Check out our list of free Predictive Analytics Software. Products featured on this list are the ones that offer a free trial version. As with most free versions, there are limitations, typically time or features.
If you'd like to see more products and to evaluate additional feature options, compare all Predictive Analytics Software to ensure you get the right product.
RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment.
Alteryx, Inc. is a leader in self-service data analytics. Alteryx Analytics provides analysts with the unique ability to easily prep, blend, and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks. Analysts love the Alteryx Analytics platform because they can connect to and cleanse data from data warehouses, cloud applications, spreadsheets, and other sources, easily join this data together, then perform analytic
Qlik Sense is a platform for modern, self-service analytics, driving discovery and data literacy for all types of users across an organization. It supports the full range of analytics use cases - from self-service visualization and exploration to guided analytics apps and dashboards, custom and embedded analytics, mobile analytics, and reporting. And, it does this within a governed, multi-cloud architecture that delivers maximum trust, scale, and flexibility for the organization. Qlik Sense pro
WebFOCUS provides an intuitive, immersive experience inviting users to begin creating analytical content, portal pages/ dashboards and reports in minutes. This same platform empowers data scientists, developers, and administrators to leverage powerful capabilities to manipulate and transform data within an unparalleled data prep and governance foundation. * Business users need complete self-service analytics capabilities that help them create, use, and share analytical content * Data scient
Q is data analysis and reporting software primarily for market researchers. It performs all aspects of the analysis and reporting, from data cleaning and coding through to creating tables and advanced analyses, exporting to Office and creating online reports.
Kraken by Big Squid is an AutoML platform built to enable data analysts with deeper insights and to scale data scientists across an organization. Machine Learning is helping companies become more data-driven than ever before. Although historical and predictive reporting is extremely valuable, machine learning insights through Kraken provide an even deeper understanding of the value and quality of your data. With direct connections to your existing BI platform or data warehouse, Kraken empowers
H2O.ai is empowering companies to be AI companies. Market leading organizations are using H2O.ai platforms to solve a myriad of AI transformation use cases across industries, including determining credit, decrease fraud and money laundering risks; improve product design, marketing and business innovation; improve early disease detection, drug discovery, personalized medicine; increase customer experiences and loyalty, and improve brand safety. H2O.ai offers enterprise customers with multiple p
Since 2007, we are creating the most powerful framework to push the barriers of analytics, predictive analytics, AI and Big Data, while offering a helpful, fast and friendly environment. The TIMi Suite consists of four tools: 1. Anatella (Analytical ETL & Big Data), 2. Modeler (Auto-ML / Automated Predictive Modelling / Automated-AI), 3. StarDust (3D Segmentation) 4. Kibella (BI Dashboarding solution).
Agile analytics and reporting tool, which enables business users to make informed decisions from real-time business data
Build and put ML pipelines into production, fast. Without coding. Welcome to the era of data collaboration, with Datagran's all-in-one data workspace.
InsightEdge is an always-on platform for your mission-critical applications across cloud, on-premise or hybrid. The platform operationalizes machine learning and transactional processing, at scale; analyzing data as it's born, enriching it with historical context, for instant insight to action.
Neural Designer is a machine learning software with better usability and higher performance. It allows you to build artificial intelligence models using neural networks to help you discover relationships, recognize patterns and make predictions in just a few clicks. Neural Designer´s strength consists in giving you the ability to make complex operations and build predictive models in an intuitive way thanks to to its graphical user interface. You can run any task and instantly see the results i
JADBio makes it easy and affordable for health-data analysts and life-science professionals to use data science to discover knowledge while reducing time and effort by combining a robust end-to-end machine learning platform with a wealth of capabilities, ranging from smart feature selection to the reuse of predictive models. JADBio’s healthcare purpose-built platform provides leading-edge AI tools and automation capabilities, enabling life-science professionals to build and deploy accurate and
Knowi is a modern business intelligence platform purpose-built for today's modern data enabling enterprises of all sizes to dramatically shorten the distance from raw data to foresight to action. With native integration to virtually any data source, including NoSQL, SQL, RDBMS, file-based and API’s, Knowi eliminates the need for ETL, ODBC drivers, or data transformation processes that alternate solutions require. Data engineers can join structured and unstructured data sources to create blen
Lityx is an analytic solutions and services firm with a proprietary cloud-based predictive modeling and optimization platform. It applies deep expertise in complex analytic solutions with a focus on applications in marketing analytics and customer relationship management.
All your marketing data at your fingertips in a fully automated insights platform MarketingTracker is specifically developed to make decisions making better, faster and easier. MarketingTracker enables you to easily understand what’s happening in your markets and your brands, and what’s driving this. Insights can easily be shared through storyboards and presentations are automatically produced in line with management reporting cycles. MarketingTracker provides you with many robust data analysis
TADA allows you to make the most of your data through predictive analysis, even with small datasets. No skills in data science are required. TADA’s turnkey AI reveals how data behaves, what are the correlations between variables of a problem and how the future holds. TADA’s unique machine learning technology automatically selects the best predictive model. Models are transparent, easy to understand, execute and export. With TADA by MyDataModels, you will easily make better data based operationa
Zepl let you use data science to analyze your cloud data warehouse in minutes. Customers use Zepl for all kinds of use cases, including predictive analytics, marketing analytics, preventive maintenance, security, anomaly detection, sales forecasting, product recommendations and more. Zepl is an extensible, cloud-based data science and analytics platform for enterprise teams. With Zepl, teams of data analysts and data scientists can use Python, R, Spark, Scala, and SQL to find insights and mak
Predictive analytics software is all about making business outcomes predictable. Data scientists and data analysts are able to do this by using data mining and predictive modeling to analyze historical data. By better understanding the past, businesses can gain insights into the future. Predictive analytics is a step further than general business intelligence, which businesses use to pull actionable insights out of their data sets. Instead, users can develop machine learning algorithms and predictive models to help forecast and achieve business-critical numbers.
The reason businesses are able to hit those critical numbers and become more predictive is due to the boom of big data. Companies are able to harness their data like never before. By recording and owning more and more historical and real-time data, data scientists have larger sample sizes to work with, meaning they can be much more accurate. Additionally, companies that are investing in predictive analytics without first ensuring that their data is accurate, clean, and accessible will ultimately be wasting their time. However, those that are able to wrangle their data properly will create a significant competitive edge and hold an advantage in the market.
Key Benefits of Predictive Analytics Software
There are a number of applications for predictive analytics software, and reasons that businesses should adopt them, but they all boil down to understanding what has happened in the past, what could happen in the future, and what should be done to ensure positive business outcomes. These are considered descriptive analytics, predictive analytics, and prescriptive analytics.
Descriptive Analytics (understanding the past) — Descriptive analytics deals with understanding what has happened in the past and how it has influenced where a business is in the present. This means undergoing data mining on a company’s historical data. This type of analysis can be obtained by using business intelligence tools, big data analytics, or time-series data. Regardless of how it is attained, providing descriptive analytics is a key foundation of predictive analytics and creating data-driven decision-making processes. It requires thorough preparation of data and ensuring the data is organized in a manner that allows for easy descriptive analysis.
Predictive Analytics (knowing what is possible) — Predictive analytics allows users and businesses to know and anticipate potential outcomes. Building predictive models based on the descriptive analysis can ensure that businesses do not make the same mistake twice. It can also provide more accurate forecasting and planning, which helps to optimize efficiency. Ultimately, this analysis makes the unknown known.
Prescriptive Analytics (so now what?) — The final step, and ultimate reason for using predictive analytics software, is to make clear actions based on the suggestions and recommendations of the predictive models. This is where machine learning and deep learning functionality comes into play. Some predictive analytics solutions can provide actionable insights without human intervention. For example, it can provide a short list of sales accounts that should close quickly based on a number of variables. Becoming prescriptive take analytics a step further, and it is the ultimate reason for adopting advanced, predictive analytics.
To fully take advantage of predictive analytics software, businesses need to hire highly skilled data scientists with knowledge in machine learning development and predictive modeling. There is not an abundance of these skilled workers, therefore they are often paid very well. Dedicating financial resources to these positions may not be an option for every company, but those who can afford data scientists have a leg up on the competition.
While data scientists or data analysts are the employees tasked with using predictive analytics software, there are a number of industries and departments that can be impacted by utilizing predictive analytics:
Manufacturing and Supply Chain — One area that can be greatly enhanced by using predictive analysis is demand planning for manufacturing companies. By having more accurate forecasting, businesses can avoid risks, like shortages and surpluses. Additionally, companies can get predictive around quality management and production issues. By analyzing what has caused production failures in the past, companies can anticipate, and avoid, production breakdowns in the future.
Distribution is another major aspect of supply chain that can be further optimized with predictive modeling. By better estimating where goods will need to be delivered, and the risks that may hold up distribution modes, businesses can provide a better service and more efficiently deliver their products to customers. Taking into account historical data, such as weather, traffic, and accident records, shipping can become a more precise science.
Retail — Retail is another industry that is ripe for optimization with the help of predictive analytics. Retail predictive analytics can provide businesses with insights on everything from pricing optimization to understanding how shoppers navigate brick-and-mortar stores for better in-store organization of merchandise. E-commerce businesses are able to track these factors in a much more efficient manner. All e-commerce interactions can be recorded into a database and influenced by predictive models. This is one of the main reasons that Amazon has been able to be so successful and disruptive to brick-and-mortar retailers. Every decision can be made predictive with the help of data.
Marketing and Sales — Being able to predict the actions of customers and prospects is an invaluable service for any business. Marketing teams can leverage predictive analytics software to project how marketing campaigns may perform, which segment of prospects to target with ads, and potential conversion rates of each campaign. Understanding how these efforts impact the bottom line is critical to the success of marketing teams and translates into a much more efficient and productive sales team. At the same time, sales teams can leverage predictive modeling in such areas as lead scoring, determining which accounts to target first because they have a higher chance of closing. Ensuring that sales representatives are working smarter instead of harder means more revenue. A few CRM and marketing automation solutions provide some level of predictive functionality, but data scientists can separately funnel that data into dedicated predictive analytics tools to find cross-departmental correlations.
Financial Services — The banking industry has long been ripe for disruption, but financial administrations are taking advantage of predictive analytics solutions to better predict risk. Historical data can power predictive analytics software to predict fraudulent transactions and determine credit risks, among many other functions.
Predictive modeling is a complex science that takes many years of training to understand. There is a reason data scientists are in high demand. Not many people have a complete grasp of how to build predictive models. There are two main types of predictive models: classification and regression models.
Classification Models — Simply put, classification puts a piece of data into a bucket, or a class, and labels it as such. Classification models essentially label data based on what an algorithm has already learned. The ultimate goal of classification models are to accurately bucket new data points into the proper classes so that the data can become predictive and prescriptive.
Regression Models — Regression models analyze the relationship between two separate data points and help to forecast what happens when they are put side by side. Putting the technique into a baseball example, teams may perform a regression analysis on the relationship between the number of fastballs thrown and the number of home runs hit.
Decision Trees — One common type of classification model is a decision tree. These models predict a number of possible outcomes based on a variety of inputs. For example, if a sales team builds $1 million in a pipeline, they can close $100,000 in revenue, but if they build $10 million in a pipeline, they should be able to close $1 million in revenue.
Neural Networks — Neural networks, known in the AI world as artificial neural networks, are extremely complex predictive models. These models are able to predict and analyze unstructured, nonlinear relationships between data points. These solutions provide pattern recognition and can help to track anomalies. Artificial neural networks were originally created and built to mimic the synapses and neural aspects of the human brain. They are one of the contributing factors to the accelerated growth in artificial intelligence and deep learning.
Other types of predictive modeling include Bayesian analysis, memory-based reasoning, k-nearest neighbor, support vector machines, and time-series data mining, among others.
Lack of Skilled Employees — The main issue with adopting predictive analytics software is the need to have a skilled data scientist to interact with the data and build the models. There is a distinct skill gap in terms of finding users who both understand how to pull data and build models and the implications that the data has on the overall business. For this reason, data scientists are in very high demand and, thus, expensive.
Data Organization — Organizing data in a way that can be accessed easily is a challenge that many companies face. It is not easy in today’s world to harness big data sets that contain historical and real-time data. Companies often need to build a data warehouse or a data lake that can combine all the disparate data sources for easy access. This, again, requires highly knowledgeable employees.