By David Loshin

At the end of our last entry, we had come to the conclusion that standardization of potentially variant data values was a key activator for evaluating record similarity when looking to group customer records together based on any set of characteristic attributes. From an operational standpoint, this activity is supported using data quality tools that can parse and standardize data.

But the process must go beyond the purchase and use of the tools. For any
customer centricity program in which connectivity is relevant, there are going
to be multiple dimensions of connectivity employed in business decisions. We can
immediately fall back on my original example of the “household” grouping, and
depending on the objectives for customer outreach and experience, other groups
will be overlaid with each other.

Here is a clear example that builds on my post from a few weeks back. We
originally suggested that the household was relevant for mobile telephone
companies looking to expand residential customer commitment though increased
product sales and service contracts within the household, since one
decision-maker might be responsible for adding new lines and services.

That same mobile telephone company might also look at their business-to-business
relationships and look to expand their footprint among business customers,
suggesting a new grouping of customers based on their employer.

Overlaying the households and the corporate customers would provide a picture of
companies existing brand predispositions among the employees. Identifying the
key corporate decision makers and offering combined business and residential
account discounts might be a good way to exploit knowledge of overlapping
connected groups.

The result is that the analysis not only depends on good quality data, it
assumes that good processes are in place for managing the hierarchy data that
maps individuals into groups – an example of what could be called metadata
quality. Keeping hierarchies of concepts, data attributes, and mappings among
individuals based on those hierarchical attributes (and of course, similarity
scoring for linkage!) is a valuable skill, one that we will revisit in upcoming
series of posts…


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