Database Reference
In-Depth Information
Understanding Customer Sentiment
Some industries or products have disloyal customers with very high churn
rates. We worked with one telecommunications company that experiences
more than 50 percent annual customer churn. They wanted to enhance reten-
tion by identifying which types of customers are most vulnerable earlier in
the billing cycle. For this client, even small improvements in the conversion
rate to more stable plans would double one of their offerings' revenue stream.
Quite simply, they didn't have to hit a home run to be more successful; they
just needed to get a base hit. Of course, that's easier said than done, however,
because of how transient the opportunity is with the customer. When you're
dealing with those levels of churn and massive volumes, being able to find,
trap, respond, and interact is not a small challenge.
The key to making this happen is being able to detect loyalty decay and
work that into your client engagements and next-best-action modeling before
the next contact with the customer. It isn't quite real time, but what we call
customer time
. Customer time is simply the notion of being able to process
everything needed before the next customer interaction so it looks seamless
in the eyes of your client. However with Smartphones, customer time is getting
close to real time because there are always opportunities such as sending an
e-mail, text message, or offer.
In addition to operating in customer time, this use case provides another
example of the value of capturing all of the available information to look at
events that help to build context. After you've captured one aspect of the
communication spectrum, you can move on to the next and start correlating
that with everything from emails to social media and other things that we've
talked about in this chapter; you can even correlate it with back-office service
quality reports to see whether people are calling and expressing dissatisfac-
tion with you based on your back-end systems. If you're able to identify a
pattern that shows where your systems have been slow or haven't behaved
properly, and that just happens to be the reason why a particular individual
is calling to cancel services without explicitly mentioning it, you can estab-
lish a correlation with what the customer is saying. In fact, a goal of one of
our FSS clients is to have a good idea why you're calling before they even talk
to you so they can “pre-resolve” your issue and inform you of the fix rather
than having to even talk to you about your problem!
