Database Reference
In-Depth Information
We're sure that you can relate to the following scenario: You call a service
provider's customer service department after getting disconnected twice.
You have to reauthenticate and then repeat your whole story to yet another
agent because when you got cut off, the original agent didn't finish making
notes. Now imagine calling back and hearing the agent say, “We're sorry,
we've been having some telephony issues and noticed that you've been dis-
connected twice. I apologize that I have to reauthenticate you, but I under-
stand why you are calling and here's what we can do to help resolve it…”
We're willing to bet that such a response would exceed your expectations.
Another example: How many times have you called your Internet service
provider to complain about your high-speed Internet service, only to get
the impression that the customer service representative's (CSR) job is to make
you feel unimportant and to get you off the phone as quickly as possible?
From a business perspective, you have to wonder if the service issues are really
being captured. Perhaps the agent handling a call fills out a form that outlines
the basic service complaint, but will it be correlated with point-in-time quanti-
tative reports that indicate how the systems were running? If you have a Big
Data platform, you can derive insight and predict issues before complaints
surface. In the case of an Internet service, quality of service (QoS) is simple to
measure—is the provider's technical support department analyzing Internet
protocol detail records (IPDRs) to understand the health of the network, and
then alerting customer service whenever there's a QoS issue in a certain part
of town?
Admittedly, the previous scenario is pretty advanced for call centers to-
day, but in the Big Data world, there's still room to grow, such as tonal change
or text analysis to identify exasperation (“This is the
third
time I've called!”),
or real-time correlation to identify trends in sentiment with a determination
of how trends in the call center are related to the rest of the business opera-
tions. If you need to explain this is the
third
time you've had to call on the
same issue, shouldn't all of the interactions with the provider reflect that,
even if you chose to use a different channel?
Understanding customer sentiment is a really interesting Big Data use
case, because it can be applied to what is possible today (using analytics on
data that's in motion or at rest) and to emerging capabilities. You can use one
of the IBM Big Data platform at-rest analytic engines, such as BigInsights,
