How Are Predictive Analytics Commonly Used?

Predictive analytics is a type of advanced analytics that uses historical data in order to determine the likelihood of unknown future events. Predictive analytics is typically used by businesses to predict specific outcomes like future customer behavior, moves from competitors, and other events relevant to their industry. This is possible thanks to a combination of advanced statistics, computer algorithms, and machine learning techniques.

Predictive modeling, a subset of predictive analytics, is used in a more general or academic sense compared to the traditional commercial uses of predictive analytics. Predictive modeling is used more frequently, and these data models are often compared with past or existing data sets and can be embedded alongside current data models in applications. Predictive analytics is often followed up with prescriptive analytics, which seeks to offer actionable insights and recommendations based on the predicted outcome.

Components of Data Analytics

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To truly understand predictive analysis, it’s important to break down how analytics works in general. You essentially have three main components.

Data exploration and visualization: Exploration is the process of going through large data sets to identify patterns and gather interesting information. It can be done manually or use automated data mining techniques as well as automated visualization to make data easier to process.

Data science: This is an umbrella term that refers to the process of using automated processes and algorithms to extract insights from structured datasets and unstructured data alike. Machine learning allows computers to learn from data in real-time to accurately predict outcomes.

Data reporting: This is where data is summarized and distributed to business leaders to help them make better decisions. Here are a few common predictive analytics examples and where they’re used.


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A person’s credit score is determined by a predictive model that essentially collects all data relevant to their creditworthiness to predict whether they’re likely to default on a purchase or have difficulty paying back a loan. This type of prediction helps businesses and lenders reduce their risk by working with safer customers in the first place.

Predictive analytics also helps to detect fraud and identify theft. For example, your bank can be alerted to the possibility that your credit card was stolen if it’s suddenly used to make unusual purchases based on past transaction data. Predictive algorithms also help with cybersecurity across all sectors by making it easier to detect anomalies and potential security threats as they appear.

Customer Management

Predictive analytics tools help businesses keep track of and engage with customers in several ways. They can analyze customer churn rates to find explanations for why it happens and create strategies to prevent it in the future. They can also use insights gathered from CRM systems to forecast future customer spending and determine when they should promote special offers. Predictive analytics models even help with inventory management so that companies can ensure their supply always keeps up with demand.


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Predictive analytics has been a boon for healthcare services. Hospitals have a much easier time managing their supply trains, and they can use predictive capabilities to ensure patients are ready to be discharged and avoid penalties for readmissions within 30 days.

Real-time data helps with monitoring the vitals of patients with the highest risk of complications, and predictive models help with risk assessment for problems like cardiac arrest, stroke, and infections. Hospitals can even use predictive insights to determine a patient’s likely reaction to certain medications and procedures to tailor the best course of treatment for each patient.

The technology for predictive analytics has technically existed for decades, but its importance and popularity has exploded in recent years. This is largely due to big data turning automated options into necessities as well as more affordable and easy to use technology simplifying the process. Predictive analytics is a crucial part of any enterprise’s business intelligence solution.

Interesting related article: “The importance of analytics in the business world.”