What Is Predictive Analytics? (With Uses and Examples)

By Indeed Editorial Team

Published June 10, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Many companies use predictive analytics to determine how they are likely to perform in the future. Experts in most businesses can use data and statistical modelling to generate these forecasts, helping them plan for risks and improve productivity. Understanding how organizations use predictive analytics can help you develop an efficient system for a company. In this article, we provide an answer to "What is predictive analytics?", highlight the benefits of using these analytics, discuss some of its uses and constituents, and review some examples of predictive analytics.

What is predictive analytics?

Anyone interested in using data to generate a business forecast may want to know the answer to "What is predictive analytics?" It's a process that businesses can use to make forecasts about various factors that affect its operations. Professionals use past and current data, statistics, and analytical reasoning to make forecasts or models for the future. Depending on the company and the industry, this process may involve technology, such as artificial intelligence, automated computer programs, data mining, or machine learning. Professionals use these tools and data to determine what might happen in the future based on previous events.

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Benefits of using predictive analytics

Here are some reasons predictive analytics are beneficial to businesses:

  • Minimizes risks: Businesses can use predictive analysis to assess and anticipate risks. For instance, financial institutions may use analytics to reach proper loan resolutions based on forecasted risks.

  • Enhances marketing operations: The analysis can assist marketing companies and divisions plan more effective marketing initiatives that reach more clients.

  • Improves efficiency: You can use these analytics to assess data and project performance. The analysis can help you identify areas that may benefit from upgrading, which may help increase efficiency within the company.

  • Simplifies decision-making processes: These analytics can help you make decisions using company statistics. You can use existing data to help recognize patterns and make informed decisions.

  • Helps detect fraud: Incorporating analytic techniques can enhance pattern recognition and may help avert criminal activities. By using behavioural analytics, a company can assess all actions in a system to identify irregularities and identify fraud and overcome persistent threats.

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Uses of predictive analytics

Here are some common uses of predictive analytics in diverse settings:

Banking and financial institutions

The banking industry and other financial establishments can apply these analytics to assess large data sets. They can employ technological knowledge and tools to reduce the possibilities of fraud, appraise a loan's risk, and develop marketing approaches. For instance, institutions may use programmed computer practices to assess a client's operation and monitor irregular actions.

Government agencies and departments

Government agencies may find these analytics helpful in improving cyber security. They may apply mathematical analysis and computer software to examine risk on their websites. They may also conduct assessments based on factors like population growth.

Insurance agencies

Insurance agencies may complete a predictive analysis to review a claim and assess a client's risk before drafting a new policy. For example, a home insurance company can use information concerning the home, together with its age, size, and location, to generate a personalized policy for the client. This may help their decision-making process and increase productivity.

Supply chain companies

Companies taking part in the supply chain might conduct predictive analysis to make forecasts. For example, manufacturing companies and shipping corporations may use this process. These analytics can help them estimate how much stock they require to increase their efficiency. Predictive analysis may also help them enhance quality and reduce risk in the manufacturing process.

Utility providers

Utility companies, such as electricity, oil, and gas providers, may find these analytics helpful in enhancing the effectiveness of their operations. Such companies may use existing information to predict the best time to renovate or change their tools. They may also use data to assess risk factors that affect their operations.

Retail stores

Retail businesses might conduct predictive analyses to assess their client's needs. They may collect information to help predict the items that customers require and the items they are likely to purchase. This analysis helps businesses establish fair and effective prices. The analytics can also enable them to generate a marketing plan, showing the number of items they aim to sell in a specific period. This helps increase their income and enhance productivity.

Healthcare providers

Understanding the health history of a patient requires their health data. Predictive analysis techniques help identify illness by diagnosing it based on historical information. With the help of specific health aspects, these analytics can help doctors diagnose the underlying causes of ailments. Timely analytics help them work on remedies earlier and increase the chances of a favourable prognosis.

Entertainment companies

Entertainment companies can use algorithms and representations to visualize what customers might like to watch based on their past preferences. These companies may apply predictive algorithms to propose content for clients based on ratings, type of shows, and keywords. The system, which employs highly sophisticated analytics, can help predict customers' behaviour to help ensure a constant content supply to meet clients' demands.

Related: What Is an Internal Analysis and How to Conduct One

Components of predictive analytics

Here are some elements of predictive analytics you can expect during the analysis process:

Data collection

Data collection is a fundamental aspect of predictive analysis. Data is important to allow descriptive models to predict future results. Businesses can select from a range of tools and procedures to collect pertinent data. Organizations may apply data mining methods, computerized solutions, or physical data entry to gather information for their forecasts.

Analysis

Businesses can employ statistical analysis and frameworks to assess outcomes and establish the next step for the company. Most businesses use programmed procedures to improve the efficiency of this process. For instance, a manufacturing firm can design a program that examines the predictions and establishes how much stock to purchase based on the framework.

Modeling

Predictive analysis mostly incorporates modelling, though this can be unique for each organization. You may use one of these approaches to develop frameworks showing your predictions:

  • Regression: This element of the numerical analysis process shows patterns connecting data. You can use this approach to help establish how specific aspects, like price, influence their processes.

  • Decision trees: Decision trees are straightforward and one of the most effective techniques for developing and envisaging predictive representations and algorithms. The tree splits data into smaller sections.

  • Neural network: This modelling approach mostly shows complicated prototypes. Usually, businesses use artificial intelligence to auto-generate these models using big composite data sets.

Examples of predictive analytics

Explore these examples of how predictive analysis applies for diverse industries:

Customer service example

Consider this example of how a tech firm uses predictive analytics to improve its customer service:

Bestware Solutions is a tech firm that sells software solutions to customers. The customer support section opts to employ predictive analytics to help enhance client satisfaction and increase the team's general productivity. First, the in-house tech division develops a data collection process to store information automatically relating to user calls, sales, satisfaction rates, and rankings. The company uses figures to examine how specific services, such as calling the customer back immediately, or mentioning the customer's first name when following up on a concern, may enhance client satisfaction rates.

Bestware Solutions also creates a computerized program that helps interpret information. This allows analysts to assess how using a particular method may increase client satisfaction rates and improve sales in the next phase. The customer support executive can assess this prediction to develop a new code of behaviour for their team. The team then looks at how best to employ these actions to help achieve the predicted outcomes.

Human resources example

Here's an example of how you can use predictive analytics to determine a company's hiring needs:

Jane is a human resources manager at Beety Productions. The firm aims to grow its operations efficiently by recruiting new employees. Jane applies predictive analysis to evaluate the organization's hiring requirements. She first gathers data about the existing employees and uses past information to examine how many employees work on every task. She also collects data about retention rates and remuneration. Jane uses a software program to arrange this information and create forecasts about future recruitment requirements. This can help Jane recruit the optimum number of employees for future assignments.

Marketing example

Below is an example of how you can apply predictive analytics to establish the success of a specific marketing strategy for a business:

James is a marketing executive at JollyApex Digital Ltd. The marketing team employs predictive analytics to study the effectiveness of campaigns. They gather information on the target market and apply data mining tools to get client information, including location and age. The team uses representations to predict the effectiveness of specific alterations in marketing strategies. They use modelling to evaluate various campaign approaches, including emails, social media, and commercials. The team then uses the outcomes to choose the most efficient technique. James and the team determine that social media is a successful way to reach key clients.

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