Glossary term

Predictive Analytics

What Is Predictive Analytics?

Imagine if you knew what was going to happen to your business, and when it would occur. You’d be able to plan more effectively, make better decisions, and increase profitability. Luckily, you don’t have to imagine that anymore — predictive analytics can make that dream a reality.

Predictive analytics (PA) forecasts future results and answers questions like, “What’s most likely to happen, and what can we do to change that outcome if it doesn’t align with our desired end result?”

But first, what exactly is predictive analytics?

Predictive analytics is a form of advanced analytics that uses historical data, artificial intelligence, machine learning, statistical modeling, and data mining techniques to make predictions about future outcomes. It helps enterprises identify trends and disruptive industry changes and allows them to plan for unknown events and discover ways to use these insights to their advantage.

Wondering how predictive analytics can work for you? Read on to find out!

Why Your Organization Should Care About Predictive Analytics

Businesses of all sizes are turning to predictive analytics to solve complex problems and discover new opportunities. If your organization isn’t using predictive analytics already, you should get started today. Check out a few ways PA can help make a real impact, real fast

Detecting Fraud 

By combining multiple analytics models and reviewing your organization’s historical data, predictive analytics can detect abnormal patterns and prevent criminal activities.  

For example, The Pegasus Group leveraged RapidMiner’s real-time scoring capabilities to create a model that scored HTTP requests, detected attackers, and blocked them in real-time, providing better protection against bots in web applications. 

Reducing Risk 

Predictive analytics utilizes your business’s past and present data to mitigate risk, especially when it comes to sensitive, highly regulated matters like personal data, insurance claims, financial transactions, etc. Backing your risk management framework with predictive analytics allows you to understand potentially risky situations and avoid them at all costs. 

In 2012, SustainHub used RapidMiner’s data mining capabilities to check for errors and omissions in data, flagging products that might pose a risk to their supply chain. 

Optimizing Marketing Campaigns 

Organizations use predictive analytics to understand consumer purchasing behavior, predict future trends, and plan their marketing campaigns accordingly. Predictive analytics not only helps businesses decide which demographic to target, but also to identify the proper channels and timing to run marketing campaigns. This helps businesses attract, retain, and even grow their most profitable accounts.  

For instance, Qlikview uses RapidMiner’s segmentation solution to break their customers into groups based on previous behavior. Since segmenting their customer base, they have a better understanding of their consumers’ typical customer journey, and they can better communicate with and sell to both prospects and existing customers. 

Streamlining Operations 

Predictive analytics also helps manage resources and ensure operations are as efficient as possible. PA is particularly useful in service-based businesses, such as hotels, as it can determine the ideal number of guests per night to yield the maximum occupancy and optimal revenue, as well as in airlines to set appropriate ticket prices based on fluctuating demand. 

For example, a popular automotive manufacturer leveraged RapidMiner to make demand more manageable across the supply chain, optimize orders, and generate more accurate sales forecasts. 

Industry-Specific Use Cases

Still not convinced PA can make a big impact on your business? Here are a few ways today’s top enterprises are successfully leveraging predictive analytics. 

Manufacturing: Predictive Maintenance 

For most manufacturers, equipment failure and machine downtime can slow down business operations and cost millions in repairs and lost employee production time. 

Predictive maintenance helps managers monitor the equipment’s performance and condition to predict failures before they happen by using sensors that push real-time data outward. Predictive algorithms then forecast wear and tear and recommend fixes to avoid future malfunctions and save time, money, and effort.  

RapidMiner has helped Lufthansa predict airplane equipment failures, allowing them to reduce downtime by 20 percent, lower operating costs, and prevent major disasters from occurring. 

Retail: Improving Customer Experience 

Today, online shoppers expect a seamless retail experience with easily accessible customer reviews, product comparisons, and recommendations based on their purchase history. To deliver this, retailers need to analyze, process, and act based on data.  

Predictive analytics lets retailers use past consumer behaviors to predict what they’ll want (and need!) in the future. With predictive analytics models, companies can check how effective their promotional offers have been and increase the likelihood that their next campaigns will perform even better. 

Healthcare: Boosting Patient Outcomes

We’ll put a disclaimer right up front—predictive analytics can’t determine the risk of illness in patients. However, the healthcare industry can use PA and data mining as tools in their toolbox to help identify which patients are at higher risk of an illness based on previous doctor visits and family history. That way, patients at high risk can be prioritized for urgent care in emergency situations, and potentially diagnose serious illnesses quicker than before. 

Predictive analytics also allows health insurance companies to examine risk patterns among patients of the same age, with the same conditions, and from the same social determinants of health. This information allows health insurance companies to make more informed, equitable financial decisions. 

A Key Challenge  

Continually executing on predictive analytics in an enterprise setting isn’t always easy—mostly due to an all-too-common lack of trust in AI. 

If you don’t have buy-in from your executive team, your data science initiatives won’t go anywhere. It’s essential for data scientists to demonstrate the potential impact of their models to build trust in data science, but also for teams to invest in upskilling all their employees to better understand, and even execute, data science initiatives of their own. 

Predictive Analytics for Everyone 

Organizations of all sizes can benefit from using predictive analytics to predict when supply chain disruption will occur, machines will fail, and customers will make a purchase. This information is as good as gold—and your business could be using it to become more profitable and face less customer churn. 

However, you can’t do it alone. Investing in a data science platform that allows everyone, not just data scientists, to harness the power of the data you already have and turn it into real results, will impact your bottom line even more positively.

Want to learn more about what machine learning could do for you? Check out A Human’s Guide to Machine Learning Projects to help your team kickstart your first machine learning initiative today! 

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