Companies everywhere recognize that 'knowing your customer'
is critical to today's business practices. An ideal single view of the customer
will help improve customer service and marketing, enhance business
intelligence, and meet regulatory compliance. Knowing who your customers are,
which products they own, what their history is, and how profitable they are can
provide a truly competitive edge.
While most companies already have the data necessary to gain
these insights, it is often fragmented or scattered, held in incompatible
formats, in legacy systems, generally not well integrated, and always changing
- making data hygiene a difficult, albeit essential, element to the accuracy of
the single view. Data quality tools and techniques are vital to the process.
What is full contact data quality?
Determining whether two or more records refer to the same
customer can be extremely difficult. For example, in one database a customer's
full first name might be referenced as a nickname in another (i.e., Robert
versus Bob). This particular customer may also have a valid email address in
one data set, but a different email address in another. Customer information
fields may also be missing or inconsistently completed. A single view can only
be achieved with consistent, current and accurate data.
It is difficult to consolidate duplicate records and match
accurate data to the actual customer without a method to determine the data
interconnectivity between similar records. Fortunately, "full contact data
quality" can help businesses make clear connections throughout and across
data sets. The full contact data quality process links historical snapshots of
each contact data component to identify the most up-to-date records (i.e., the
most current street address on file). The process brings the entire customer record
to its most current form, enhancing the single view of the customer.
Beyond validation
Companies often rely on the validation of their contact data
and call their DQ efforts complete. As a "rules-based" approach,
validation is used to determine whether data fits the required format. If the
data is reasonable and possible. For instance, validating a phone number
requires that there are 10 digits and no letters in the string. Or, when
entering a date, the validation for month is 1-12; entering a '13' is not
allowable as it is out of range. Unfortunately, this traditional process falls
short of true data quality.
Full contact data quality goes further to verify data by
comparing that data to a set of reference data for accuracy. By implementing
this process, an organization can match its data to USPS data to identify which
customer addresses are deliverable, which can be corrected, and which are
completely undeliverable. Verified and corrected addresses can then be recorded
in a standardized form. The same process of matching customer data to reference
datasets can be used to verify and correct full names, telephone numbers, email
addresses, and other contact data.
The benefits of verified contact data are significant and
include postage savings, reduced return mail, better response and conversions
for marketing campaigns, and enhanced customer satisfaction.
Connecting with customers is crucial
Possessing verified contact data is only part of the puzzle.
Knowing that contacts are reachable is essential. For example, customer John
Smith is associated with the following address: 100 Main Street, Anytown,
California 92688. We can certainly verify that the address exists and is
deliverable, but how do we know Mr. Smith still lives there? Or that he ever
lived there, for that matter? The same goes for John Smith's telephone numbers,
and even his email address.
Full contact data quality compares data across multiple
sources, including USPS records, telco data, title information, and other
public and proprietary information -current and historical. This process first
associates and then connects verified contact data to the specific customer.
With full contact data quality, an organization has the assurance that a direct
marketing or telemarketing campaign, for example, will reach the targeted
customers and prospects because they are reachable.
Quality comes from completeness
Just about every CRM system is missing one or more piece of
data on any given customer. Fortunately, a skilled data quality vendor can fill
these holes with verified street addresses, email addresses, phone numbers, and
even relevant demographics. With a collection of accurate, reachable, and
complete contact data, businesses may now perform more holistic, meaningful
analysis of their customers to generate further growth opportunities.
Data quality has evolved significantly over the past 20
years. Just as decision support systems, data warehousing, and business
intelligence have prompted more in-depth analysis of the data used to measure
and monitor corporate performance, a changing attitude has steadily altered our
perception of what is meant by "quality information." Today, the
concept goes well beyond data cleansing, standardization, and enhancement, to
include full contact data quality. With its unique ability to resolve disparate
representations of a record and then link together all touch points of customer
data, full contact data quality offers the means to attain and enhance a true
single view of the customer.
