What Is Predictive Analytics & Why Is It Important?

Have you ever thought about how cool it would be to predict the future? Imagine being able to predict the exact type of product or service your customers would buy. Or even seeing what a competitor is going to do down the road and beating them to it.

Predictive analytics works in a similar way to help forecast what can happen in the future. You’re able to look for patterns and predict potential outcomes. It can be a powerful tool for your business to get ahead of the competition.

Here’s What We’ll Cover:

What Is Predictive Analytics?

Why Is Predictive Analytics Important?

Key Takeaways

What Is Predictive Analytics?

Predictive analytics uses different modeling techniques to make predictions of future outcomes. Using things like statistical modeling and machine learning, you can generate future insights. And it gives you the ability to take past or current data to forecast future trends.  

When you can see what’s coming down the road, you can prepare resources to leverage trends or behaviors. Predictive models get used all around us on a daily basis. They can get used for things like weather forecasts and video game development, to name a few. 

How Does It Work?

You can use techniques like data mining and artificial intelligence to help forecast potential outcomes. The data is put into a mathematical model that looks at different trends and patterns in the market. Then, the data from that model is used to predict what’s going to happen with actionable insights. 

Understanding customer behaviors can give you a competitive edge. Getting inside the mind of past behaviors and spending habits provides applicable data. You can then identify opportunities to optimize your product or create new offers.

Think about when you’re watching Netflix. You have watched a bunch of different TV shows and movies and really enjoy action and adventure. Based on your viewing history, predictive analytics recommends new content to watch.

The Three Most Common Predictive Modeling Techniques

There are lots of modeling techniques out there. But there are three classification models that are most commonly used.

Regression

Regression models look for patterns in data sets. You can use regression to figure out how much influence something has. And it can take into account two or more different variables to help predict an outcome.

Decision Trees

This method uses different variables to understand how and why decisions get made. The branches of the tree signify a choice and the leaves represent a decision. The decision tree then determines which variables affect a decision most. 

Neural Networks

This model is more than capable of handling massive amounts of data. Neural networks can be handy to confirm the data found when using decision trees. They also work well if there’s no formula or if a prediction is more important than explaining the data.

Why Is Predictive Analytics Important?

Information is the key to making smart business decisions. And the data that predictive analytics generates is a gold mine of information. 

When it comes to data analytics as a whole, there are a few other types worth highlighting. Descriptive analytics, for example, tries to explain what has happened in the past. Diagnostic analytics then takes that information to explain why it has happened. Generally, this is through predictive insights. 

Then there’s prescriptive analytics. This takes into account different variables and scenarios to determine outcomes. Then, predictive analytics uses that information to forecast what can happen next. 

It can be used for things like detecting and combating fraud and reducing overall risk. On top of that, predictive analytics can also identify new opportunities. 

Industries Using Predictive Analytics

One of the greatest things about predictive analytics is that any business in any industry can use it. You can use it to limit risk, generate more revenue or streamline operations. Some industries and businesses that are using predictive analytics include:

• Banking and financial services for fraud detection and to measure credit risk
• Health insurance companies to identify chronic diseases and determine who’s most at risk
• Oil and gas to forecast resource needs and improve performance
• Retail companies to predict the offers consumers want most and overall merchandise planning
• Governments to understand the behaviors of consumers and predict cybersecurity risks
• Manufacturing companies to optimize distribution and predict warranty costs
• Aerospace to predict fuel consumption and aircraft reliability

Key Takeaways

It doesn’t matter what your company does or what industry you are in. Using predictive analytics benefits anyone who wants to positively influence their business outcomes. 

It also gives an inside look into future behaviors and evolving trends to stay ahead. You can make better decisions for customer service or for your marketing efforts. Plus, creating a solution to a potential problem can identify new opportunities. 

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