Why Predictive Analytics Is Important for Your Customer Analytics Strategy
On a daily basis, my inbox is flooded with emails. There is so much information to be processed and so much data to look at that it can at times feel overwhelming. I’d wager this phenomenon is not one I’m facing alone.
Every day, marketers are getting more and more information about what their customers are doing. As a result, customer analytics and predictive analytics have become increasingly important. You’re collecting data from digital marketing, social media, call centers, mobile apps, and more. But how do you take that data and make it actionable? How do you use the information on what your customers have done in the past to gauge what they may do in the future? The answer to these questions is simple: you use statistics.
Previously, I talked about customer analytics and its importance to your business. Predictive analytics is a huge piece of the customer analytics puzzle. Understanding what our customers are likely to do is at least as important as what they have done or purchased in the past. To have a truly robust understanding of your clients and their needs, predictive analytics needs to be part of your toolset.
Predictive analytics has many business applications. Ultimately, this science helps you understand what your customers are likely to do based on what they’ve done in the past. Many companies shy away from this science because it seems like such an overwhelming concept. Isn’t using statistics to predict behavior terribly futuristic? Not as much as you’d think. Predictive analytics is something you likely encounter every day.
For instance, your credit score is calculated using predictive analytics. Based on a set of data points regarding your payment history, loan applications, employment history, and more, financial institutions calculate your credit worthiness. In their eyes, your credit score is a predictive indicator that uses statistics to determine how likely you are to repay a loan. This science has been used for years. That said, predictive analytics is growing in both popularity and accuracy. Plus, new tools on the market make it much more accessible to the average business.
Still, it can be tough to understand what goes into the calculations behind this science. Predictive analytics would be nothing without statistics—a word many find daunting. Not to fear. Statistics can be easily accessible and interesting when explained well. In fact, my colleague John Bates can even use statistics to explain why there is a correlation between poison and knitting. How much more fun could science be?
You may be asking yourself how this could possibly be fun. In fact, you could even be asking yourself what a correlation is. Don’t worry. I’ll make this fun by giving you a job we’ve all always wanted. Imagine you are a detective. Your job is to solve a mystery. This mystery may be something as pertinent to your business as finding out whether mobile app users are more loyal. To solve that mystery, you’ve got a set of data points (which is kind of what facts and clues really are anyway).
The set of data points we’re going to look at encompasses all the facts you have as a detective. These facts can include information about Web behaviors, mobile interactions, call center experience, point of sale information, social media activity, and more. This set of data points is what organizations refer to as Big Data.
The problem for most detectives is that Big Data is just what it sounds like: it’s a ton of data. A huge data set can be incredibly hard to sift through and understand. In fact, Deloitte published an article earlier this year stating that in a survey of 100 CIOs, Big Data was expected to be one of the biggest technological disruptors of 2013. Most companies simply don’t know how to handle the volumes of data they now have access to. So how are you going to sort through that and solve your mystery in a fashion that won’t take years? That’s right: statistics! Using statistics to power predictive analytics will make your detective work successful.
A great example of this is The General Automobile Insurance Services. By analyzing its Web traffic, this enterprise found that users tended to stop the application process when they landed on a page requesting their VIN number. The General used this information to create a program that prepopulates the VIN number based on other information, resulting in an increase in conversions. They used statistic and customer data to predict how a change in their interface could increase customer satisfaction and sales.
Still not sure you need statistics and predictive analytics? Consider it this way: statistics let you determine the relationship between certain data points. Without this, you’re making educated guesses at best. Statistics works like a lever, allowing you to work with much larger data sets than you normally could manage the same way a lever allows you to lift much heavier loads than you, as a mere mortal, would normally be able.
If you’re not utilizing Big Data, statistics, and predictive analytics, you can’t get to the bottom of your business questions, create effective strategies, or accurately market to your customers.
If you’re looking to learn more on predictive analytics or statistics, check out this great predictive analytics reading list at Forrester. And, of course, keep stopping by to read more on my blog.