CRM
Databases
One of the
hottest areas of growth, noticed lately is in the area of customer
relationship management (CRM) applications and ultimately the need
for CRM Databases to handle the large amount of data. Increasingly,
organizations both large and small are building CRM applications. In
some cases, these CRM Databases are being built using sales force
automation (SFA) packages, while in others they are based on data
warehouse technology.
Sophisticated
organizations are blending both approaches: using SFA packages to
manage the sales cycle, and then moving the data into
decision-support CRM Databases for pre- and post-sales marketing and
long-term customer management. They are also consolidating this
sales-related data with other data, such as customer service,
billing and receivables, and external demographic or market research
data, to build a richer customer profile using CRM Databases.
If you build
CRM Databases, it can yield major benefits for your company. There
are a number of important issues that you will need to address when
building CRM Databases.
The first is
data quality in CRM Databases. Validation and verification is
essential for CRM Databases; otherwise, you take the risk of
creating multiple records for the same individual or identifying two
people as the same individual. In either case, you can lose money
and alienate your customer by, for example, sending multiple copies
of a mailing to a single individual. This why you need to pay close
attention to the problems associated with customer identifiers that
are created by uncoordinated applications. For example, a
health-care institution has to be sure that each patient is
identified by a unique medical record number that is used by all
applications throughout the institution, such as admitting, billing,
laboratory, radiology, pharmacy, and so on. So the requirement of
fool-proof CRM Databases with advanced
capabilities.
Names and
addresses require particular attention. Names, which are unique to
an individual, should be parsed and normalized before they are
entered into CRM Databases. Parsing is the process of decomposing
the name into its elements, such as first name, last name, surname,
prefix (Dr., Rev., Mr.), suffix (M.D., Ph.D., Jr., III, Esq.) and
relationships (Trustee for …, and so on). Normalizing refers to
reassembling the name into a consistent format in CRM Databases.
Addresses are somewhat easier to normalize and validate than names,
since they have a more consistent structure and can be verified
using specialized CRM Databases. It's relatively easy, for example,
to validate a ZIP+4 field against a table, available from the postal
service, of all ZIP codes in the
U.S.
Another issue
to consider in CRM Databases is the rich, relatively unstructured
set of data you'll want to collect on customers. Items such as
support calls, demographic data and customer inquiries are all grist
for the CRM Databases, but these tend to be unstructured, text-based
data elements that can be difficult to query and analyze in a
decision-support environment. You'll need to carefully consider the
kind of data your users will require, and how to structure the data
in a way that will be meaningful and useful to
them.
Once you've got
the data in CRM Databases, the next thing you need to consider is
how to maximize its value. First, and foremost, is to put the data
into the hands of the people who can best use it. Every customer
touch point, such as customer service or telemarketing reps, should
have easy, quick access to this data.
This kind of
data is essential for determining customer lifetime value in every
CRM Databases. This process helps to identify the good customers who
should be wooed and cultivated from those who consume products and
services but are unprofitable and should be gently steered in the
direction of your competition. Profitable customers can be provided
with additional benefits, services and special premiums or offers
that will entice them to remain good customers. Non-profitable
customers can be managed differently, through additional fees or
reduced services. These techniques can have the effect of either
making these customers profitable or encouraging them to take their
business elsewhere.
