depends on the tested channel, population, period, and spending level, as well as uncertainty in the experiment and its transfer to the model. The evaluation distinguishes prior-based calibration, a linking interface, and comparability with attribution-based priors.

6. What should buyers look for in an incrementality testing tool?

Start with your test types, analysis needs, data sources, and operating model. Then assess test design, result ingestion, the experiment library, MMM integration, and governance. Use the criterion-level results to prepare demonstrations and questions. Verify current functionality, implementation effort, pricing, and support directly.

7. How should a zero or partial score be interpreted?

A zero means Claude and ChatGPT found no public evidence under the stated methodology. It does not prove that a feature is absent. Each model assigned 0, 0.5, or 1; averaging their assessments can produce 0.25 or 0.75. Displayed scores are rounded, while calculations use unrounded values.

8. How does an open-source library compare with a commercial platform?

Meridian GeoX is assessed against the same criteria as the seven commercial tools. Requirements such as a hosted user interface, a unified library, and enterprise controls can produce low scores for a library with a narrower purpose. Teams should separately assess the engineering work and operating responsibilities of their intended deployment.

Change log

2026 May 7. Research was launched

2026 June 15. Research methodology was updated. We changed from Sellforte-based scoring to LLM-based scoring to minimize author bias. Evaluation for all vendors was updated.

2026 September 9. Research scoring was updated: all vendors were re-evaluated using the latest information on their website. Analytic Partners, Lifesight and Liftlab were added as new vendors.

Evaluation Dates by Vendor and LLM

The table records the model versions and completion dates for the current evaluation. Fourteen assessments were completed on September 8, 2026; the two Analytic Partners assessments were completed on September 9.

Limitations & Disclosures

Author affiliation. Sellforte designed and published this comparison and is one of the tools evaluated. Claude and ChatGPT assigned the scores, but this does not remove the conflict of interest or potential bias in criterion selection, source discovery, interpretation, and presentation.

Evidence and product scope. This is a public-documentation assessment of seven commercial tools and one open-source library, not a hands-on product test or an exhaustive market survey. It does not measure pricing, implementation quality, service quality, customer outcomes, or the accuracy of the statistical methods in production. A zero score records missing public evidence under the methodology.

Snapshot in time. The current results use assessments completed September 8–9, 2026. Documentation and products may change. The evaluation log identifies each model and date, and the methodology explains aggregation and rounding.

Interpretation of totals. Each of the 32 criteria has equal weight, so categories with more criteria contribute more to the total. The same framework may fit some products and buyer needs better than others. Category averages include Meridian GeoX and should not be read as market-wide adoption statistics.

Corrections. Vendors can submit documentation supporting a correction or re-evaluation to research@sellforte.com. Review the source evidence and model rationales in the scoring workbook when investigating a disputed score.

Buyer decisions. Use the comparison to prepare vendor questions and demonstrations. Verify your required workflows, deployment options, integrations, governance, support, and costs directly before selecting a product.

Further Reading & Resources

Methodology

• Calibrating Marketing Mix Models with Experiments and Attribution data
• Advertising response curves: What are they and why do you need them?
• What is Causal Marketing Mix Modeling (MMM)?
• Understanding R2 in Marketing Mix Modeling: A Guide for Marketers
• MMM for Ecommerce: How Marketing Mix Modeling (MMM) Works for Online DTC Brands
• What does "Enterprise-Grade" Mean in Marketing Mix Modeling (MMM)?
• What is Incrementality Testing? Guide for Marketers
• Marginal Incremental ROAS (miROAS): What is it? And why does it matter to marketers?
• How Much Ad Spend Is Needed for Marketing Mix Modeling?

AI and Agents in Marketing Measurement

• The Rise of Agentic MMM (Marketing Mix Modeling): How AI Is Transforming Media Optimization
• State of AI in MMM: Only 13% of MMM Vendors Implementing AI
• Webinar: Agentic MMM in Action: The Future of Autonomous Media Planning and Buying in Real Time

Use-cases on media spend optimization

• 11 Benefits of Marketing Mix Modeling (MMM) Every Marketer Should Know
• 6 Reasons eCommerce & DTC Brands Should Use Marketing Mix Modeling (MMM)
• The Shift in Marketing Mix Modeling: Why Campaign-Level Optimization is Taking Over
• The Five Requirements of Autonomous Media Buying and Optimization: M.A.G.I.C.
• Bid Optimization: How to Calculate the Optimal Bid Values for Your Campaigns Using miROAS
• ROAS, iROAS, miROAS: Choosing the Right KPI for Optimizing Media Spend

Original Marketing Measurement Research by Sellforte Labs

• The 3 Danger Zones of Last-Click (And How to Avoid Them)
• How to measure Meta correctly? GA4 vs. MTA vs. MMM
• How to Measure the True Effectiveness of TikTok Ads?
• From Last-click to Marketing Mix Modeling (MMM): Unlock +6.5% more sales
• The Missing 24%: Why MMM Without Promotions Behaves Like Last-Click

Practical hands-on guides

• How to Integrate Experiments Into an MMM Platform: A Practical Guide
• MMM Pilot Best Practices: 4 Steps to Plan, Run & Scale Your Marketing Mix Modeling Pilot

Asked by Marketers: Practical incrementality questions

• How should we prioritize incrementality tests across markets and channels?
• What is the right holdout-group size for an incrementality test?
• Should we run a full marketing blackout or a partial holdout test?
• When is a market or channel too small to justify an incrementality test?
• Can you trust Meta Conversion Lift tests for MMM calibration?
• How should geo-lift and conversion-lift studies be combined when calibrating MMM?
• How should we weight multiple incrementality tests when calibrating an MMM?

Marketing Measurement Tools, software and vendors

Evaluation guides:

• How to Choose an Incrementality Testing Tool: 31 Evaluation Criteria
• How to Choose an AI Tool for MMM and Incrementality Testing: 49 Evaluation Criteria

AI tools for MMM and incrementality testing:

• Best Conversational AI Tools for MMM and Incrementality Testing in 2026: An In-Depth Comparison

MMM tools, software and vendors for Ecommerce

• Best MMM Tools for Ecommerce Brands: Top 10 Software for 2026
• Best Real-Time MMM Tools for Ecommerce Brands: Software That Delivers Instant Marketing Insights
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Review Sellforte SaaS Product Features

• Visit Sellforte demo (no sign-up required): Sellforte demo
• Sellforte Experiment product page

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.

Kacper Solarski is a Lead Data Scientist at Sellforte, focused on developing Sellforte's Experiments product. Kacper is one of the most senior data scientists and developers at Sellforte, where he has implemented Marketing Mix Models and incrementality testing solutions to Sellforte customers, while at the same time developing Sellforte's platform. Follow Kacper in LinkedIn.

Juha Nuutinen is the Chief Executive Officer and co-founder at Sellforte, with over 15 years of experience in optimizing marketing spend and promotional activity for the largest advertisers in the world. Before co-founding Sellforte, he worked as a management consultant at the Boston Consulting Group, specializing in promotion optimization. Follow Juha in LinkedIn, where he is actively sharing his views on marketing measurement.