Put Your Customer Interactions Under a Microscope
| Published: October 28, 2015 | Comments
In 1953, scientists James Watson and Francis Crick published the first accurate double helix model of DNA. Since then, the scientific community has used this incredibly detailed genetic coding to make breakthroughs in everything from health care to forensics. Great, but why is this topic being discussed in a customer analytics-related context?? Simple - to make a point that the customer experience space cannot make a significant breakthrough with data analytics because it has the wrong data.
OK, maybe wrong is too strong but when I was call center neophyte in the late 90s, we were looking at reports around things like talk time, hold time, wrap time, disposition, customer sentiment and call resolution. Fast-forward to 2015 and guess what, we’re dealing with the same data! Oh sure, the industry has added text analytics, 3rd party surveys, tool utilization reports and a few other good data points but I’m not sure any of us would call that “breakthrough” material. Summary-level interaction data is not enough.
Look closer at a customer interaction. The dialogue that you hear is based on a sequence of thoughts, emotions, questions, statements, mouse clicks, key strokes, and more. Summary reporting may answer broad questions about WHAT happened but fails to capture the time-sequenced detail of HOW it happened. Think of the HOW as the “DNA” of a customer interaction. These unique characteristics in each interaction never make it into a database to be analyzed. That’s because, until now, there has been no “microscope” capable of seeing the DNA level detail.
The real science is in the sequenced events. For example, it’s not enough to know that a customer was agitated on a call to the call center. At what point in the call did the customer’s sentiment begin to change? How many times did it change? What took place immediately before the sentiment change that prompted a change? The sequenced data unlocks those mysteries allowing much deeper insights into customer interactions.
With this “DNA sequencing” methodology applied to customer interactions, the amount of actionable data increases exponentially. Consider the effect of knowing what took place prior to a customer threatening to take their business elsewhere (churn triggers), or understanding the ebb and flow of a sales conversation that includes positioning, objections, rebuttals and acceptance. Marketing teams have been starved for years with anecdotal information from the call centers that can be neither validated nor quantified for decision making. Customer Interaction “DNA” is a game changer for such organizations. With the single interaction data aggregated with common taxonomy, analytics can be easily performed to look for causation and correlation to improve sales, net promoter scores, first call resolution and more.
The benefits of time sequenced interaction data go beyond the customer experience. Call center operations can use the same data to drive efficiencies that lead to a lower cost structure. For example, call handle time can broken into segments to understand where opportunities exist to decrease the time it takes to complete a call into customer care or tech support. Stratifying call time into segments such “reason for call” to “resolution of problem” can help pin point training needs, insufficient call center tools and knowledge or outlier agents who may not be best suited to handle those calls.
Here’s the rub. Like everything else in data analytics, nothing comes easily. This data simply cannot be captured with speech analytics or text mining. It requires the knowledge and expertise of subject matter experts to collect the data in it native state. Context and inferences are critical. Fortunately, most call centers already have this expertise on staff. Even better, these people are usually underutilized and have bandwidth to add so much more value. Any guesses who that may be? It’s the Quality Assurance team! Take a look at what your QA team is doing today and you’ll likely agree that they can do much more.
Call centers are the front line of most companies. Problems tend to surface there first in the form of symptoms. Those symptoms have root causes that are typically well upstream of the call center. Today’s analytic tools lack the data needed to identify root cause. Get a microscope and see the DNA of your customer interactions. The root cause breakthroughs will follow!
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