A growing air of excitement surrounds the use of advanced business analytics, with many pundits claiming that organizations must be data-driven in order to succeed. Of course, being data-driven is not a new thing. It's just that companies now have the ability to collect, store, and analyze much greater amounts and more varied types of data then ever before possible. So what folks really mean when they say "data-driven" is that companies are beginning to use data in smarter ways.
Historically, for example, an organization may have used descriptive analytics to comb through transactional data from customers. This told them what happened, and why. Now, with today's advanced analytics techniques, they can make predictions about customer behavior and find new ways of creating business value. They can do so by supplementing traditional customer data stored in structured databases with unstructured data contained in contact center records, in Twitter comments or Facebook posts, and in email and voice mail, for example.
The trick is integrating the insight into the contact center or communications platform, and doing so in a way that doesn't jeopardize privacy or result in customer or employee outrage over data use.
Analytics and the privacy tipping point
Customers likely understand on a general level that their communications with businesses aren't entirely private. Many may even recognize the value of letting companies crunch their data. However, there is usually some tipping point where the people being analyzed get that eerie feeling of Big Brother watching them, and that tipping point is usually reached when an organization hits a little too close to home with how it uses its data. Perhaps the most iconic example of this comes from the consumer arena, when Target identified that a teenage girl was pregnant and began marketing maternity products to her all before she had even told her family. The convergence of powerful analytics tools and more robust data sets are making it possible to reveal behavior before customers have made conscious decisions.
In the contact center realm, we're beginning to see how the use of analytics can come into play. A company called SATMAP, for example, analyzes data that's connected to contact center callers' phone numbers to identify demographic and behavioral characteristics. It then matches callers with compatible agents based on their personalities. While that can be a plus, some customers might feel manipulated by use of their data in this manner.
Companies like Google and Facebook that deal with enormous amounts of data have already had to actively fight legislative battles over how to use that data appropriately and how much transparency will be required. It is not a great leap of faith that as more organizations begin diving into customer data, they'll face similar challenges. As businesses do explore analytics more tightly focused on their customers, they should establish frameworks for transparency and dealing with privacy conflicts.
Analyzing the human factor
One of the other issues likely to emerge will come when technology like personality-matching call center analytics tools are widely implemented. The idea of employees who always interact with customers they're most compatible with is reminiscent of the concept author Eli Pariser refers to as filter bubbles. This concept describes the way individuals' Web browsing experience is highly tailored to their personal tastes, such that two people performing Google searches using the same terms can see vastly different results. Many areas of the Web now filter what people will like most based on previous browsing history and habits. Pariser argues that this creates the risk that little of the information online users run into will challenge or broaden their views of the world.
A similar problem may emerge if businesses increasingly tailor the work environments only to their employees' strengths. The short-term benefits of call center representatives never having to interact with a customer whose personality is incompatible with their own are clear: the potential for reduced call time and increased completion rates. Although the long-term effects of this reality remain to be seen, it's feasible that key skills such as problem solving and conflict resolution would decline because these employees wouldn't be using them nearly as much in their daily work.
While analytics technology is powerful and has the potential to aid in decision making, it doesn't reduce the need for critical thinking. In a September 2014 TED Talk, data scientist Susan Ettinger perfectly explained why critical thinking is even more important in the data-driven world:
"As business people, as consumers, as citizens, we have a responsibility, I think, to spend more time focusing on our critical thinking skills. Why? Because at this point in our history, as we've heard many times over, we can process exabytes of data at lightning speeds, and we have the potential to make bad decisions far more quickly, efficiently and with far greater impact than we did in the past," she said.
In other words, advancements in technology have increased the speed at which businesses and people can consume data. However, the end result of those endeavors will greatly depend on our ability to think critically about it. This applies as much to top-level executives making strategic corporate decisions as it does to the call-center agent helping solve a customer problem.
