5 Applications of Predictive Analytics
Predictive analytics will be a major player in turning knowledge into power
What’s in a name? For predictive analytics, quite a lot the name tells you the basic premise of what the practice hopes to accomplish. Predictive analytics is about using existing data about past events to put the present in context, and forecast potential future events and how to handle them. In other words, learning to recognize a pattern. If Action A has resulted in Outcome B in 80% of previous scenarios, and Action A is happening now, then there’s a strong chance that Outcome B will follow. In this way, it’s not so much about seeing into the future as it is about making educated guesses based on previous data. The more data you have, the more reliable a guess you can make.
There are quite a few ways that this technique can be applied to how you conduct business in order to make informed decisions and stay ahead of the curve. Here are five ways you can put predictive analytics to work for you.
Why is customer loyalty so important? After all, if you can replace customers who terminate your services with new ones, have you really taken a loss at all? Actually, yes—acquiring a new customer is almost always more expensive than retaining an existing one. While it’s always good to be converting leads into new customers, it pays to keep the ones you have, too. So how do you prevent losses? By looking at data regarding previous customers who have left, you can use predictive analytics to flag those clients who are exhibiting behaviors indicative of the same potential outcome—empowering you to address concerns before they become major issues, so you can drastically increase customer retention.
Insight into the Sales Funnel
Getting into the mind of the average lead can be a tricky task at the best of times, but by using predictive analytics, you can create a behavioral model of their journey through the sales funnel, and what individual actions—returning to the site repeatedly, for example, or browsing related products—have to say about their intent to purchase.
Not all applications are sales-related. Any scenario where insight into potential outcomes can guide the decisions made by you and your team is a good candidate for predictive analytics. Predictive algorithms are a valuable tool in discerning the risks involved in a particular investment or another course of action. By comparing the conditions of the present against historical figures to identify risk factors, you can tailor your decisions to mitigate risk and ensure success.
Forecasting sales figures in advance is a little bit more complicated than just expecting a big boost around the holiday season (though you should never neglect it). Everything from the value of the dollar and the cost of living to time of year, weather trends, and even politics can have a vast impact on the sales landscape. An individual analyst may be able to model a forecast based on a few key factors, but a comprehensive and data-driven predictive algorithm has the potential to factor in everything under the sun.
Every company is unique, and faces unique challenges—but these unique challenges are little more than a new combination of a set of finite factors. So while you might not immediately think of using predictive analytics to help your business out of a tight spot, the right algorithm could help you make sense out of a whole mess of data that previously appeared meaningless. These insights can help navigate difficult financial times, putting you head-and-shoulders above the tide and giving you the tools to come out stronger than ever.
There’s an old adage that knowledge is power, and in the world of business, this proves true no matter how cliche it might sound. Those companies that can take raw data and turn it into actionable intelligence will thrive. Going forward, predictive analytics will be a major player in turning knowledge into power.