Predictive Analytics reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
Products classified in the overall Predictive Analytics category are similar in many regards and help companies of all sizes solve their business problems. However, medium-sized business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Medium-Sized Business Predictive Analytics to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Medium-Sized Business Predictive Analytics category.
In addition to qualifying for inclusion in the Predictive Analytics Software category, to qualify for inclusion in the Medium-Sized Business Predictive Analytics Software category, a product must have at least 10 reviews left by a reviewer from a medium-sized business.
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
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.
SAS Advanced Analytics software is infused with cutting-edge, innovative algorithms that can help you solve even your most intractable problems. Make the best decisions possible. And unearth opportunities you would otherwise miss.
The IBM SPSS Modeler is a leading, visual data science and machine learning solution. It helps enterprises accelerate time to value and desired outcome by speeding the operational tasks for data scientists. Leading organizations worldwide rely on IBM for data discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets. The IBM SPSS Modeler empowers organizations to tap data assets and modern applications with complete, out-of-box algorithms and
SAP Analytics Cloud is a powerful data visualization tool that helps businesses of all sizes do more with data. By transforming static spreadsheets into actionable insights, you can make better fact-based decisions, uncover hidden trends and improve your business performance quickly. SAP Analytics Cloud combines all analytics capabilities -- including business intelligence, planning and predictive analytics -- in a single cloud-based solution.
Board is the #1 decision-making platform. Founded in 1994 and Headquartered in Chiasso, Switzerland, and Boston, MA, Board International has enabled more than 3,000 companies worldwide to drive digital transformation by effectively deploying Business Intelligence, Integrated Business Planning, and Predictive Analytics applications on a single platform. Board enables companies to achieve full transparency of business information and gain complete control of performance across the entire organiz
As a leader in analytic process automation (APA), Alteryx unifies analytics, data science & machine learning, and business process automation in one, end-to-end platform to accelerate digital transformation. Organizations of all sizes, all over the world, rely on the Alteryx Analytic Process Automation Platform to deliver high-impact business outcomes and the rapid upskilling of their modern workforce. Visit Alteryx.com for more information, and to start your free trial.
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
Reimagine your business processes for the digital economy by using predictive insights to optimize your resources and improve margins across your entire enterprise. Uncover trends and patterns from Big Data, the Internet of Things, and your existing data sources with SAP’s powerful predictive analytics software.
Accelerate ROI from your data science initiatives with a collaborative analytic workflow builder that lets you transform data into insight within Hadoop and other big data environments. Unlock your data's hidden potential and increase the value of your big data infrastructure.
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.