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Average price: 12 products listed
Avg rating
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Price range
$49–$49/mo
Free options
8 tools
New this quarter
12 added
Akkio is an AI product in the Data Analysis Agents category. Generative BI for agencies and teams. This directory profile is based on publicly available information and is unclaimed — if you represent Akkio, you can claim it to add full details, pricing plans, and media. Compare Akkio with alternatives on Saaskart.
Deployment
SAS Viya is an AI product in the Predictive Analytics category. Cloud analytics and predictive AI. This directory profile is based on publicly available information and is unclaimed — if you represent SAS Viya, you can claim it to add full details, pricing plans, and media. Compare SAS Viya with alternatives on Saaskart.
Deployment
H2O.ai is an AI product in the Predictive Analytics category. Open-source and enterprise AutoML. This directory profile is based on publicly available information and is unclaimed — if you represent H2O.ai, you can claim it to add full details, pricing plans, and media. Compare H2O.ai with alternatives on Saaskart.
Deployment
Altair RapidMiner is an AI product in the Predictive Analytics category. Data science and predictive modeling. This directory profile is based on publicly available information and is unclaimed — if you represent Altair RapidMiner, you can claim it to add full details, pricing plans, and media. Compare Altair RapidMiner with alternatives on Saaskart.
Deployment
Alteryx is an AI product in the Predictive Analytics category. Analytics automation and prediction. This directory profile is based on publicly available information and is unclaimed — if you represent Alteryx, you can claim it to add full details, pricing plans, and media. Compare Alteryx with alternatives on Saaskart.
Deployment
Dataiku is an AI product in the MLOps category. End-to-end platform for data and AI. This directory profile is based on publicly available information and is unclaimed — if you represent Dataiku, you can claim it to add full details, pricing plans, and media. Compare Dataiku with alternatives on Saaskart.
Deployment
Tellius is an AI product in the Data Analysis Agents category. AI-driven decision intelligence. This directory profile is based on publicly available information and is unclaimed — if you represent Tellius, you can claim it to add full details, pricing plans, and media. Compare Tellius with alternatives on Saaskart.
Deployment
Sisense is an AI product in the Predictive Analytics category. Embedded analytics with predictions. This directory profile is based on publicly available information and is unclaimed — if you represent Sisense, you can claim it to add full details, pricing plans, and media. Compare Sisense with alternatives on Saaskart.
Deployment
Pecan AI is an AI product in the Data Analysis Agents category. Predictive analytics without data science. This directory profile is based on publicly available information and is unclaimed — if you represent Pecan AI, you can claim it to add full details, pricing plans, and media. Compare Pecan AI with alternatives on Saaskart.
Deployment
Obviously AI is an AI product in the Predictive Analytics category. No-code predictive analytics. This directory profile is based on publicly available information and is unclaimed — if you represent Obviously AI, you can claim it to add full details, pricing plans, and media. Compare Obviously AI with alternatives on Saaskart.
Deployment
Qlik AutoML is an AI product in the Predictive Analytics category. Predictive analytics in Qlik Cloud. This directory profile is based on publicly available information and is unclaimed — if you represent Qlik AutoML, you can claim it to add full details, pricing plans, and media. Compare Qlik AutoML with alternatives on Saaskart.
Deployment
DataRobot is an AI product in the MLOps category. Enterprise AI and MLOps platform. This directory profile is based on publicly available information and is unclaimed — if you represent DataRobot, you can claim it to add full details, pricing plans, and media. Compare DataRobot with alternatives on Saaskart.
Deployment
Saaskart Market Grid™
Explore how leading Predictive Analytics solutions compare based on customer satisfaction, market presence, adoption, and buyer feedback. The Market Grid helps you identify category leaders, high-performing solutions, and emerging products within the Predictive Analytics ecosystem.
Category Leader
Tellius
#1 in Predictive Analytics
Best Value Predictive Analytics
Akkio
From $49/mo
Trending
Tellius
Most viewed
Market Insights
Derived from live Saaskart marketplace data — engagement, reviews, and pricing for this category.
Live Rankings
Predictive analytics software uses machine learning and statistics to forecast outcomes and behavior from data — predicting churn, demand, risk, and revenue so teams act proactively. This guide explains what predictive analytics is, how it works, what matters, and how to choose one.
Predictive analytics software uses machine learning and statistics to forecast outcomes and behavior from data — predicting churn, demand, risk, and revenue so teams act proactively. This guide explains what predictive analytics is, how it works, what matters, and how to choose one.
Predictive analytics software analyzes historical and current data to forecast future outcomes — customer churn, demand, sales, risk, equipment failure, and more — using machine learning and statistical models.
It spans no-code/automated predictive platforms, data-science tools for building custom models, and predictive features embedded in CRM, marketing, finance, and operations software.
The category turns data into foresight for proactive decisions. Buyers weigh prediction accuracy and explainability, data requirements and integration, ease of use (for business vs. data-science users), and how predictions are operationalized into action.
Predictive analytics ingests historical data, identifies patterns, trains models to predict a target outcome, and applies them to new data to produce forecasts and scores — which feed dashboards, alerts, or automated actions.
Platforms combine data integration, automated or custom model building, evaluation, and deployment, with explainability and integration into business systems.
Teams connect data and define the outcome to predict, build or auto-generate models, validate accuracy, and operationalize predictions into workflows, monitoring and retraining as data changes.
AutoML and no-code tools build predictive models without deep data-science expertise.
Predict outcomes (churn, demand, risk, LTV) and score records for prioritization.
Explain which factors drive predictions for trust and actionability.
Connect to your data sources and warehouse for training and scoring.
Deliver predictions via dashboards, alerts, APIs, or automated actions.
Track accuracy and retrain as data and conditions change.
Anticipate churn, demand, and risk and act before outcomes happen.
Scores prioritize where to focus effort for the most impact.
Data-driven forecasts improve planning and reduce surprises.
Predict failures, fraud, and risk to prevent costly events.
No-code tools bring prediction to teams without data scientists.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| No-code/AutoML platforms | Predictions without data science | SMB to enterprise | Accessible, fast | Less control than custom |
| Data-science platforms | Custom predictive models | Teams with data scientists | Full control and accuracy | Expertise required |
| Embedded predictive features | Predictions inside business apps | Any | In-context, easy | Limited to that app's data |
| Domain predictive solutions | Churn, demand, risk, maintenance | Any | Purpose-built | Narrower scope |
Technology: Technology teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Healthcare: Healthcare teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Financial Services: Financial Services teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Retail & E-commerce: Retail & E-commerce teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Education: Education teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Professional Services: Professional Services teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Manufacturing: Manufacturing teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Media: Media teams use predictive analytics to forecast churn, demand, risk, and revenue, prioritize with scores, and act proactively — turning historical data into foresight for better decisions.
Test prediction accuracy on your data with proper validation, not just training performance.
Confirm the tool explains prediction drivers so teams trust and act on results.
Assess how much clean historical data is needed and whether yours suffices.
Match the tool to your users — business teams need no-code; data scientists want control.
Verify predictions flow into dashboards, alerts, or actions in your systems.
Understand seat, usage, or platform pricing and how it scales.
AutoML and generative interfaces are making predictive analytics accessible to more business users.
Predictions are increasingly operationalized into automated, agentic actions.
Explainability and uncertainty quantification are improving trust and decisions.
Buyers should prioritize accuracy and validation, explainability, data fit, and operationalization into action.
Predictive analytics uses machine learning and statistical models to forecast future outcomes from historical and current data — predicting customer churn, demand, sales, risk, equipment failure, and more. The software spans no-code/AutoML platforms, data-science tools for custom models, and predictive features embedded in CRM, marketing, finance, and operations software, turning data into foresight for proactive decisions.
Accuracy depends heavily on data quality and quantity, the predictability of the outcome, and proper model validation. Good models on solid data can be highly useful, but predictions carry uncertainty and can degrade as conditions change. Validate accuracy on held-out data, monitor over time, and treat forecasts as informed probabilities, not certainties.
Not necessarily. No-code and AutoML platforms let business users build and use predictive models without deep expertise, which is enough for many use cases. Data-science platforms offer more control and accuracy for complex problems but require expertise. Choose based on your team's skills and the complexity of what you're predicting.
Common applications include customer churn, lifetime value, and conversion; demand and sales forecasting; credit and fraud risk; predictive maintenance (equipment failure); and inventory and staffing needs. Essentially, any outcome with enough relevant historical data to learn from can be a candidate — feasibility depends on data quality and signal.
Teams need to understand why a model predicts an outcome to trust it and act appropriately — for example, which factors drive churn so you can intervene. Explainability also helps detect bias and errors. Black-box predictions are harder to act on and riskier, so favor tools that explain prediction drivers.
Predictions are operationalized by delivering scores and forecasts into dashboards, alerts, CRM/marketing systems, or automated workflows so teams or systems act on them. Operationalization is often harder than model-building, so confirm how a tool integrates predictions into your systems and processes, not just how it generates them.
Reputable vendors offer encryption, access controls, and compliance; confirm whether your data is used to train shared models and how it's processed. Since predictive analytics uses potentially sensitive historical and customer data, review data handling, residency, and governance before adopting.
Prioritize prediction accuracy with proper validation on your data, explainability, fit with your data requirements and quality, ease of use for your users (no-code vs. data science), operationalization into your workflows, integration, and pricing. Pilot on a real prediction problem and validate accuracy and actionability before scaling.
