Before the advent of computers, the Internet, and digital technologies, collecting, storing, and processing information was tedious and time-consuming. Nowadays everything is different: information is collected constantly and at high speed, and storage cost is much less than before. The concept of Big Data emerged as a display of data storage and analysis/processing capabilities. Getting internal dependencies that were not previously visible is possible only with a large amount of data.
Predictive analytics has been around for a very long time. It is used in the financial sector and insurance, oil and gas, retail, and travel. It can be used to forecast the impacts of different variables on the supply chain or as predictive modeling in healthcare, can be integrated with telemedicine to collect data or boost the efficiency of marketing campaigns. Furthermore, the approach has recently begun to be implemented in marketing, and the opinions are radically divided. Some consider such analytics a baseless prediction. Others are sure that it works and demonstrates valid predictions.
To figure it out, I decided to analyze popular predictive analytics tools and define their advantages for business.
What is predictive analytics?
Predictive analytics is applied to predict unknown events in the future, answering the question “What can happen?” based on the accumulated information analysis. There are many methods used here: mathematical statistics, modeling, machine learning, and other areas of Data Science and data mining. For example, predictive analytics of the current and past performance of production equipment will determine the time of its preventive maintenance in advance to avoid damage to expensive equipment. Predictive analytics collects historical data and building a model to forecast future events. Generally, advanced analytics has been a field of talented data scientists, analytics, statisticians, and data engineers.
How do predictive analytics models work?
Predictive analytics tools mine and analyze data patterns to forecast the next outcomes by deriving pieces of data from data sets to detect patterns and trends. With various statistical analyses, analysts, business users, data scientists, and software developers use predictive analytics tools to build decision models and better understand their audiences, products, and partners and identify potential risks and opportunities.
Benefits and possibilities of predictive analytics for business
Predictive analytics allows you to:
- Reduce risks;
- Optimize resources;
- Boost profits by meeting the needs of customers as much as possible, increasing competitiveness;
- Optimization of operating activities;
- Improving and simplifying the decision-making process.
Predictive analytics is now an affordable tool for all business levels, providing accurate and valuable predictions.
Types of predictive analytics
Regression. The most popular type of predictive analytics. During the regression, a quantitative variable is applied. A car’s selling price will depend on many predictor variables: make, transmission, drive, color, appearance, interior condition, etc. The relationship between price and all predictors will underlie the model. There are several types of regression, among them – multivariate linear, polynomial, regression trees.
Classification. Here the so-called categorical variation of the answer has found application. For example, income level. It can be roughly divided into three groups: low, medium, and high. The classifier will study the received data set, where each observation will contain information about the variable and predictors.
Methods of predictive analytics include:
- Data mining: a combination of a wide range of mathematical tools (from classical statistical analysis to new cybernetic methods) and the latest advances in information technology.
- Text analytics: acquiring analysis-friendly structured data from unstructured text.
- Predictive modeling: creating and modifying a model to predict forthcoming results.
Top Predictive Analytics Software and Tools
EverString is a predictive marketing software that provides data curation and mapping, and it serves millions of B2B customers. The platform continuously integrates with existing marketing and CRM applications to find the customers for the companies and gives an in-depth analysis of the target accounts. EverString allows personalizing messaging for each phase of the customer life cycle before launching the campaign. This makes it a useful tool for account-based marketing (ABM) initiatives.
EverString set up its platform so that accounts’ profiles are divided into small bits of data tasks, like checking a company’s industry classification. If there’s a discrepancy, that data task is pulled for review by a higher-level worker.
The platform applies the most accurate data tasks to train its deep learning AI engine, which can then perform similar data tasks for millions of companies.
Infer is a predictive sales and marketing tool that consolidates all data sources to identify the prospects that convert by helping to understand their purchasing behavior. It boosts campaigns, accelerates your pipeline, and improves close rates. It uses machine learning to find patterns and builds a predictive model using the previous accounts and the rules specified by users. Infer can help account-based marketing and sales parse the collected data to optimize the sales funnel while also reducing the overall sales cycle.
Radius is explicitly designed for revenue-driven marketers and presents a series of data analytics services. The platform offers a self-service AI and a list of features that make it outrank other predictive analytics software and tools. It provides a self-service AI and a list of features that make it stand out from other predictive analytics software and tools. Some of these include:
The Radius Customer Exchange (RCX) helps to specify look-alike audiences for campaigns. It promotes a smooth working relationship between those businesses to arrive at building marketing lists.
Radius Connect: helps to transfer particular the required marketing data to Salesforce.
Radius also helps share and manage data within the organization. Besides, it allows marketers to personalize campaigns and find new accounts from internally existing databases. Opposed to EverString and Infer, Radius is a cloud-based system.
Statistica Decisioning Platform
Statistica provides many business intelligence tools that can operate synchronously. A decision-making platform transforms predictive analytics into an efficient and robust process with more refined business decisions.
It focuses on customer and market behavior forecasts. This tool also helps businesses to identify and address possible growth opportunities.
It helps to build all-inclusive and well-detailed predictive models. The tool is developed for use by a wide range of industries, although its history is associated with developing fraud and risk models specifically for the insurance and finance sectors.