Learn about Predictive Analytics with examples, identify its uses, and understand how it works
Published 9 Dec 2022
Predictive Analytics is a form of technology that aims to recognize the likelihood of future outcomes based on combined current and historical data using figures drawn from analytics techniques such as artificial intelligence, machine learning, data mining, statistics, and modeling.
The goal of predictive analytics is to identify risks and assess how these can be mitigated; recognize opportunities to leverage and convert them to optimum result; and use the data collected to improve operational efficiency.
Companies use predictive analytics to get actionable insights, make more strategic solutions, enhance their decision-making capabilities, and quantify business growth based on the data interpreted.
According to the analysis released by ReportLinker, the market of Predictive Analytics is expected to grow from $10.5 billion in 2021 to $28.1 billion by 2026 as more and more enterprises worldwide rely on advanced analytics for their business management and in increasing their competitive advantage.
Different techniques are used depending on the purpose and response sets that would work best for an enterprise. Amazon and Netflix for example, use predictive analytics to determine consumer behavior and personalize user experience. Below are the basic steps predictive analytics softwares typically follow in the process of forecasting analytics:
How Predictive Analytics Work
Data gathering has long been recognized and practiced by organizations for reasons such as identifying patterns, understanding changes in consumer behavior, and analyzing important information for other future use.
Predictive analytics pairs these data gathered with mathematical tools to anticipate what may happen and respond to them proactively.
With the use of this type of analytics, businesses can optimize marketing campaigns to predict customer purchases based on their buying behavior. It can help in identifying leads that should be prioritized based on audiences that are closer to conversion.
Predictive analytics tools can also help improve operational efficiency by forecasting inventory and maximizing resources. Data gathered through analytics can also be used to determine prices for products and services that might be affected by seasonality.
Other benefits of using predictive analytics include the capability to reduce risk and detect fraud. Generally, this kind of advanced analytics is best utilized to foresee specific scenarios that organizations can use to their advantage.
Choosing the right predictive model is essential in coming up with the most accurate forecast, while choosing the wrong one can lead to inaccuracy and irregularity in your project. There are several types of statistical techniques to use in predictive analytics but below are the three most common:
Types of Predictive Analytical Models
Various industries are leveraging the use of predictive analytics to help with their efficient operation and decision making. Listed below are some of these industries and as well as the advantages offered by using predictive analytics:
SafetyCulture (formerly iAuditor) is a powerful tool used by industry leaders for data collecting, organizing, and storing. Although its Analytics doesn’t function exactly the same as predictive analytics tools, it can offer the following advantages:
These iAuditor features help reduce performing manual tasks and can be used to help predictive analytics tools proactively identify issues in operations and efficiently fix them.
Jaydee Reyes is a content writer and researcher for SafetyCulture. Her six-year experience in the field of data research and media monitoring adds expertise and quality to her work. She is also a champion of leveraging technology to promote a culture of safety in workplaces around the world. As a content specialist, she aims to help companies adapt to digital changes through interesting and informational articles.
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