Database Reference
In-Depth Information
interest that it was told through the best-selling topic and blockbuster movie
hit
Moneyball
, starring Brad Pitt. Eventually, other teams caught up. Today,
there isn't a single team in MLB that doesn't use sophisticated analytics.
Widespread adoption of analytics within MLB has neutralized any advan-
tage that the Oakland A's once had in this domain.
It's fair to say that we're now entering an era in which analytics will be
considered to be a “table stakes” capability for most organizations. Let's look
at a specific example of how an organization is taking the use of analytics to a
whole new level. Using analytics to measure customer support effectiveness is
a common practice among many customer-centric organizations. It enables
them to monitor customer satisfaction, drive retention, and manage the cost of
support. The traditional approach involves analyzing all of the data associated
with a support incident, such as call duration and speed of resolution, and
then identifying opportunities for improvement. It also involves conducting
surveys and collecting satisfaction metrics. One such metric, the
net promoter
score
, can be a very effective tool in gauging a customer's perception of the
company or product based on their interactions with support personnel.
Although this traditional approach can be effective in improving customer
satisfaction and reducing churn, the
analytical cycle time
(the time interval
between a support call and the actual process improvements that get pushed
to the front line) can be quite long. During that time, other customers might
have similarly poor support experiences that could cause them to churn. The
opportunity for organizations to differentiate and compete revolves around
not only the use of deep analytics at the core of their business, but also the
analytical cycle time. Like a giant snowball rolling down a hill, the impact of
analytics on your business is slow at first, but with every rotation, the poten-
tial impact becomes greater and greater.
With this in mind, the question really becomes, “Is it possible to take the
analytical models and processes that have been built on historical data sets
and apply them in real time to streaming data?”
One of our clients is currently in the process of doing exactly that. They have
an intelligent intercept agent that monitors all telephone conversations between
customers and customer support representatives (CSRs). This agent monitors
the conversation, applies sentiment analysis to that conversation, and provides
recommendations to the CSR in real time. For example, if a customer uses tonal
inflection to ask a question, or uses sarcasm to express displeasure, the auto-
mated agent is able to detect that immediately and provide specific guidance to
