A Guide to Predictive Analytics

Data is the most valuable resource modern businesses have. With data gathering technologies more accessible than ever, the need for successful data analysis processes has grown as well.

One such process is predictive analytics. When done right, it can give you a better grasp of your target consumers, improve operations, and drive revenue growth.

What is predictive analysis?

Predictive Analytics is the process of interpreting past data to make educated forecasts about sales growth and market behavior, among others.

Predictive analytics algorithms are often integrated with machine learning and artificial intelligence. Together, they help companies predict the future and influence it. Through predictive analysis, businesses can detect patterns, changes, and trends that can shape operational improvements, reduce risks, and increase conversions.

Who’s using it?

Predictive analysis no longer just concerns mathematicians and statisticians. With interactive and easy-to-use software becoming more mainstream, marketers and business analysts have started using these technologies as well.

Let’s look at how predictive analytics is deployed across different industry verticals:

  • Energy & Utilities. Predictive analysis helps the energy sector in foreseeing the demand and supply of electrical energy, reducing safety risks, and assessing weather patterns. Analysis of machine sensor data can also help providers prepare for equipment repair and maintenance.
  • Banking and Finances. Banks and other financial institutions are deploying predictive analytics to ensure that accounts of all types are secure and user-friendly. Predictive analytics can also be used to increase sales and retention by customizing packages based on the client’s profile. With huge amounts of money and sensitive data at stake, banks take advantage of predictive insights to detect frauds, measure credit risk, and predict the chances of fraud before authorizing transactions.
  • Manufacturing. Predictive analysis aids manufacturers in detecting the causes of quality and production failures. It also optimizes the entire production process from supply chain management to distribution and quality control.
  • Health insurance. Insurance agencies are leveraging predictive analytics to track claims fraud, identify at-risk patients, and tailor the right interventions. With controlled risk management analytics, providers can do real-time portfolio monitoring and enhance operational efficiency.

Why is it important?

Employees in discusion

Businesses are relying on predictive analytics to improve operations and explore new opportunities for growth. Common uses include:

  • Detecting fraud. As cyberthreats become more sophisticated, leveraging multiple analytics methods can refine pattern detection and combat criminal activity. High-performance behavioral analytics examines networks from top to bottom to spot irregularities that might indicate fraud, vulnerabilities, and malicious activities.
  • Refining marketing campaigns. Predictive analytics is used to monitor purchases, customer behavior, ad performance, and website traffic. Predictive tools assist businesses in attracting, retaining, and building a profitable customer base.
  • Streamline operations. Many firms take advantage of predictive models to anticipate inventory, manage supplies, and modify pricing.
  • Reduce risk. Predictive tools generate credit scores by factoring in data essential to a person’s creditworthiness. Credit scores are used to determine a buyer’s likelihood of default for purchases and eligibility to complete loan payments. Other risk-related applications include payment collections and insurance claims.

Today, data is easily harvested through digital clicks, social media interactions, and more. Companies must take advantage of this massive library of information to exceed customer expectations and deliver the experiences customers they’re looking for.

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