Predictive analytics is a powerful business tool, enabling organizations to race to the front of the pack—sometimes literally. It’s used in Formula E racing to analyze weather conditions, tire wear, and the distance between cars to tell the driver how to best navigate the course and get the most out of their vehicle.
Even if race car driving isn’t your primary business, you can still use predictive analytics to fuel your organization’s “engine” and streamline your operations—hopefully making your enterprise the first to cross the finish line. Predictive analytics involves using historical data, AI, ML, and data mining to predict what’s likely to happen in the future. What business wouldn’t benefit from having a technology-powered crystal ball?
Read on to discover five real-world predictive analytics examples and see how data science can help you race to the front of the pack.
5 Industry Examples of Predictive Analytics
Predictive analytics can have an impact across industries—whether it’s by telling you when a faulty machine needs to be replaced or alerting you that a customer account is at risk of churn. Here are five key areas where predictive analytics can elevate your business.
1. Fine-Tune Your Revenue Model with Price Optimization
Armed with predictive analytics, you can gain a strategic advantage and optimize your pricing. Rather than grappling with disequilibrium between supply and demand levels, predictive analytics allows you to automatically adjust your price model based on real-time data, ensuring you not only give customers attractive deals, but also maintain profitability.
For example, an electronics retailer can alter its pricing strategy to ensure it clears old inventory in time for new stock to arrive. Using a machine learning algorithm, the store can identify how long it takes for items to sell at different price levels. If a Bluetooth speaker tends to sell within two weeks when priced at $119.95, but it sells within an average of five days when priced at $109.95, the store can choose to set the price at $109.95 to get it off the shelf quicker.
ML models can also continually study both the frequency of purchases and inventory levels to automatically recommend pricing changes depending on how much stock the company has left in its warehouse.
2. Use Demand Forecasting to Boost Future Market Performance
Demand forecasting involves using predictive analytics to study historical data and figure out how customer demand for certain products may fluctuate in the future.
For example, suppose a telecom company uses an SD-WAN system to serve customers across different states. By optimizing how calls, text messages, and data flow through its network, the telecom can ensure seamless service for its customers and fewer headaches for its IT team.
Using a predictive analytics system, the telecom models data usage for different times of the year. The system discovers that data usage peaks in the days leading up to Thanksgiving as users reach out to each other to coordinate their annual family celebration. The telecom company can leverage this data to ensure its SD-WAN is ready to handle a higher call volume and perform optimally as turkey day approaches.
3. Increase Revenue by Optimizing Customer Lifetime Value
Customer lifetime value (CLV) is the product of average customer purchase value times purchase frequency times average customer lifespan. Even though the formula is simple, the factors involved in determining customer value can vary greatly from one organization to another. This is where predictive analytics can come to the rescue.
Say a restaurant chain issues a customer benefits card that users can present when they make purchases—either online for delivery or in person. They can use predictive analytics on the card’s data to calculate the type and number of purchases individual customers will make over their average lifetime value, which they’ve calculated to be a certain number of years.
With this data, the restaurant can choose which specials to highlight during promotions, which dishes to phase out, and how much to charge for different items to boost the average CLV for different geographical areas.
4. Stop Bad Actors with Fraud Detection
In a global, digital-first environment, it can be difficult to prevent fraud, especially because bad actors use advanced technology to avoid detection and maximize their payloads. But, by using predictive analytics in conjunction with behavioral monitoring, you can predict criminal activity before it occurs by examining data connected to attempted purchases.
For example, a consumer might try to use a chargeback scam and make a purchase online but claim the product never arrived or arrived damaged to get their money back from the seller. Meanwhile, they either keep the perfectly good item or sell it for a profit.
Using predictive analytics, a company can collect and analyze several data points in connection with each order before it’s placed. They can use this to calculate the probability of the order being fraudulent. For example, the following criteria could be used:
- Country the order is coming from
- The number and frequency of orders made by that account in the past
- If any previous users were also claimed missing or damaged
- The age of the user’s account
If the model detects anything suspicious, it can flag the activity and alert the credit card company or retailer immediately.
5. Use Data to Drive Your Quality Assurance Program
Predictive analytics can empower your quality assurance program with data-based insights. In this way, you can get ahead of a small quality issue before it balloons into a real problem.
For example, a logistics and transportation company can use predictive analytics to provide more accurate transit times to their clients. They can then forward this information to their online customers, so they know exactly when their package is going to arrive. This is particularly helpful in a business climate impacted by supply chain issues.
For instance, in manufacturing, if there’s a hold-up at a port used by a factory in China, this information could be automatically used to recalculate the delivery window of any impacted items. That way, the client can get their customers the information they need to avoid unexpected delays.
Race Past the Competition with Predictive Analytics
Every day, businesses generate more and more data, and it’s time to start putting it to use. According to Harvard Business Review, 86% of surveyed executives say that AI became a “mainstream technology” at their company in 2021. By harnessing the power of predictive analytics, you not only create a more efficient operating model, but also set yourself up to become a competitor in your industry.
Still trying to figure out where to start? Check out our library of AI use cases, 50 Ways to Impact Your Business With AI, and get inspired to get started today!