Demystifying data quality: the top five questions for marketers
"Know thy customer” is the marketing wisdom that underscores every effective
marketing campaign. Relevant insights and accurate information help marketers up-sell
and cross-sell as well as ensure that their customized offers reach the right household
or individual via mail, email or phone. Savvy marketers who procure third-party data
to enrich their customer databases and expand prospecting opportunities want to be
confident in the coverage, accuracy and quality of their data investment. However,
many marketers are perplexed by the contradictory findings published by data providers
and unable to effectively discern data quality and coverage. We asked Andy Johnson,
Experian Marketing Services’ Vice President of MIS Data Sourcing, to identify
some basic questions marketers can ask to demystify data quality.
1. Who conducted your provider’s data quality audit?
Johnson: Objective auditing is a critical component of evaluating
data quality and coverage across a crowded competitive landscape. An independent third-party
evaluation versus an in-house analysis creates an additional level of assurance that
the results are credible. As a consumer, what is more compelling to you: the manufacturer
telling you the product is the best or an independent consumer report rating the product
as the best? The more objective and transparent the study methodology, the more confident
you can be in the credibility of the results.
2. What are the data application and data attributes for the test specified in
the research methodology?
Johnson: The data application and data attributes identified in
the methodology discussion will set the stage for how the data will be matched and
validated for quality and coverage across all providers. Typically, there are two
types of data applications that are audited: enrichment or prospecting data. Enrichment
data is most commonly used to append individual or household profiles so the depth
and the breadth of attributes available (e.g., lifestyle, demographic, etc.) are benchmarked
and compared. Mailing tests validate prospect contact information (e.g., name and
home address) and the deliverability of those records. In order to ensure audit fairness,
the competitor sample request should specify the data application and data attributes
for the sample geography to ensure a fair comparison across competitors included in
3. How was the data pulled?
Johnson: The source of the data also should be identified in the
methodology. So fairness is maintained, each competitor should be blindly solicited,
receive the same sample request documentation and be responsible for producing the
sample. If the competitive sample is pulled from a reseller or another party, the
sample is potentially compromised. The age, accuracy, comprehensiveness and mix of
data sources (e.g., known, modeled, inferred, etc.) will vary depending on the provider.
A sample procured directly from the data compiler or aggregator lowers the risk that
results will be skewed and unreliable.
4. What was the geographic sample for the test?
Johnson: The geographic sample for the test should be random and
large enough so that it is statistically representative of the United States. As a
potential data purchaser, you want to know the quality and reliability across the
entire national database, not for a handful of ZIPTM codes. A statistically significant
sample size is projectable and a reliable indicator of the data performance across
5. How was data matched, and how was the accuracy validated?
Johnson: Ultimately, the audit will provide a scorecard that helps
the discriminating marketer assess coverage and quality. As a marketer, you want to
know what percentage of records have valid, nonmissing values by provider. The overall
match rate should be explained and put into context in order to understand the breadth,
depth and accuracy under review. Census or syndicated research data at the geographic
level is a reliable benchmark for validating the data’s accuracy. Higher counts
or greater attribute coverage is of little value to the marketer if the basic IDENT
information is wrong.
Unfortunately, there is no perfect data source, and there will be imperfection
in any data file. The key is to understand what factors affect that variance, such
as the ratio of known versus inferred data, how households are defined by provider,
and how each provider sources and compiles data. Often, these factors are obscured
in data quality audits, but in the real world they can have an enormous impact on
marketing intelligence and campaign effectiveness.
To learn more about Experian Marketing Services’ marketing data quality,
we invite you to read the 2011 ConsumerView℠ marketing data quality report.