The emergence of Big Data has been one of the most disruptive events of the new millennium, impacting practically every industry.
Technology has leapt forward again and again over the last decade, and brought us new tools for accessing, collecting and delivering data with mind-boggling volume and velocity. With the floodgates open, businesses large and small are still looking for the best way to turn this vast ocean of disparate information into valuable insights, action steps and outcomes.
One new tool enabled by Big Data fits comfortably into the customer experience manager’s tool belt: predictive analytics. By streamlining internal and external sources of customer information, this method of data-mining is applied to anticipate an individual customer’s needs and wants with greater speed and reliability than has ever been possible.
Finding Shapes in the Cloud
Predictive analytics, in a nutshell, means identifying patterns in an existing data set and extrapolating those patterns to deduce what is most likely to occur next. Businesses were already doing this before the Information Age, but by largely outsourcing the task to algorithms, we’re now able to crunch much larger data sets in much less time and come out with much more nuanced portraits of customers.
Another advantage of predictive analytics is the ability to quickly and easily drill down to the individual level. A single customer produces a wealth of data on a daily basis by simply going through the motions of his/her life. By applying resources to examine just that customer (rather than the general demographic or profile he/she fits), a business can design a tailor-made experience with the best likelihood of producing the desired outcome – be that sales, loyalty, or resolution of a complaint.
Export that ability across every individual in your customer base, and you can see how the lines between responsive and proactive are blurring. For example, Wells Fargo rolled out ATMs that deliver a unique display of buttons and options each time a customer signs in, reflecting how that particular customer has used ATMs in the past and will likely use it this time.
Big Data vs. Big Brother
In a way, predictive analytics has taken us back to the Main Street General Store model of doing business, where the proprietor not only knows your name but has your shopping cart all but ready to go when you set foot inside the door. This kind of personal attention is what customers want and what keeps them coming back, right? Yes – to an extent. But it’s deceptively easy to cross the line.
You may recall this headline from 2012: Target figured out one of its customers, a teen girl, was pregnant before her father did. The retailer relied on patterns in her customer data to reach this conclusion and ‘congratulated’ the young woman with personalized coupons for maternity and baby gear. Her father intercepted the mail, leading to a very irate confrontation with an oblivious store manager who had nothing to do with the decision to target (no pun intended) this customer with maternity messaging.
As it turned out, the data didn’t lie, but the damage was done and not limited to just that household. The story spread rapidly across the Internet and became part of the growing narrative of distrustful consumers and intrusive, creepy companies who know just a little too much. Brands want relationships with customers, and customers do respond well to the personal touch, but they sure don’t want to be stalked.
That’s why, even as the technology continues to leap forward, there’s still no real substitute for the kind of expertise that comes from years of hands-on customer experience management. With great power comes great responsibility, and as a discipline, predictive analytics is still maturing. Leaving all decision-making to the algorithms may be accurate, but wisdom doesn’t translate well to automated code.
By integrating CSP’s Voice of the Customer research with actual sales results, our Predictive Sales Analysis (PSA) brings together the best of both worlds. We produce a unique set of key drivers determined by employee behaviors that have the greatest correlation to sales metrics, and coach clients to focus on the areas that are most important to their business strategy. Learn more about Predictive Analytics here.
