The Gist

  • Predictive analytics definition. This advanced analytics branch uses statistical algorithms and machine learning to predict future events.
  • Predictive’s role. Healthcare, marketing and insurance are just a few of the industries utilizing predictive analytics. 
  • Challenge’s ahead. Data quality and avoiding over-reliance on predictions are key hurdles to effective use of predictive models. 

Editor’s Note: This article has been updated on October 26, 2023 to include new data and information; the original content was authored by Lesley Harrison.

In the world of data science, predictive analytics is a crucial component in the toolkit of modern businesses. And as companies work through the complexities of the ever-evolving digital age, understanding the key differences between types of data analytics is paramount. There are four major types of data analytics that organizations turn to: descriptive, diagnostic, predictive and prescriptive. 

  • Descriptive Analytics: Answers the question, “What happened?”
  • Diagnostic Analytics: Answers the question, “Why did it happen?”
  • Predictive Analytics: Answers the question, “What could happen?”
  • Prescriptive Analytics: Answers the question, “What should you do next?”

Our focus today is on how predictive analytics operates, delving into its intricacies and looking at how businesses can harness its potential.

What Is Predictive Analytics?

To provide a predictive analytics definition, it’s a branch of advanced analytics using statistical algorithms and machine learning techniques to identify the probability of future events based on historical data. It also utilizes tools and techniques like data mining, modeling and artificial intelligence, all with the goal of analyzing data, identifying patterns and making predictions. Organizations that use this form of data analysis can anticipate future outcomes, identify risk and are better positioned to make decisions.

Related Article: Exploring Customer Data: Definition, Types & Usage

The History of Predictive Analytics

Predictive analytics can be traced back to early statistical models and data mining techniques, which laid the groundwork for modern data-driven decision-making. Over the years, as computational capacities expanded, predictive analysis evolved from simple linear regressions to sophisticated algorithms under the umbrella of machine learning.

The convergence of vast datasets, enhanced computing power and innovative algorithms propelled predictive analytics to the front of business leaders’ attention, allowing organizations to forecast future outcomes with high accuracy. As these methodologies matured, predictive analytics transformed from a niche specialty to a mainstream tool, assisting industries from healthcare to finance.

Predictive Analytics vs. Prescriptive Analytics

Predictive and prescriptive analytics both serve distinct roles in the world of data-backed decision-making. While predictive analytics uses past data to forecast future outcomes, prescriptive analysis goes a step further, offering actionable recommendations on what to do next based on those predictions. 

5 Examples of Predictive Analytics in Action

Predictive analytics techniques can be used to tweak and test processes across a variety of industries. Earl Sires, senior product marketer at EAB and previously digital content marketer at Rapid Insight explained, “Many industries use predictive analytics as a core part of their strategy.”

Some common use cases include:

  1. Healthcare
  2. Marketing
  3. Insurance
  4. Supply chain management
  5. Fund management 

1. Predictive Analytics in Healthcare

Healthcare workers use predictive analytics in a variety of ways to improve the efficiency of the service they provide. Effective modeling of patient data can also help improve patient outcomes. “Outside factors, known as Social Determinants, can play a greater role in your patient’s health than anything that happens within the hospital doors,” said Sires. 

2. Predictive Analytics in Marketing

Marketing is an industry that relies heavily on metrics. Marketers track clicks, engagement, views and other behaviors. Brands can use predictive analytics to take a huge database of information and score leads based on how likely they are to buy a product. This gives brands an idea of where they should prioritize their outreach to get the biggest return on investment.

3. Predictive Analytics in Insurance

The insurance industry, like the marketing industry, is driven by statistics. Accident reports and historical data are used to judge the risk factors for individual clients. Predictive analytics can help with processing claims and preventing fraud.

Jason Rodriguez of Instant Insight said predictive analytics could reduce the need for professional oversight in areas, such as loss handling and initial triage. Fraud is a huge issue for insurance companies, and models that highlight suspected fraud could save time and money for underwriters.

4. Predictive Analytics in Supply Chain Management

Sires explained that companies can use predictive analytics to “model different risk factors to see how they impact your supply chain and incorporate information from disparate sites or sources into one model to get the most accurate, relevant picture of your operation.”

The information from a predictive model can then be used to prioritize shipments or guide the creation of prescriptive models. Supply chains can be complex and have many points of failure. By examining each of these points in turn, organizations can make their supply chains more robust and adaptable.

5. Predictive Analytics in Fund Management

Deloitte looked at a major financial organization that transformed from being a risk-averse pension fund into a risk management organization. The company’s old systems weren’t capable of the more complex models required for new investments. Using predictive project analytics (PPA), the company was able to run models to determine whether the safest course of action was to update the systems all at once or step-by-step. By following the suggestions given by predictive modeling, the company completed its update ahead of schedule and under budget.

Related Article: How Can Predictive Analytics Impact Customer Experience?

Where Does Predictive Analytics Work Best?

Predictive analytics work best in scenarios where forecasting is important, especially for short- and medium-term trends. From assessing market trends to gauging customer behaviors, it’s useful in areas that demand proactive (rather than reactive) strategies. Some examples of when to apply predictive analytics include:

  • Product demand
  • Pricing strategies 
  • Revenue forecasting 
  • Customer retention 
  • Maintenance scheduling 
  • Risk assessment 
  • Talent acquisition 

Prescriptive analytics models are more complex to build, but they allow an organization to explore multiple what-if scenarios. Meanwhile, predictive analytics models focus on a more narrow set of parameters. This means these models are easier to build and can provide a quick overview of a situation.

Predictive analytics helps bring clarity and objectivity to decision-making. It can inform major spending or policy decisions in situations where managers may otherwise be prone to wishful thinking. Models cannot predict the future with 100% accuracy, but they can assist with making educated guesses.

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