Predictive analytics has become a popular concept, with interest steadily rising over the past five years according to Google Trends.
Increasingly often, the idea of predictive analytics (also known as advanced analytics) has been tied to business intelligence. But are the two really related—and if so, what benefits are companies seeing by combining their business intelligence initiatives with predictive analytics?
What Is Predictive Analytics?
Predictive analytics refers to using historical data, machine learning, and artificial intelligence to predict what will happen in the future. This historical data is fed into a mathematical model that considers key trends and patterns in the data. The model is then applied to current data to predict what will happen next.
Using the information from predictive analytics can help companies—and business applications—suggest actions that can affect positive operational changes. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue. At its heart, predictive analytics answers the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?”
Predictive Analytics in the Real World
For many companies, predictive analytics is nothing new. But it is increasingly used by various industries to improve everyday business operations and achieve a competitive differentiation.
In practice, predictive analytics can take a number of different forms. Take these scenarios for example.
Identify customers that are likely to abandon a service or product. Consider a yoga studio that has implemented a predictive analytics model. The system may identify that ‘Jane’ will most likely not renew her membership and suggest an incentive that is likely to get her to renew based on historical data. The next time Jane comes in to the studio, the system will prompt an alert to the membership relations staff to offer her an incentive or talk with her about continuing her membership. In this example, predictive analytics can be used in real time to remedy customer churn before it takes place.
Send marketing campaigns to customers who are most likely to buy. If your business only has a $5,000 budget for an upsell marketing campaign and you have three million customers, you obviously can’t extend a 10 percent discount to each customer. Predictive analytics can help forecast the customers who have the highest probability of buying your product, then send the coupon to only those people to optimize revenue.
Improve customer service by planning appropriately. Businesses can better predict demand using advanced analytics. For example, consider a hotel chain that wants to predict how many customers will stay in a certain location this weekend so they can ensure they have enough staff and resources to handle demand.
How Does Predictive Analytics Work?
An accurate and effective predictive analytics takes some upfront work to set up. Done right, predictive analytics requires people who understand there is a business problem to be solved, data that needs to be prepped for analysis, models that need to be built and refined, and leadership to put the predictions into action for positive outcomes.
Any successful predictive analytics project will involve these steps.
First, identify what you want to know based on past data. What questions do you want to answer? What are some of the important business decisions you’ll make with the insight? Knowing this is a crucial first step to applying predictive analysis.
Next, consider if you have the data to answer those questions. Is your operational system capturing the needed data? How clean is it? How far in the past do you have this data, and is that enough to learn any predictive patterns?
Train the system to learn from your data and can predict outcomes. When building your predictive analytics model, you’ll have to start by training the system to learn from data. For example, your model might look at historical data like click action. By establishing the right controls and algorithms, you can train your system to look at how many people that clicked on a certain link bought a particular product and correlate that data into predictions about future customer actions.
Your predictive analytics model should eventually be able to identify patterns and/or trends about your customers and their behaviors. You could also run one or more algorithms and pick the one that works best for your data, or you could opt to pick an ensemble of these algorithms.
Another key component is to regularly retrain the learning module. Trends and patterns will inevitably fluctuate based on the time of year, what activities your business has underway, and other factors. Set a timeline—maybe once a month or once a quarter—to regularly retrain your predictive analytics learning module to update the information.
Schedule your modules. Predictive analytics modules can work as often as you need. For example, if you get new customer data every Tuesday, you can automatically set the system to upload that data when it comes in.
Use the insights and predictions to act on these decisions. Predictive analytics is only useful if you use it. You’ll need leadership champions to enable activities to make change a reality. These predictive insights can be embedded into your Line of Business applications for everyone in your organization to use.
Predictive analytics can lead to priceless business outcomes—including catching customers before they churn, optimizing business budget, and meeting customer demand. It’s not magic, but it could be your company’s crystal ball.