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Description

A modeling platform has been created to assist marketing teams, analysts, and agencies in evaluating the genuine incremental effects of their marketing expenditures across various channels. In contrast to conventional attribution tools that depend on cookies and focus solely on digital interactions, MixModeler allows organizations to assess the impact of offline advertising methods such as TV, radio, and outdoor promotions, in conjunction with their digital marketing efforts. This platform merges advanced statistical techniques, such as Bayesian inference and regression analysis, with a user-friendly interface, enabling individuals without a background in data science to engage in sophisticated marketing measurement. Users have the option to either construct custom models manually or take advantage of AI-driven automation, allowing them to rapidly identify which marketing initiatives are yielding tangible business outcomes. Furthermore, MixModeler provides a range of pricing options, starting from free plans tailored for smaller teams to comprehensive solutions for larger enterprises, thereby making high-quality marketing mix modeling accessible to organizations of all sizes and enhancing their strategic decision-making capabilities.

Description

Stella is a platform designed for marketing measurement, providing marketers with robust, scientifically validated insights into which advertisements, campaigns, and media channels effectively contribute to increased revenue. The platform is equipped with three primary tools: Incrementality Testing, Always-On Incrementality, and Media Mix Modeling (MMM). Through Incrementality Testing, Stella conducts geo-holdout studies, also known as inverse holdouts, to evaluate performance differences between test and control areas, effectively isolating the causal effects of advertisements as opposed to relying solely on attribution methods. This tool simplifies complex statistical processes, including causal inference and confidence intervals, allowing users to understand the potential outcomes without a specific campaign, thus uncovering the genuine “lift” attributed to each advertisement. Furthermore, its Media Mix Modeling feature employs a unique Bayesian approach to dissect historical marketing expenditures and various external influences, such as seasonality and promotional events, to assess the contribution of each channel to overall sales effectively. By leveraging these advanced methodologies, Stella empowers marketers to make informed decisions based on accurate data analysis.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

No images available

Screenshots View All

Integrations

Amazon No 
Google Cloud Platform No 
Meta AI No 
Shopify No 
Snowflake No 

Integrations

Amazon Yes 
Google Cloud Platform Yes 
Meta AI Yes 
Shopify Yes 
Snowflake Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

$2,000 per month
Free Trial Yes 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

MixModeler

Founded

2025

Country

United Kingdom

Website

mixmodeler.com

Vendor Details

Company Name

Stella

Country

United States

Website

www.stellaheystella.com

Product Features

Marketing Analytics

A/B Testing No 
Campaign Management No 
Channel Attribution No 
Customer Journey Mapping No 
Dashboard No 
Performance Metrics No 
Predictive Analytics No 
ROI Tracking No 
Social Media Metrics No 
Website Analytics No 

Product Features

Alternatives

Alternatives