Top 1,831 Predictive Analytics Software SaaS Companies in July 2026
As of July 2026, there are 1,831 SaaS companies in Predictive Analytics Software. They have combined revenues of $20B and employ 138.4K people. They have raised $19.3B 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.
Developer of a cloud-based emotional analytics platform created to measure and understand emotions in real-time. The company's platform combines neuroscience, cognitive technology and machine learning to understand emotions and their impact on learning, health and well-being as well as tracks, predicts and recommends customers based on relevant emotional states, enabling users to receive a new technology of artificial intelligence with empathy to improve their lives.
Developer and provider of data science platform intended for data scientists. The company's SaaS platform which equips data science teams with high-leverage automation tools which include one-click publishing and sharing, scaling to GPUs, flexible custom environments, and eliminating hours of traditional, manual work, enabling companies to perform data science at a new level of scale, with one-click solutions.
Next Gate Tech, a Luxembourg-based FundTech, provides SaaS solutions for asset management with machine learning, data analytics, and cloud-based automation.
Developer of a cloud-based data platform designed to empower businesses with data-driven machine intelligence. The company's platform offers timely, enriched data through a streaming geospatial intelligence engine that provides ready access to enriched and accurate data leads when crop are failing, energy of water usage is excessive or when economic and other intel is needed, enabling clients to take timely strategic decisions while reducing risk and increasing profitability.
Withthegrid is a Dutch company that connects and empowers infrastructure assets, grid operators, energy producers and users through monitoring and flexibility solutions.
Worth AI is a fintech platform that automates onboarding and underwriting for financial institutions, utilizing AI to provide real-time insights and risk management.
InRule Technology® is an AI decisioning company providing integrated decisioning, machine learning and process automation software to the enterprise. By enabling IT and business leaders to make better decisions faster, operationalize machine learning, and improve complex processes, the InRule® Intelligence Automation Platform increases productivity, drives revenue, and provides exceptional business outcomes.
More than 500 organizations worldwide rely on InRule for mission critical applications. InRule Technology has been delivering measurable business and IT results since 2002. Learn how to make automation accessible at www.inrule.com.
Revenue
$9.8M
Customers
-
Year founded
2002
Funding
-
Team size
89
Growth
-
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.
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