Meridian vs. Sellforte MMM SaaS: The Complete Comparison for 2025

Most data-driven marketing teams are currently looking to add Marketing Mix Modeling (MMM) to their toolkit. When comparing different MMM solutions, we often see businesses considering between an MMM SaaS, such as Sellforte, and building the MMM capability from scratch with Google's open-source modeling library Meridian.

In this comparison article, our objective is to help marketing teams and data scientists understand key differences between Sellforte and Meridian, so that they are equipped with facts when choosing their approach.

End-to-End Solution vs. Modeling Library

Before we dive into the details, it's important to highlight that comparing Sellforte and Meridian is not an apples-to-apples comparison. Meridian is a modeling library, whereas Sellforte is an end-to-end SaaS measuring marketing performance. The picture below illustrates the differences in Sellforte's and Meridian's scope:

Meridian is a Bayesian Marketing Mix Modeling (MMM) library. Bayesian modeling is broadly considered as the golden standard for MMM. While Sellforte also has Bayesian Marketing Mix Modeling core, Sellforte provides all the other elements of an end-to-end MMM SaaS as well:

• Before modeling:
  • In-built data connectors to advertising platforms (Meta, Google, Tiktok, Microsoft..), analytics platforms (Google Analytics 4, Adobe Analytics...), ecommerce platforms (Shopify, Magento..) and so on
  • Data processing pipelines, for example harmonizing data, campaign grouping and mapping data to a media hierarchy
  • Algorithms for generating model calibration inputs based on Geo lift tests, Conversion Lift tests, and attribution data
  • Creating modeling features based on the processes data, including base features such as promotions and weather
• After modeling
  • Online user-interface for ROI analysis
  • Online user-interface for optimizing budget allocation
• Service:
  • Customer Success service for data onboarding, as well as analysis and impact delivery
  • Data Science team that ensures modeling quality throughout the pipeline

If a company wants to use Meridian for measuring marketing performance, all the missing components need to be built as well.

With this context, let's now walk through the key differences between Sellforte and Meridian one-by-one.

Key Differences between Sellforte and Meridian: Modeling

While both Sellforte and Meridian use Bayesian Modeling approach, there's several differences in the modeling approach:

1. Modeling on Daily vs. Weekly data

Sellforte: Modeling on Daily data
Meridian: Modeling on Weekly data

One of Sellforte's significant advantages lies in its use of daily data for modeling, while Meridian implementations operate on aggregated weekly data. This difference has implications for modeling accuracy and marketing decision making use-cases that the model supports.

Modeling accuracy: Using daily data dramatically increases the volume of data points available to the model, which means that the model can leverage more variance when estimating how each media affects sales of a business. When working with 365 data points per year instead of 52, the model can detect more easily detect patterns that weekly aggregation might obscure. To illustrate this, below is an example of the same sales data, first aggregated on a weekly level, and then on daily level on the second chart. 

Granular campaign analysis: Daily modeling also enables granular campaign analysis that's impossible  to do with weekly data. Consider Black Friday campaigns, flash sales, or weekend-specific promotions—these high-impact, short-duration activities require daily granularity to measure effectively. Weekly aggregation can mask the performance of these campaigns.

Calendar-month use-cases: Daily modeling also solves a practical business problem: budget planning alignment. Many marketing planning discussions happen on a calendar month-level, and there is no accurate method for transitioning calendar weeks to calendar months. A simple example: if you want to do a media budget comparison between this year's January to last year's January from your MMM data, you cannot do that accurately with weekly data.

2. Automated Daily Updates

Sellforte: Yes
Meridian: No

Another benefit related to daily data, is Sellforte's ability to provide daily model updates, which is not available in Meridian. Daily model updates unlock a major tactical use-case for performance marketing teams: daily tracking of campaign performance.

Despite updating insights daily, Sellforte's models are highly stable, meaning that data scientists don't need check the model after each model update.

Meridian updates on the other hand, are manually triggered, and are typically done by businesses once per month or once per quarter.

3. Customer Type - and Product Category - level Modeling

Sellforte: Provides Customer Type- and Product Category - level Modeling
Meridian: Does not provide this feature

Sellforte's platform includes built-in capability for modeling different customer types (new vs. returning customers) and product categories simultaneously (Women Dresses, Women Shoes, Men Shoes, Men Accessories, … ). This is not available in Meridian.

This multi-dimensional modeling approach addresses one of the most common limitations in traditional MMM: the assumption that all customers and products respond identically to marketing stimuli.

Sellforte supports up to 30 unique combinations of customer types and product categories. This means a retailer can understand impact of their media in a very specific way, for example how Google's Branded Search performs in acquiring new customers in the women's shoes product category. Below is an example from the Sellforte enterprise demo splitting the incremental sales impact of Google Branded Search into different product categories.

4. Advanced Base Variables: Promotions and Weather

Sellforte: Promotions and weather included by default
Meridian: Not typically included

Promotions can drive 20-30% of total sales in eCommerce and 5-20% in Retail, which means that their effect to the sales of a business can be in the same league with media. If promotions are excluded, there's a major risk of attributing promotion-driven sales to media. If measurement allocates promotion-driven sales to media, there's a high chance for inflated ROIs, especially for the channels that are activated simultaneously with promotions. In the end, this leads towards a poorly optimized media allocation.

Below is an example what eCommerce sales decomposition can look like (from the Sellforte eCom demo). This data has 18% of sales driven by promotions.

There are other base features as well, that can have a major influence on sales, such as weather in some industries.

As an eCom/DTC and Retail specialist, Sellforte models include promotions and weather in the default configuration.

While Meridian theoretically offers an opportunity to add control variables for the base, we see companies struggle incorporating them. The reason is that these features require deep understanding of the underlying phenomena to model properly: you can't just plug in avg. price data, promo flag data or temperature data and hope that the model is able to estimate promo and weather impact.

5. Measuring Margin ROI (in addition to Sales ROI)

Sellforte: Yes
Meridian: No

While most MMM solutions focus exclusively on sales or revenue ROI, i.e. how many Dollars/Euros of revenue 1 Dollar/Euro spent in a channel brings back, Sellforte includes margin ROI modeling. This is a critical capability for businesses where product profitability varies significantly across categories or channels. This feature enables cross-platform media optimization based on actual profit contribution rather than just revenue generation.

Meridian's standard implementation doesn't include profit margin ROI capabilities.

6. Calibration with Geo Lift tests, conversion lift tests, and attribution data

Sellforte: Yes, systematic framework and tools for forming prior distributions
Meridian: Limited, Manual input of priors

Model calibration is a method which radically improves the reliability of modeling results, as well as enables making modeling results more granular. In model calibration, the model is given external information about media performance in the form of informative prior distributions. These prior distributions can be formed based on Incrementality tests (Geo lift tests, Conversion lift tests), as well as attribution data (by combining them with incrementality factors).

Below is an illustration of prior distributions in Bayesian modeling, showing the difference between informative priors and non-informative priors. Non-informative priors are the default setting in most Bayesian models. For a full rundown of model calibration, check the Sellforte model calibration guide.

Sellforte's platform has an in-built tool for generating prior distributions based on experimental data (geo-lift tests, conversion lift studies) and attribution data. All Sellforte models are highly robust due to the systematic use of this model calibration approach. In addition, Customer’s data scientist can get full control and transparency in weighting different calibration data sources when formulating the prior means and prior scales.

Meridian's model calibration is more manual: It is possible for the user to input priors, but the user needs to have external tools or analyses to arrive at priors. Meridian lacks a structured workflow for translating external data into appropriate priors. This increases the risk of human error in the process, and in the wrong hands can make the modeling more like a craft, than a systematic data science process.

Key Differences between Sellforte and Meridian: Data input and processing

Key differences include:

1. Connecting or collecting data

Sellforte: Automated data connectors
Meridian: Manual collection of data / self-maintained pipelines

As a modeling library, Meridian itself does not include tools for collecting data, or automatically fetching it from advertising platforms. The only exception is Google data: Meridian website highlights the opportunity to use Google's MMM data platform.

Sellforte has in-built data connectors to all advertising platforms (Meta, Google, Tiktok, Microsoft..), analytics platforms (Google Analytics 4, Adobe Analytics...), ecommerce platforms (Shopify, Magento..) and so on. Integrated and automated data connectors represent a significant advantage in setting up the model, as well as conducting model updates. Below is an image illustrating Sellforte connector view:

2. Data processing

Sellforte: Automated data pipelines
Meridian: Manual collection of data / self-maintained pipelines

Everyone who has worked with marketing data knows that it is can be messy. Sellforte's data processing pipelines are built for preparing and harmonizing marketing and sales data for modeling. Data processing includes for example campaign grouping, so that Meta prospecting, awareness and retargeting campaigns can be modelled separately. It also includes mapping of all marketing data to a standard hierarchy that enables easier analysis later, such as mapping all prospecting campaigns across all social platforms to Paid Social Prospecting.

Meridian does not offer similar data processing capabilities.

Key Differences between Sellforte and Meridian: Historical Performance Analysis

Key differences include:

1. Analysis Format

Sellforte: Interactive online user-interface
Meridian: No user-interface. Static reports generated by data scientists

Meridian does not have a user-interface for easy results analysis. Meridian users have the option exporting data from to a spreadsheet or an external visualization tool like Tableau, or using some of Meridian's in-built plotting tools. As an example, users can call the function Summarizer-class to create following outputs:

Sellforte Dashboard is an interactive user interface that enables dynamic exploration of MMM results with multiple filtering and drill-down capabilities. Users can seamlessly switch between different time granularities (year, month, week, day) and analysis levels (ad platform vs. advertising channel, media group vs. media channel) to uncover insights relevant to their specific needs.

This interactivity transforms MMM from a periodic reporting exercise into an ongoing analytical tool that marketing teams can use for day-to-day decision-making. The ability to quickly explore different scenarios and time periods significantly increases the practical utility of MMM insights. Below is an example of Sellforte Dashboard, which is available for testing in the public demo.

2. ROI and Sales Contribution by channel

Sellforte: Yes, based on standard channel hierarchy
Meridian: Yes, but limited due to a lack of standard channel hierarchy and campaign grouping

Both Sellforte and Meridian offer channel level ROI analysis. Example from Meridian:

Example from Sellforte:

Contrary to Meridian, Sellforte operates based on a best practice standard media hierarchy that defines the channels and their names. As an example Meta campaigns are always grouped to Prospecting, Awareness, Retargeting, Remarketing and so on based on certain rules. Meridian lacks a similar approach, as it does not offer data processing tools.

3. Campaign-Level Analysis

Sellforte: Yes
Meridian: No

Sellforte  provides detailed campaign-level ROI measurement through its Campaign Dashboard, enabling measurement of individual campaigns rather than just channel-level ROIs. Campaign-level analysis bridges the gap between strategic MMM insights and tactical campaign optimization, making the modeling results directly actionable for performance marketing teams. Meridian's standard implementation doesn't provide this level of granular analysis. Below is an example view from Sellforte's Campaign Dashboard with ROI highlighted. 

Key Differences between Sellforte and Meridian:  Optimization Capabilities

Key differences include:

1. Optimizer tool

Sellforte: Interactive online tool
Meridian: Code-based scenario configuration

Sellforte's interactive media optimizer enables real-time scenario planning with immediate feedback on projected outcomes. Users can test different scenarios and instantly see the impact on expected sales, margins, and ROI metrics. Below is an example of Sellforte Optimizer, which is available for testing in the public demo:

Meridian offers scenario configuration through Python code, which requires technical expertise and doesn't enable the rapid iteration that characterizes effective media planning processes. Below is an example code from Meridian documentation for configuring a scenario:

2. Optimizing budget by channel

Sellforte: Yes
Meridian: Yes

Both Meridian and Sellforte provide channel level optimization. Below are example optimization views from Meridian:

Below is an example from the Sellforte demo showing 5 different budget scenarios

3. Forecasting total sales in each scenario

Sellforte: Yes
Meridian: No

Meridian's optimizer provides a sales forecast for the incremental media-driven sales in each scenario, but it does not provide a forecast for total sales of the business. Below is an example from Meridian documentation:

Sellforte provides forecast for total sales of a business, including media-driven sales, promotion-driven sales, and base sales. Total sales forecast makes it possible to compare scenarios to the business plan of the company: Is this budget sufficient for reaching our revenue target? Below is an example from the Sellforte demo:

4. Optimizing budget by week

Sellforte: Yes
Meridian: No

Unlike Meridian, Sellforrte's optimizer also provides the optimal budget allocation by channel for each weekly. This is a key requirement for realistic budget allocation. If the optimizer solution does not understand how a Black Friday week differs from a low season week, the optimization outcome is likely disconnected from reality. Below is an example of optimal weekly allocation from the Sellforte demo:

5. Optimizing campaign spend

Sellforte: Yes
Meridian: No

Sellforte also generates campaign-specific optimization recommendations via the Campaign Dashboard. It helping marketing teams identify which campaigns to scale, and how much.  Below is an example view from Sellforte's Campaign Dashboard with spend recommendation highlighted:

Key Differences between Sellforte and Meridian: Other considerations

Key differences include:

1. Implementation time

Sellforte for eCommerce: 1-2 weeks 
Sellforte for complex omnichannel Retailers: 2-4 months
Meridian: 6-12 months for full implementation

Sellforte's shorter timeline is enabled by integrated data connector and automated modeling pipelines. 

2. Costs

Sellforte: See Sellforte pricing
Meridian: Min. 3-4 FTE team of data scientists, data engineer(s), marketing consultant(s). Cloud costs

While Meridian is open-source and technically "free," the total cost of ownership tells a different story. Meridian implementations require:

• A dedicated team of data scientists for ongoing model development and maintenance
• Data engineering resources for pipeline development and maintenance
• Marketing expert(s) for interpreting results.
• Ongoing infrastructure costs for data, processing and storage

These hidden costs often exceed the transparent pricing of SaaS solutions like Sellforte, particularly when accounting for the opportunity cost of data science resources that could be focused on higher-value analytical projects. A business also needs to also build slack to the team due to vacations, career changes and unexpected situations.

3. Risk

Sellforte: Low
Meridian: High

The risk profiles of these approaches differ significantly:

Sellforte represents a low-risk implementation backed by a battle-tested solution that has demonstrated tangible sales and margin improvements for leading retail, eCommerce, and DTC companies. The platform's proven track record and comprehensive support reduce both technical and business risks.

Meridian implementations carry higher risk. Building an MMM solution from ground up, even if you leverage an open-source library, is a major undertaking in terms of time and cost. We know because we have done the journey of building an MMM from scratch.. It took us millions of dollars and several years in R&D to get to where we are. If a company fails its MMM project or the MMM delivers outputs that don’t lead to sales increases for the company, there is a major risk for the company falling behind competitors, and a career-risk for everyone involved.

Conclusion: Making the Choice

The choice between Sellforte and Meridian ultimately depends on your organization's specific circumstances, technical capabilities, and strategic priorities.

Choose Sellforte if:

• You're planning to increase digital media in your mix, and benefit from tactical use-cases such as campaign-level optimization
• You need rapid time-to-value with MMM insights
• You prefer predictable costs and low implementation risk
• High quality modeling results are important
• Your organization benefits from a user-friendly interface that people broadly in the organization can use

Choose Meridian if:

• You have significant data science and engineering capabilities to deploy on Marketing Mix Modeling
• You have limited data available for modeling and are satisfied with using weekly-level data in the model
• Your media mix is offline media-heavy, and you don't benefit from tactical use-cases such as campaign optimization and finding optimal budget for each week
• You are not in a hurry - you have 6-12 months available for implementation, plus a few years of time to learn how to use MMM in marketing decision making
• Your marketing team is not making high-stakes decisions that would require utmost robustness from the model

Curious to learn more about Sellforte? Book a demo.

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.