What Is Predictive Analytics? Benefits, Examples, and More

Predictive analytics is one of the four key types of data analytics, and typically forecasts what will happen in the future, such as how sales will shift during different seasons or how consumers will respond to a change in price. Businesses often use predictive analytics to make data-driven decisions and optimise outcomes.

Discover more about predictive analytics, including its use, some common benefits, and what you can do to get started.

What is predictive analytics?

Businesses use data to understand what's happening—both now and in the future. Predictive analytics falls into the latter category. It uses historical data to predict potential future events or behaviours so companies can better position themselves in the present.

Predictive analytics relies on various techniques from statistics, data analytics, artificial intelligence (AI), and machine learning to calculate future outcomes. Some common business applications include detecting fraud, predicting customer behaviour, and forecasting demand.

Benefits of predictive analytics

Predictive analytics can help businesses make stronger, more informed decisions. It can identify patterns and trends within data that enable firms to make a probabilistic determination about future events. Other benefits include:

• Decision making: Improve business functions by relying on data to determine potential outcomes.

• Risk management: Security teams can use predictive analytics, among other compliance tools, to develop risk management strategies and even prioritise the most detrimental risks.

• Customer insights: Predictive analytics helps firms better understand potential customers and what they need, enabling the development of more targeted marketing campaigns.

• Operational efficiency: Predictive analytics can make companies operate more efficiently by using historical data to understand resources and better manage them.

7 areas that use predictive analytics

Many industries use predictive analytics, including financial services, healthcare, retail, and manufacturing, each with different use cases.  Take a closer look at a few below.

1. Retail

Predictive analytics is advantageous for retailers looking to understand customer behaviours and preferences. With insights from data, they can make more informed decisions about product assortment, pricing, promotions, and other aspects. 

For example, retailers might use predictive analytics to determine which products are most likely to be purchased together and then offer discounts on those items combined. They can also identify customers at risk of leaving for a competitor and take steps to keep them.

2. Banking

Banks use predictive analytics to make more informed decisions about credit, investment products, and even trade currency. Banking-related data sets form patterns identifying customers at risk of defaulting on a loan.

Banks also use predictive analytics to determine which customers might invest in new financial offerings and direct effective marketing messages towards them.

3. Sales

Sales teams use predictive analytics to understand customers’ wants and needs better. By analysing past customer behaviours, they can more accurately predict which products or services a customer will purchase. This allows sales teams to focus on selling the most appealing items to their prospects, ultimately increasing their sales revenue.

4. Insurance

Insurance companies use predictive analytics to determine the likelihood of a particular customer making a policy claim. By analysing claims history, demographics, and lifestyle choices, insurers can develop models that help them predict which customers are most likely to file a claim. This information allows them to adjust premiums and identify and target higher-risk customers with specific policies.

5. Social media

Social media teams use predictive analytics to understand user behaviour and trends. By analysing the vast amount of data users generate on social media platforms, they can gain insights into what people care about, what they are talking about, and how they interact with each other. This information improves the user experience on social media platforms and enables them to target advertising more effectively.

6. Underwriting

Underwriting insurance policies routinely uses predictive analysis. By analysing data on past claims, insurers identify patterns that may indicate a higher risk of future claims. Armed with probabilities and predictions, they can adjust premiums for individual policies or groups of policies or even deny coverage altogether.

7. Healthcare

Predictive analytics in healthcare can identify patients at risk of developing certain diseases or conditions. By analysing demographic data, health records, and genetic information, doctors and researchers can develop models that help them create health policies and interventions. They can then use predictive analytics to create targeted prevention and treatment programmes for those patients at the highest risk.

Predictive analytics: job outlook and salary

Predictive analytics falls within the larger umbrella of data science, which has a positive outlook as the demand for data skills grows. According to a 2023 survey by the World Economic Forum, data science and data analyst roles were in the top ten roles expected to grow the fastest between 2023 and 2027 [1]. Working in data science also tends to pay a higher-than-average salary. According to Glassdoor, the average annual salary for a predictive analyst is £36,560 per year, not including any additional compensation.  [2].

What jobs use predictive analytics?

The field of predictive analytics offers a range of exciting career opportunities. Some of the most common jobs include:

• Data scientist

• Data analyst

• Business analyst

• Marketing analyst

• Risk analyst

• Quantitative analyst

• Machine learning engineer

• Fraud analyst

• Pricing analyst

• Customer success manager

How to get started in predictive analytics 

To work in predictive analytics, you’ll need to be comfortable working with large data sets, have a strong grasp of data analytics and statistics, and communicate your findings clearly to non-technical audiences. Below are some ways you can gain the skills needed to become a data professional specialising in predictive analytics:

1. Education

A data scientist typically has a strong background in mathematics and computer science and holds at least a degree in data science or related subjects like IT, statistics, physics, or research. Despite these common educational credentials of some data professionals, many data scientists have taught themselves the necessary skills through online resources and personal projects.

2. Professional experience

In addition to formal education, gaining professional experience is essential for becoming a data scientist. You can gain experience in predictive analytics through internships, working with datasets in freelancing projects, and working in junior or entry-level roles. 

Many employers value relevant work experience, so previous experience working with data and analytics tools can be helpful. You'll want to build your skill set and experience in predictive analytics. Your resume may look more robust if you have demonstrable experience in:

• Predictive modelling

• Regression analysis

• Classification algorithms

• Decision trees

• Neural networks

• Support vector machines

3. Certifications

When pivoting into data analytics, earning a professional certificate or certification can be a great way to learn about the subject and gain the skills you need to do the work.

Several certifications are available for predictive analytics professionals, such as the Certified Analytics Professional (CAP) Certification offered by INFORMS. Certificates are not always required for employment, but they can strengthen your CV. 

Common certifications and certificates include:

• Google Data Analytics Professional Certificate

• Microsoft Certified: Data Analyst Associate

• Associate Certified Analytics Professional

• IIBA Certification in Business Data Analytics (CBDA)

Explore predictive analytics further. 

Predictive analytics supports businesses to use data to predict future outcomes and inform decisions based on data. You need a solid education, experience, and technical and workplace skills to work in predictive analytics. 

Learn more about predictive analytics or data analytics through Coursera. The University of Minnesota's Analytics for Decision Making Specialisation emphasises problem-solving using predictive models, linear optimisation, and simulation methods.