Contact Data Quality and Retail Organizations: Current Perceptions, Cleansing Practices and Accuracy Levels

Contact data quality plays an important role in the retail market as it touches every part of the business, from package delivery to decisions around new store locations. But when contact data is managed incorrectly, costs are wasted and customer relationships are damaged.

To take a pulse check on the industry, Experian QAS conducted a survey in August 2010 to look at contact data perceptions, cleansing practices and accuracy levels. This report reviews current thoughts on contact data quality and includes advice on how to clean and maintain retail databases.

One hundred U.S.-based retailers took part in the survey, produced by pureprofile, an online marketing research firm, for Experian QAS. Company size varied from less than 50 employees to more than 1,000 employees, and a variety of departments were surveyed, including IT, marketing, operations, etc.

Data Quality is Top of Mind
Retailers are heavily focused on contact data management as a priority in 2010. This is reflected in the fact that 76 percent of retailers said that they plan to invest or should consider investing in data quality initiatives over the next 12 months. In addition, 58 percent of respondents have or are currently working on a contact data management strategy. Furthermore, 64 percent recognize and enforce data
accuracy as an essential issue.

This isn't surprising when reviewing the reasons that retailers maintain contact data quality. According to the survey respondents, the primary reasons for maintaining quality contact records are to save costs, enhance customer satisfaction and increase efficiency.

Contact data impacts a retailer’s ability to deliver product. Additionally, it greatly affects customer analytics, which are used by retailers to enhance marketing efforts and improve upsell and cross-sell opportunities.

Contact Data Inaccuracies Persist
Despite the strong investment in data quality mentioned previously, retailers still find their databases riddled with errors. Of those surveyed, 55 percent stated that at least 6 percent or more of their database contains inaccurate or missing contact data. The top data errors reported were incomplete or missing data, followed by outdated information and incorrect information.

Sales was cited as the main department that causes data errors, but many retail respondents also stated that multiple departments contribute to contact data errors. Budget, staff errors and internal resources were reported as the main barriers to maintaining accurate contact data.

Even with decreasing budgets, retailers still waste large amounts of budget on incorrect data. In fact, 54 percent of respondents say that 5 percent to 30 percent of their marketing budget is wasted as a result of bad data.

Data Quality Stewardship
According to those surveyed, the main department responsible for the cleansing of contact data is IT. Only 21 percent responded that multiple departments share this responsibility. This finding is surprising, since Experian QAS has found that more and more often, data quality is a cross-departmental initiative. Involving multiple stakeholders in selecting a data quality solution is typically the most effective way to impact long-term data quality results.

As for maintaining and improving data quality, respondents stated that they currently manage quality contact data through staff training, software tools and staff measurement. Retailers are also measuring the accuracy of their contact data: 62 percent are using email verification and 51 percent are using point-of-capture address verification.

Best Practices to Clean and Maintain a Database
With so much bad data contained in retail databases, implementing a cleansing strategy can be overwhelming. But with contact data playing such a key role in so many business processes, it's important to get started.

Begin with understanding your database to identify common errors and controls that are already in place to cleanse data. Next, clean existing data with third-party resources or manual processes, depending on the size of your database. It's important to clean data so that it doesn't continue to waste resources. As part of this cleaning process, duplicate records should be removed.

The next step is to verify data at all capture points so that inaccurate data doesn't continue to enter the database. And finally, enhance and update contact data. Fifty-five percent of those surveyed stated that outdated information was a common data error. By refreshing data, retailers can ensure that customers and prospects still live at their listed addresses. This means that retailers can continue to send relevant marketing messages to customers and draw prospects into brick-and-mortar locations or online to e-commerce sites.

Conclusion
Even though retailers have been scaling back, investments in data quality continue. This strong push toward data quality initiatives shows that retailers view their central database as a valuable asset, helping to drive their businesses forward.

While survey respondents revealed trends in common data errors — such as outdated information or missing and incomplete data — it's important for retail organizations to review their own data quality practices and implement strategies that will improve specific data quality problems within their organizations.