The 10 Best Predictive Analytics Software in 2026

You could argue that pretty much all analytics are meant to be predictive. Isn’t the point of analyzing past performance, on some level, to project future performance? (I guess you could just be nostalgic for the metrics underlying your favorite past fiscal quarter.)

As a dedicated tool class, however, predictive analytics software helps analysts of all kinds see what past data says about the future. While tools like these can’t tell you what will happen, they can tell you what massive amounts of data suggest is likely to happen.

So whether you’re in marketing, UX, risk management, manufacturing, or any other industry that the future matters to, predictive analytics tools like the ones I break down below should help you get the data-driven insights you need to make better decisions.

The best predictive analytics software

What is predictive analytics software?

Predictive analytics software predicts future trends and outcomes for businesses by analyzing past data and identifying ongoing patterns—it does this all using machine learning (ML) and predictive AI. 

The basis of this software—shockingly enough, predictive analytics—is the third of four stages in analytics maturity for apps:

1. Descriptive analytics software looks only at past data to figure out what happened previously.

2. Diagnostic analytics software tries to find out why a data trend or event happened.

3. Predictive analytics software guesses what will happen in the future based on past and current data. ( ← You are here.)

4. Prescriptive analytics software suggests what to do by considering possible scenarios involving your data.

Software usually isn’t exclusive to one type of analytics. For example, most predictive analytics apps also have descriptive analytics so you can see what’s happening with your business right now.

What kind of data do you need before using predictive analytics?

To make predictions, predictive analytics tools need a lot of data to work with. And the more data, the better. The ML algorithms and AI behind predictive analytics usually need hundreds to thousands of data points to predict data trends accurately. But the type of prediction you want to make and the tool you use both affect the minimum.

What makes the best predictive analytics software?

If you’re just looking for data management, reporting, visualization, or analytics tools, there are hundreds—if not thousands—of apps to choose from. Some of these solutions may include some forecasting capabilities or utilities that help analysts apply their own algorithms or AI processes.

For the purposes of this piece, I’m excluding all those tools. I’m defining predictive analytics software as platforms that have:

• Predictive focus: The focus of the software should be on performing predictive analytics for the user, or developing ML models to do so.

• Standalone utility: It should be accessible on its own without a subscription to a broader software product like a CRM or an ERP.

• Integrated ML and AI: Machine learning and AI should be primary features for enhancing predictive models, not just formatting data or automating workflows. It should also be able to apply native predictive models.

• Diversified data sourcing: The software should be able to draw data from a range of sources rather than one singular platform or proprietary repository.

Note: I’m also choosing to exclude media marketing mix (MMM) software from this list. Even though MMMs definitely meet the criteria, their use cases are a little too narrow.

Due to the richness and complexity of this software, I wasn’t able to personally test every app. So in addition to testing the apps I could, to improve the diversity and depth of my research, I also relied on product descriptions, third-party reviews, demos, and exchanges with marketing and data science professionals.

One thing to keep in mind: if you just need some prescriptive brainstorming, you can turn the results from your predictive analytics tools into prescriptive analytics with Zapier MCP. Zapier can securely access your predictive analytics tool of choice, and then you can ask the AI for suggestions for how to act on its data. For example, if you want to project your budget spend for the next quarter, you could also ask your AI for input on line items to cut.

The best predictive analytics software at a glance

Best open source predictive analytics software

Prophet (Web)

Prophet pros:

Prophet cons:

• May strain resources for large data sets or complex forecasts

• Lacks advanced forecasting capabilities

• No multivariate forecasting capabilities

Admittedly, Prophet isn’t exactly a predictive analytics platform—it’s actually an open source Python procedure for automatic forecasting. But who’s counting?

A product of Facebook’s Core Data Science team, Prophet lives up to its name by forecasting time series data that’s easily broken down daily, weekly, and yearly with strong seasonal effects spanning multiple seasons of historical data. According to Aksinia Chumachenko, team lead product analyst for Simpals, “Prophet stands out due to its automated seasonal pattern detection, flexibility in handling holidays and events, robustness to missing data and outliers, and ease of use with minimal parameter tuning.” Aksinia also pointed out that it readily integrates with popular data analysis ecosystems.

Available for Python 3.7 and later, Prophet is a simple solution that’s readily available and easy to deploy. As an automatic forecasting procedure, it gives analysts an efficient, scalable way to help their organizations set goals and allocate resources more effectively.

As an open source option, Prophet isn’t the perfect predictive analytics solution for every user. Aksinia noted that it can be pretty demanding on systems when data scale and complexity increase, and it lacks advanced forecasting capabilities. But for those with simple forecasting needs, it could be a great (read: free) option.

Prophet has solid documentation and support, and it usually gets a few updates every year, which you can track in its release history. GitHub also has an issues page for Prophet where you can ask other users for support.

Prophet pricing: Free

Best predictive analytics software for user decisions

Scios (Web)

Scios pros:

• Creates digital environments for predictive user decisions

• Slick, user-friendly interface

• Combines granular data from various sources with macroeconomic data

Scios cons:

You could manually finagle your data to show you some potential numerical outcomes to map a narrative onto—or you could use Scios.

What makes Scios unique is that it’s not a predictive analytics solution so much as it is a decision intelligence solution. Designed to show users what people would do in a hypothetical scenario, it creates digital “twins” of markets, populates them with virtual consumers, and runs input scenarios. 

Based on behavioral economics, Scios provides AI-powered insights in a slick dashboard that’s easy to navigate. Using diverse data sources, the platform injects real (or as real as data-modeled AI can get) human motivations into its virtual markets that make decisions and progress through journeys with data-driven probability. This lets you do things like find out which kinds of features your market is interested in, test update concepts, and gauge adoption likelihood. 

Rather than showing you data narratives, Scios allows you to run scenarios to test for specific outcomes of interest. It’s a little like playing God in a way that’s almost not at all creepy; it can be hugely valuable for establishing trustworthy projections and identifying possible problems in your product. With over 300 user prediction models, there’s enough granularity to align virtual consumer actions with your unique data needs. 

Obviously, Scios doesn’t specialize in creating the kinds of forecast trend reporting that many of the other tools on this list do, so it’s not for everyone. But if you’re really interested in analyzing hypothetical user behavior, Scios is for you.

Scios pricing: By request

Best predictive analytics software for automated forecasting

SAS Viya (Web, iOS, Android)

SAS Viya pros:

SAS Viya cons:

SAS is truly one of the forebears of data management software, so it’s hard not to include one of their products on this list. Admittedly more of a general data visualization platform, SAS Viya also has powerful automated forecasting features.

The fact that it’s a data automation engine of sorts makes its forecasting utility pretty compelling. The platform allows you to generate automated forecasts and visualizations based on your data, essentially hands-free. Since you don’t have to spend resources developing and tweaking forecasting models, you can save a SAS-load of time and reduce potential human bias. You can even tailor modeling techniques to individual data segments to stay flexible as you scale your data across time series.

While this automated forecasting is designed to be as automatic as possible, it’s also not completely rigid. You can feed it any known events like holidays or seasonalities to help shape the forecasts to your industry. Or, you can get your hands dirty and apply your own industry knowledge to manually override outcomes.

Does SAS Viya have the prettiest dashboard? No. Is it the easiest software to learn and deploy? Also no. Is it the fastest and most responsive tool on the market? Still no (although it is much faster and cleaner since it got a much-needed facelift recently). But that’s because this is complex, powerful software. It may take a while to onboard effectively, but once it’s up and running, it can pay for itself in insights and time savings. 

If this all sounds compelling, you have two options when signing up for SAS Viya. You can go the Enterprise route, where pricing is by request, or try a pay-as-you-go option through Microsoft’s Azure Marketplace. The latter plan will cost you $0.55 per SAS unit (SU) per hour. (SU is really just a fancy way of saying processing power or compute resources.) In other words, you’ll be charged based on how intensive your analytical workloads are, up to a maximum of 10 SUs for each hour. 

SAS Viya pricing: By usage; $0.55/SAS unit/hour

Best predictive analytics tool for people analytics

One Model (Web)

One Model pros:

One Model cons:

HR teams may struggle to retrofit most predictive analytics platforms into their people analytics processes—except for One Model, a dedicated people analytics tool.

Traditional employee management software can be a great asset for overseeing practical elements of HR processes and collecting data points, but they have their limitations when it comes to managing that data. One Model allows teams to connect HRIS systems and turn that people data into actionable insights.

Since One Model is an analytics tool tailored specifically to the people analytics space, it has richer data features than just about any other HR tool and more utility for HR use cases than just about any other predictive analytics tool. Packed with plug-and-play analytics and predictive modeling, it automatically ingests data from wherever you house your HR data. 

Since it’s such a niche tool, I found One Model to be surprisingly flexible. One Model’s data visualizations are sharp, and you can create new data views on the fly to present data the way you want to see it. You can even manipulate metrics manually (say that five times fast), integrate with other preferred business intelligence platforms, and export findings to internal repositories. 

At the core of One Model is One AI, its proprietary end-to-end AI platform. For HR teams without their own suite of data scientists, One AI can pick optimal predictive models on its own, apply those models, and integrate outcomes into storyboards, so they’re instantly actionable. If there’s ever any question about a storyboard, you can easily trace any forecast to its core data point. And if you’ve got your own models, you can use those, too.

You can also ask the One AI Assistant natural language questions about your data, and it’ll output auto-generated visualizations as part of its answers (which will, predictably, save you a ton of time). 

One Model is such a user-friendly, adaptable tool that I almost wish it was a more generalized predictive analytics product. But for those who need predictive people analytics, this should be a go-to solution.

One Model pricing: By request

Best predictive analytics software for generative AI

SAP Analytics Cloud (Web, iOS, Android)

SAP Analytics Cloud pros:

SAP Analytics Cloud cons:

I almost didn’t include this tool because it’s a more general analytics platform, combining BI, analytics augmentation, and enterprise planning capabilities. But SAP Analytics Cloud (SAC) has such strong predictive features that it’d be a shame not to mention it.

SAC is a built-in data utility offering for other SAP Cloud products, but it can also link to existing solutions and non-SAP sources to import and replicate siloed data or augment non-replicated live data. Once integrated, SAP makes it incredibly easy to visualize analytics with drag-and-drop dashboards you can build in minutes. 

While none of the above is necessarily unique, what makes SAP stand out is how well it integrates generative AI into its offering with Joule, the platform’s AI companion bot. For example, while creating reporting dashboards, you can ask Joule to come up with code to apply advanced features like timeline toggling, then drop the code into the dashboard editor and make it happen. 

You can also ask it questions like you would any other AI chatbot to get human-language insights into your data, alter data models, generate visualizations, and get tips. Taking this into predictive use cases, you can also use Joule to run simulations, automate forecasts, and even generate business plans based on findings.

Between Joule and SAC’s highly intuitive interface, the software makes it easy to manually run and automate predictive forecasts. You can choose between linear regression or triple exponential smoothing to help make allowances for complexities like seasonality. Graphs readily visualize historical data, trend projections, forecasts according to the period you preset, and confidence intervals, which you can click to drill further into.

SAC doesn’t necessarily do things other tools on this list can’t in terms of raw utility, but if you’re after the simplicity of integrated AI, SAC and the Joule copilot are meant for you. (Yes, that’s a very out-of-place Jewel reference.)

SAP Analytics Cloud pricing: By request; pay-as-you-go option available

Best predictive analytics tool for interactive forecasting

Qlik (Web, Android)

Qlik pros:

Qlik cons:

I can’t decide if I love Qlik’s name or hate it, but one thing I have decided is that it’s a handy predictive analytics solution. Qlik is a slick data integration platform with useful predictive analytics features. It brings no-code machine learning modeling and automation to relatively non-technical users, and it’s designed to make analytical processes as simple as possible. 

That extends to its automated ML application, Qlik Predict®, which can nearly instantly find algorithms to apply optimal ML models to your unique data sets. You can then tinker with these models, test them, score them, and rank them so you can prioritize the ones that work best. Qlik is also serious about transparency, so you can funnel down through every data level to find the core of its predictions with SHAP values that tell the full story.

All those features make Qlik a good predictive analytics product, but what makes it really shine is the interactivity of its reporting dashboards. By loading your predictive analytics into Qlik Sense® (for self-hosted) or Qlik Cloud Analytics (in the cloud), you can make predictive apps that bring that data to life. From there, Qlik can calculate data live as you interact with charts, graphs, maps, and other visualizations, helping you explore and report on your data more fully.

Qlik has secured a comfortable place in the niche of self-service users. It’s convenient for data exploration and handy for analytics automation, but advanced users might find that it’s got its hiccups with data load speeds and occasional programming roadblocks. And with predictive analytics costing at least $2,750/month, it might not be the best option for growing enterprises with complex data processing needs.

Qlik pricing: From $2,750/month for the Professional plan (predictive analytics not available on lower-tier plans)

Best predictive analytics software for ease of use

ThoughtSpot (Web)

ThoughtSpot pros:

ThoughtSpot cons:

Predictive analytics software isn’t the easiest app class to wrap your head around, so whenever a tool makes ease of use a priority, I take note. And for a data analytics platform, ThoughtSpot has a seriously mellow learning curve—like, almost level. 

You can sign up for a free trial and see for yourself: over just a few minutes, you’ll find out how to use the natural language search function to filter sample data, pull up visualizations, and track changes over time. 

ThoughtSpot’s predictive analytics insights are AI/ML-powered and simple to use, and there’s no need to build your own models. For example, you could create a Liveboard (basically a dashboard with live updates) for tracking churn rate and, if you have the data, use it to track metrics like number of users with high, medium, or low churn risk and churn risk trends.  

You can do time-series forecasting, too, and then use the AI search tool to ask questions about your prediction or break it down with filtering. Although you can get super granular and math-y with data on ThoughtSpot, it sort of feels like a predictive analytics tool for users without a background in analytics, and that’s rare.

It’s also really easy to embed ThoughtSpot data in other apps (CRMs, HRIS, etc.) and portals, making it accessible to non-technical users and clients and cutting down on time spent switching between tools. Other apps on this list might offer more powerful predictive analytics models and visualizations, but few are as user-friendly as this one. 

ThoughtSpot pricing: From $125/month for 5 users on the Essentials plan

Best predictive analytics tool for low-code data preparation

Alteryx One (Web, Windows)

Alteryx One pros:

Alteryx One cons:

Half the struggle of working with any data is making sure it’s clean and accurate, especially when you need to code the cleaning process. Alteryx One speeds up this process with low-code automations, automating data preparation so you don’t end up with four identical records named Thomas Thomas.

The drawback is that you’ll have to ask your IT team for help the first time you create a data prep workflow with Alteryx One. But once you have it set up, you can use it with both on-prem and cloud data whenever you need, and as new data gets added, it’ll automatically go through your preset preparation process. I honestly only call this tool low-code because of that preparation step—nobody else has to code afterward.

From there, you can build a workflow to plot out your predictive analytics. After your prepared data feeds into your AI and ML models, Alteryx One creates dashboards and reports for your predictions. Since its automations run models and workflows at the same time, your analytics update in real time as you get new data.

Just don’t get too excited if you’re looking for an entry-level predictive option. You still need enough IT talent to set up and monitor it, on top of a subscription model that’s quite pricey. You can only get prescriptive analytics functionality from the Professional Edition, which doesn’t have a public price (but unless Alteryx One is implementing a highly risky experimental pricing structure, it certainly costs more than the Starter Edition’s $250/month per user).

Alteryx One pricing: Contact for pricing for the Professional plan (predictive analytics not available on lower-tier plans)

Best predictive analytics tool for AutoML

DataRobot (Web)

DataRobot pros:

DataRobot cons:

If you take more of a DIY approach to your predictive analytics, chances are you’re building your own ML models. DataRobot speeds up the process with a full kit of AutoML (automated machine learning) tools. AutoML lives up to its name by automating model creation for people who don’t have a PhD in data science but do have some coding experience.

DataRobot connects with sources like SAP, Databricks, and AWS to pull modeling data quickly. Then, its recipes (repeatable processes) clean and prepare your sample for more accurate predictions. You can also have DataRobot generate new data to balance biases in your samples or streamline multiple data formats like images and geocoordinates.

As one of those aforementioned non-data-science-PhDs, I was impressed by how easy DataRobot makes it to prepare and create ML experiments. It generates potential variables to save you time adjusting and choosing them yourself. After that, you can compare your models side-by-side and edit them as needed. DataRobot then lets you put your model to use by integrating it into your software through an API or building your own simple app.

If you work in a regulated industry, DataRobot also has all the transparency and security you’ll need. It explains how your model comes to its conclusions by showing which variables have the most impact, and with its automated governance features, you can manage access and compliance from one menu. 

DataRobot pricing: Contact for pricing

Best predictive analytics tool for enterprise MLOps

Microsoft Azure Machine Learning (Web)

Microsoft Azure Machine Learning pros:

Microsoft Azure Machine Learning cons:

Microsoft Azure Machine Learning offers all the tools you need to develop, experiment with, and deploy new models, but it really shines in its ability to manage your MLOps pipeline. 

In a world of table-based interfaces, I always prefer a clear visual builder, and Microsoft Azure Machine Learning’s Designer has just that. You can click any model, dataset, or pipeline building block on the left, then drag and drop it into your pipeline. From there, you create connections using each task in the ML process from data prep to scoring. For later experiments, you can copy previous pipelines to use as a template.

With Microsoft Azure Machine Learning’s ML tools all connected to this pipeline, enterprises can get their new models up and running quickly. And, as with any Microsoft software, the app has enterprise-grade security and governance features, like an integration with Azure Policy, to ensure all your ML resources follow your organization’s rules.

If you’re worried about Microsoft being up to its usual stick-within-Microsoft tricks, you’ll be happy to hear that you can pull data from outside sources—not just Microsoft—into Microsoft Azure Machine Learning. And soon it’ll connect outside apps through Microsoft Fabric, an analytics and data integration tool with over 170 connectors. But if you already keep a lot of your ML resources in a competitor cloud like AWS, it could take a lot of money and effort to switch them to Microsoft.

Plus, if you’re here just for the predictive analytics, Microsoft Azure Machine Learning also requires coding to put your model to work for you. Similar to DataRobot, you’ll have to build something yourself or get a separate tool for predictive analytics to manage your predictions. But for enterprises that need a way to organize their MLOps processes, you can’t get much clearer.

Microsoft Azure Machine Learning pricing: Pay-as-you-go options starting at $70.08/month/instance; charges for additional Microsoft services may apply

Automate your predictive analytics

The tools above give you a serious edge in forecasting, but they generate more signal than most teams can act on alone. That’s where connecting your analytics stack to your AI assistant pays off: you can pull insights, run follow-up questions, and trigger downstream actions without jumping between tabs.

Zapier lets you give your AI assistant governed access to your analytics tools and the rest of your business apps—without handing it raw API keys or building custom auth from scratch. Your credentials stay in Zapier’s managed connection layer, and when you need to revoke access or change what your agent can reach, you do it in one place.

From there, 9,000+ pre-built integrations means you can ask AI to consider data from all your tools, make predictions on the spot, and then actually take action.

Related reading:

This article was originally published in April 2024 and has also had contributions from Dylan Reber and Melissa King. The most recent update was in July 2026.