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Data analytics has many interesting applications and use cases if you’re a data engineer or scientist. If you’re a stakeholder in a business, you want tangible, practical use from those complex data sets. In this area, one specific area of analytics shines: predictive analytics.
Today, we’ll show you how predictive analytics works and helps with educated decision-making, as well as some practical examples from various industries.
Before we discuss predictive analytics, let’s take a look at the basics of data science to better understand where it fits in.
These are the main types of data analysis:
If you want to get the maximum out of your existing data and business intelligence tools, you should run all types of data analyses. However, you could simply focus on predictive analysis e.g. to prevent churn or improve customer experience.
Predictive analytics is a type of advanced analytics that uses historical data, machine learning techniques, and statistical algorithms to predict future events. Predictive analytics uses tools such as artificial intelligence to identify patterns in past data and then forecast what is likely to happen in the future, helping companies make better decisions based on real-time insights.
Key elements of predictive analytics include:
There are two main types of models in predictive analytics.
Regression models predict a continuous outcome based on one or multiple variables. For example, forecasting future sales based on historical sales metrics and data.
Classification models predict a categorical outcome and classify data into distinct categories or groups. For example, using logistic regression or decision trees to predict if a customer is likely to churn based on their previous behavior.
Modern AI predictive analytics tools rely on both models to provide accurate results.
There are many real-world scenarios and examples where predictive analytics can be extremely useful. However, figuring out your own predictive analytics solutions from scratch can be difficult if you’re just getting started. Here are some ways to make the most use of predictive analytics.
In the healthcare industry, predictive analytics is used for a variety of use cases. For example, you can use it to predict when critical equipment can fail. Machine learning algorithms can consider machine usage, age, and performance data, which allows healthcare providers to determine when downtime might happen.
You can also use it to predict seasonality in healthcare. For example, you can see the most common illnesses and when they happen in the year. E.g. the flu season could cause a disruption in how the healthcare provider works. Predictive analytics can give them actionable insights on when they should hire more staff and when things are likely to get busy, which improves patient care.
If you’re wondering how e-commerce and brick-and-mortar stores rarely run out of their most popular products, predictive analytics is a huge piece of the puzzle. Retail businesses can use algorithms to determine which products sell well and when, optimizing their supply chain.
Over time, this helps with optimizing inventory levels, determining market trends, managing stock, and improving overall customer satisfaction because they’ll always get the product they’re looking for.
Imagine you run a bank. There is a huge number of data points for each client, and making a data-driven decision on the spot can be difficult. With predictive analytics, banking institutions can take a look at a client’s previous credit history and the loans they took. With these smart workflows, they can spot whether the client paid off their debts in time.
By getting familiar with their payment behaviors through statistical modeling, they can determine if lending them more money brings profitability or if it’s likely to lead to a default.
Now you run a company that makes egg cartons and you want to ensure that operations run smoothly, so you store tons of real-time data on your equipment. You monitor your machines and keep a log of equipment failures and how long the downtime lasts. You also add data points about the regular servicing cycles.
With all the data in one place, you can create a data visualization that accurately shows you when a piece of machinery is likely to fail next, so you can do preventative maintenance ahead of time and ensure the world is not left for want of egg cartons.
Now you have your own cell phone carrier and you sell mobile service to certain demographics. You have contracts in place and you want to prevent churn, i.e. users switching to another carrier and this is where predictive analytics comes into place.
You analyze customer behavior, customer service interactions, the number of complaints, how long someone has been with your company and a few more data points and the results are clear. You’ll easily determine which customers are likely to churn and leave soon, so you can jump on the phone and ensure better customer retention.
Energy companies such as Enersee use predictive analytics to determine consumption patterns. They consider various data points such as historical consumption, weather, and economic factors. This way, they can easily determine if a major increase in energy consumption is about to occur due to e.g. drought season.
Did you know that 90% of turns that UPS trucks make when they deliver packages are right turns? This is not an accident but a consequence of good predictive analytics. They crunched the numbers and figured out that left turns are inefficient and cause more delays and fuel spent. In fact, they saved over 10 million gallons of fuel in one decade because of this big data hack.
Predictive analytics can be used in the transportation industry to optimize routes for delivery based on traffic pattern analysis, weather conditions, and historical data on deliveries. This ensures speedier delivery, less time wasted sitting in traffic, and more fuel saved.
In the insurance industry, predictive analytics is commonly used for fraud detection. When a new claim pops up, an insurance company analyzes historical records, external data sources, patterns in claim data, and other items to find out how likely the claim is to be fraudulent.
Educational institutions can’t keep tabs on all of their students as that would require far too much time and money. Instead, predictive analytics can factor in data points such as attendance rates, grades, student engagement, and more.
They can then provide more support to students who are struggling and at risk of dropping out. This leads to better retention rates and academic results.
Predictive analytics is immensely useful in marketing campaigns. Based on user behavior, companies can do customer segmentation and split up their entire customer base into smaller segments.
For example, if you sell sporting goods, you can create segments with customers that are more interested in certain sports and product types. With this information, you can create more customized and effective marketing strategies on social media or through email.
Employee turnover is the specialist term for when someone quits their job. And with predictive analytics, much of that turnover can be prevented. For example, a company called Strobbo uses predictive analytics to determine staffing needs in industries with flexible staffing and scheduling.
Predictive analytics analyzes employee performance reviews, engagement levels, job satisfaction scores, employee survey results and more. As a result, you get an accurate depiction of who your (un)happiest employees are.
Have you ever logged into Booking or a similar platform one day, and a few days later, the price for the same accommodation is vastly different? This is largely due to predictive analytics.
Travel and hospitality platforms use predictive analytics automation to adjust pricing for accommodation based on factors such as seasonality, competitor rates, booking patterns and more.
Predictive analytics is one of the most important future trends in data analytics. While AI-based predictive analytics tools are still not good enough to replace data scientists, they can be of massive help when making more informed decisions.
At Luzmo, we support you with visualization and actionable decision-making on top of your complex predictive models. We offer embedded analytics tools that you can use in your software and give your users access to predictive analytics. Put them in the driver's seat and give them power over their own data.
Get a free demo of Luzmo today to see how we can help make your predictive analytics insightful!
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
