By David Loshin

We have looked at using enhancement for operational purposes, as well as analytical
purposes, but there are ways that we can merge the two into a hybrid: using
enhanced data for analytics, whose results are incorporated into operational
activities using the same types of enhancements.

Actually I was a bit sneaky in my last few blog entries, because I already
started to plant some ideas in advance of this post. I mentioned the use of
enhancement for person names, perhaps in a customer support capacity, in order
to verify identity.

At the same time, I referred to analytics that use customer data enhanced data
for customer profiling and geo/demo/psychographic segmentation. These two
processes can be combined to provide even more effective recommendations to
improve customer support or even drive additional sales.

Consider this scenario: call origination data (such as telephone number) is
provided when callers reach out to an inbound call center. Location data
associated with the originating telephone number is used as a key to look up
geographic/demographic data, which is used to enhance the inbound caller’s
record with segmentation data nominally associated with the individual.

As the call center representative walks through specific scripts provided to
help the caller, the enhanced profile information is used to adjust offers in
real time based on previously calculated statistics.

As a more direct example, complaints about dropped mobile calls might lead into
a script to recommend upgrading equipment. Based on the caller’s enhanced
location and geographic profile, historical measures of accepted offers coupled
with connectivity statistics in the given area can be mined.

The results can then be fed into the call center application, which provides
specific suggestions for the call center rep to offer a particular type of phone
that is best suited for maintaining connections in the customer’s location, at a
promotional price that the customer is likely to accept.

With a little bit of thought, we can come up with many types of these hybrid
scenarios – ones where data enhancement is used for both analytical and
operational purposes. In the upcoming months I anticipate looking at a number of
different aspects of data enhancement, data standards, and data cleansing, all
with a focus towards improved business functions.

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