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Top 1,830 Predictive Analytics Software SaaS Companies in May 2026

As of May 2026, there are 1,830 SaaS companies in Predictive Analytics Software. They have combined revenues of $19.1B and employ 138.2K people. They have raised $18B and serve 1.3B customers combined.

Predictive Analytics Software refers to a category of applications designed to analyze historical and current data to forecast future events and trends. These tools employ various statistical techniques, machine learning algorithms, and data mining methods to help organizations make informed decisions. Users in fields such as finance, marketing, operations, and supply chain management seek predictive analytics for its ability to enhance decision-making processes through actionable insights. Typical features of predictive analytics software include data integration capabilities, predictive modeling, visualization tools, and reporting functionalities. Common workflows often involve data extraction, preparation, analysis, and reporting, enabling users to identify patterns and make predictions based on factual data. Buyer personas may include business analysts, data scientists, and operational decision-makers who require insights for strategy development and resource allocation.

Companies
1,830
Revenue
$19.1B
Funding
$18B
Employees
138.2K

Filters

Sorting: Highest -> Lowest

Filters

Top Predictive Analytics Software Companies

Showing 10 of 538 companies ranked by annual revenue.

1
Preisenergie

Munich, Bayern, Germany

Preisenergie develops dynamic pricing software for the energy sector that helps increase loads on renewable generation.

Revenue
$1M
Customers
-
Year founded
2016
Funding
-
Team size
1
Growth
-
2
Loominance

San Jose, CA, United States

Whilst there are many players in the ABM and CDP space, these are fundamentally creating new data silos that are not fully integrated into the operation fabric across sales, marketing, and C-level executives. As we have emerged from stealth mode after more than four years, having self-funded on early key customer engagements, we are finding many frustrated and under served customers. The opportunity is that all companies know that they need to move their go to market organizations to an account centric approach. We have a proven and industry leading SaaS solution and are now ready to go to market. We are laser-focused on building, implementing, and supporting an easy-to-use, infinitely scalable automated customer intelligence for our customers. Our solutions are built around core seminal patents, delivering an architecture that adapts to your changing needs and priorities. Companies can target and segment target accounts as needed, avoiding the costs, delays and limited capabilities of building and maintaining complex data science and professional services type engineering projects. These projects are unable to adapt to the continuous changes and are therefore, are limited to professional services type revenue models. Loominance is uniquely bringing a scaled SaaS delivery model that can be adapted to each customer and constantly changing business requirements.

Revenue
$1M
Customers
-
Year founded
2017
Funding
-
Team size
5
Growth
-
3
ProAxion, Inc.

Cary, North Carolina, United States

ProAxion is an Industrial Internet of Things (IIoT) company whose mission is to maximize the up-time and overall competitiveness of industrial manufacturing facilities. The company's technology allows customers to predict and prevent disruptive failures of industrial machines by leveraging the latest in wireless sensor and computing technology.

Revenue
$1M
Customers
-
Year founded
2015
Funding
$1.2M
Team size
10
Growth
-
4
DemandBox

Blaine, Minnesota, United States

DemandBox is a commercial SaaS solution (AI tool) for revenue management: analyzes the sales funnel, identifies deals at risk and suggests next steps to increase conversion and customer retention

Revenue
$1M
Customers
-
Year founded
-
Funding
-
Team size
162
Growth
-
5
AiSolve

Hillsborough, New Jersey, United States

Leverage the power of AI to gain a competitive edge. Our tailored solutions deliver actionable insights, streamline workflows, and unlock new revenue streams. We're your trusted partner in navigating the future of AI.

Revenue
$1M
Customers
-
Year founded
-
Funding
-
Team size
1
Growth
-
6
Patterns

San Francisco, California, United States

Next-Gen Financial Analysis and Reporting with AI Agents

Revenue
$1M
Customers
-
Year founded
2021
Funding
-
Team size
6
Growth
-
7
Evidently AI

San Francisco, California, United States

Open-source monitoring for machine learning models

Revenue
$1M
Customers
-
Year founded
2020
Funding
-
Team size
7
Growth
-
8
Julius

San Francisco, California, United States

AI Data Scientist

Revenue
$1M
Customers
-
Year founded
2022
Funding
-
Team size
5
Growth
-
9
ClearMacro

London, England, United Kingdom

Saas solution & quant tools to support institutional investors in their decision-making processes, covering global asset classes and the macro environment in over sixty countries and sectors

Revenue
$994.8K
Customers
-
Year founded
2014
Funding
-
Team size
5
Growth
92.41%
10
EidoSearch

Boston, Massachusetts, United States

Provider of a predictive analytics technology intended to provide a time series of data. The company's predictive analytics technology uses probability intelligence tool identifies data series from data set, search for multiple disparate patterns simultaneously, find similar conditions and provide relevant results, enabling businesses to get data-based intelligence to uncover opportunities and better estimation of risks.

Revenue
$991.3K
Customers
-
Year founded
2010
Funding
-
Team size
8
Growth
108.37%

Inclusion Criteria

- The software must provide advanced analytics capabilities to analyze historical and current data. - It should include predictive modeling to forecast future trends and behaviors. - The tool must facilitate data visualization to present insights clearly and effectively. - It should integrate with other data systems and sources for comprehensive analysis. - Not just basic reporting; it must include statistical analysis and machine learning features. - User interfaces should be designed for accessibility to business users, not just data experts.