9 Best Predictive Analytics Tools

Discover top predictive analytics tools that drive data success, improve decision-making, enhance customer experiences, and optimize business performance.


Predictive analytics is the practice of using historical data to anticipate future trends or outcomes. 

Businesses use a variety of predictive analytics tools to accelerate and simplify the development of accurate predictive models. These tools help companies find new business opportunities and detect risks, and enable a proactive approach to decision-making.

9 best predictive analytics tools

Predictive analytics has become indispensable for any business that wants to turn large data sets into a competitive advantage. Below, we list 9 predictive analytics tools that are used by industry-leading companies.

1. Alteryx Analytics Cloud Platform

Alteryx offers an all-in-one, enterprise-grade Analytics Cloud Platform.

Important features:

  • Supports self-service and beginner users with a predictive analytics starter kit that teaches them how to use linear regression models, A/B analysis, and time series models

  • Provides a single solution for data ingestion, preparation, analysis, and reporting

  • Available for cloud, on-premise, and hybrid deployment


  • From $4,950 for Designer Cloud and $5,195 for Designer Desktop

  • Users outside of the U.S. should reach out to the company directly for pricing information

2. Azure Machine Learning

Microsoft’s Azure Machine Learning platform is an end-to-end solution for data preparation and creating and managing machine learning and artificial intelligence (AI) models.

Important features:

  • Suitable for use by developers and data scientists as you can build predictive models without any programming

  • Includes algorithms (such as linear regression models) you can use on your data to make predictions

  • Provides a responsible AI dashboard to help you make fair and reliable data-driven decisions


  • Pay-as-you-go plans with discounts available for one-year and three-year plans

3. H2O Driverless AI

H2O is an open-source machine learning platform. H2O Driverless AI is the company’s machine learning solution for enterprises.

Important features:

  • Automates many tasks in the process of building predictive models, such as feature engineering

  • Suitable for data scientists, IT professionals, DevOps, and business analysts

  • Includes an AI wizard that helps you follow data science best practices and suggests removing data that could have an adverse impact on the model


4. IBM Watson Studio

IBM Watson Studio is the company’s solution for building and managing AI models. It’s part of the IBM Cloud Pak for Data platform.

Important features:

  • Includes a data refinery to clean and prepare data

  • Allows you to easily build models with a user-friendly drag-and-drop interface

  • Supports Python, Scala, and R programming languages, as well as open-source frameworks, such as scikit-learn, PyTorch, and TensorFlow


  • Pay-as-you-go pricing is available for users of IBM Cloud Pak for Data as a Service 

  • Watson Studio Lite is free

  • Watson Studio professional costs $1.02 per capacity unit-hour

  • Users of IBM Cloud Pak for Data can purchase a license

5. KNIME Analytics Platform

KNIME Analytics is an open-source, free data science platform.

Important features:

  • Connects to over 300 data sources, including data warehouses

  • Features a library of extensions and integrations that allow you to run advanced algorithms and analyze complex data; some extensions and integrations are free, while others are paid

  • Low-code/no-code platform is suitable for beginner users, such as business analysts; experienced users have more advanced machine learning techniques at their disposal


  • Free

6. SAP Analytics Cloud

SAP Analytics Cloud is a unified solution for predictive analytics, business intelligence, and planning.

Important features:

  • Includes natural-language processing that enables you to ask questions and get an immediate response

  • All employees can develop predictions, regardless of technical expertise

  • Supports the simulation of different scenarios and then generates appropriate plans in response


  • Two pricing plans are available: 

    • For Business Intelligence only, the price is $36 per user per month

    • For Planning, the price is available upon request

7. SAS Viya

SAS Viya is an analytics and AI platform for business users, IT teams, and data scientists.

Important features:

  • Allows ML engineers to quickly put predictive analytics models into production with automated deployment and without recoding

  • Stores and monitors models in a single repository for better governance

  • Includes a drag-and-drop dashboard builder for self-service analytics


  • Available upon request

8. RapidMiner Studio

RapidMiner (recently acquired by Altair) is an enterprise data science platform.

Important features:

  • Domain experts can develop predictive models without any code

  • Provides templates for frequent use cases, such as predicting customer churn or fraud

  • Results are available in a custom dashboard, but you can also export and view them in another business intelligence solution of your choice


  • Available upon request

9. TIBCO Data Science

TIBCO Data Science is a predictive analytics platform that can be used by expert and citizen developers.

Important features:

  • Ability to build machine learning modules with drag-and-drop workflows, AutoML, and Jupyter Notebooks

  • Reusable templates for quick data analysis

  • Implements drift monitoring and alerts you when it’s time to retrain models


  • Available upon request

Why are predictive analytics tools important?

In the age of big data, we can’t rely on manual calculations alone. Data sets are massive, and sifting through them can be an arduous, time-consuming task (not to mention the risk for human error). 

Predictive analytics tools are able to simplify and streamline the process of gleaning insights from data, offering a high-degree of accuracy as well as speed. 

Data-driven decision making

“Data-driven decision making” has become a well known phrase across industries – urging businesses to use insights and information to hone their strategies. 

The energy industry is a great example of this practice at work.  Energy providers must predict customer usage to meet demands and produce an optimum amount of electricity. Predicting energy consumption for just one building requires providers to take into account numerous factors, from weather forecasts to the type of building in question (as residential buildings have different usage patterns than office buildings.) In this case, manually calculating usage forecasts would be incredibly time-consuming. But predictive analytics tools can quickly wrangle this data and produce reliable forecasts.

Enhanced customer experience

Companies today have a wealth of customer data at their disposal. App usage, website visits, product purchases, newsletter signups, email interactions – all of which can be fed into a predictive model.

With predictive analytics, businesses can then predict churn or anticipate operational issues (like holiday spikes in traffic, or supply chain issues). This allows businesses to take preemptive action, like intervening when a customer is flagged as a churn risk. 

McKinsey report found that one airline increased customer satisfaction by 800% and reduced priority customer churn by 60% with a machine learning model. The model enabled the airline to discover and prioritize customers who were predicted to churn in real time. By offering the customer compensation when their flight was delayed or canceled, the airline preserved important relationships.

Improved financial performance

Predictive models can help businesses boost their bottom line. One way is by proactively preventing customer churn (as acquisition can be 5x-25x more expensive than retaining customers). 

Generate predictive analytics with Segment and AWS Redshift

If you need predictive insights ASAP, you can use Segment and Amazon Redshift ML to leverage predictive analytics without developing data pipelines or writing code.

The integration is simple. Segment’s customer data platform (CDP) will ingest data from all of your platforms and transform it so it’s ready for queries. Then you use SQL commands in Redshift to build a machine learning model.

In this Segment recipe, for example, you can learn how to quickly develop a predictive model to forecast customer lifetime value. Depending on the data volume, the process could take a few minutes or a few hours. 

Once it’s complete, the marketing team can apply these insights to campaigns, such as reaching out to customers with a high lifetime value.

Predictions with Segment

Segment helps businesses collect customer data from every touchpoint, and clean and consolidate this data in real time. This is crucial for businesses to scale operations and workflows, and trust the data they’re using to drive decision-making (as well as AI and machine learning models). 

Now, businesses can use Segment to predict the likelihood that a person will perform a specific event. Predictions can help businesses forecast lifetime value, propensity to buy, or churn risks (to name a few things). These predictions are then saved to user profiles, and can be further used to build specific audiences or customer journey orchestration. 


Segment offers five templates that are prebuilt with Predictions for personalized audience creation.

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Frequently asked questions

A predictive analytics tool is software that allows you to design, develop, and implement predictive models. With predictive analytics software, you’re able to anticipate future trends with considerable accuracy.

Some of the top applications of predictive analytics include:

  • Sales forecasting

  • Predicting customer behavior (e.g., propensity to buy, likelihood of churn)

  • Risk mitigation

  • Anticipating staff shortages or supply chain disruptions

Data mining is the process of analyzing and extracting insights from large volumes of raw data. Companies use data mining to understand current trends in customer behavior, detect fraud, uncover manufacturing bottlenecks, and more.

Predictive analytics uses historical data to make forecasts and predict trends. A company may use predictive analytics to predict customer attrition and address it proactively.

Business intelligence tools help companies understand past trends and events. This can be done with descriptive analytics, data mining, and benchmark reports. Predictive analytics tools provide insights into future trends using decision trees, machine learning, statistical analysis, and data mining.

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