Data quality is essential to businesses in prospecting and retention efforts. Without accurate data, companies struggle to execute successful marketing campaigns or fulfill customer expectations. Despite the importance of accurate data, a recent Experian QAS study revealed that 45 percent of businesses do not have a data quality strategy.
Organizations without a clear data strategy argue that implementing a master data management strategy can be a daunting undertaking. With so many areas for improvement - contact data, inventory management, constituent records and more - businesses are left tangled in a web of confusion, costs and committee meetings.
This does not mean that an MDM strategy is bad. MDM strategies answer a number of data quality questions. However, because those answers come at the expense of large budgetary allocations and IT resources, it’s no surprise that almost half of businesses lack a clear data quality strategy.
Taking time to unravel the web of MDM confusion is fair. However, this drawn-out process is leaving businesses vulnerable to data inaccuracies. Businesses, however, can finalize larger-scale data quality strategies while quickly and cost-effectively implementing manageable MDM.
Five tips can improve contact data quality along with suggestions for measuring and tracking ROI.
Tip 1: Track mail deliverability.
Personalized communications, through email or direct mail, drive a significant percentage of marketing efforts. These channels have evolved to facilitate truly relevant and customized messaging.
In addition to the strategic marketing advantages, email and direct mail also act as high-level barometers for data quality. By creating a process to track returned mail and email bounces, organizations can monitor data accuracy and update where necessary.
Despite the short-term increases to administrative work and program costs, a proactive database audit will drastically improve communication effectiveness and overall planning in the long term.
Tracking may be as simple as flagging contacts within campaign spreadsheets or databases. The objective is to systematically force updates that ensure future mail accuracy and provide reporting information on deliverability over time. Once a process is in place, set benchmarks and address accuracy goals for the future.
Tip 2: Verify information before database entry.
Correcting contact information is always easier when the client is engaged. This engagement can occur through a Web form, a telephone call or even a live online chat. Ideally, data verification software tools will prompt either the staff representative or the audience member to complete missing contact details and will then format the address to comply with USPS or email standards.
When the contact is not engaged, for example when sales and marketing professionals enter new prospect information, verification will enable faster and more accurate data capture. As staff keys in contact information, the software highlights incomplete elements to improve the flow of good information into a business database.
After this process has been implemented, check the business database to see if information is standardized and complete. This will allow database administrators to distinguish if user adoption has taken place and measure the effectiveness of the new software tool.
Tip 3: Understand organizational data and how it got there.
It is important for businesses to fully understand the information contained in their database in order to improve operations and communications. This will provide insight into data quality challenges and allow managers to better select solutions that get to the root of their troubles.
To fully understand a business database, first identify potential data entry trends, specifically areas where bad data accumulates. Next, determine possible bottlenecks that may hinder data collection processes.
Administrators should ask themselves:
- Can bouncebacks be traced to a particular operation or target audience group?
- What are the potential sources of bad data?
- What tools will enhance data collection within high error rate areas?
Organizations must consistently analyze their data capture procedures, which relates back to tip 1, tracking deliverability. It’s important to understand how deliverability changes over time and what factors contributed to those changes.
Tip 4: Appoint multiple data quality managers.
Data management is a multifaceted strategy that should be shared by all employees who capture, access, manipulate and update records. Whether better data translates into more effective prospect on-site meetings, lead nurturing campaigns, customer implementations, product deliveries or other mail communications, the business benefits of promoting data quality are shared throughout all departments.
First, determine which employees have the ability to create or update data. Next, find out who accesses data and for what purpose. Once all data users are identified, look for tools that empower staff and that work across departments or facilities.
Solutions should easily integrate into an organization’s main database. It may not be necessary to enable data capture functionality for all users. By identifying which employees require technology enhancements, the organization can quickly and cost-effectively update those machines with technology that meets the accuracy and formatting standards for other departments.
Tip 5: Schedule regular database checkups.
Individuals and businesses have become mobile in today’s economic and social environment. Consider how quickly data expires within a given audience. A complete data quality strategy calls for regular database checkups on top of data capture best practices.
To ensure contact data integrity, supplement real-time verification efforts with regular bulk processing. These updates allow businesses to better measure the quality of data after each strategy is implemented. More importantly, they ensure that old data is refreshed and continues to perform for years to come.
The ROI of Data Management
The five tips presented will ultimately ensure better communication with customers and prospects. By managing the capture and update of contact data, organizations will improve interdepartmental activities, data collection and data tracking. When combined, these improvements significantly enhance communication accuracy and streamline operational activities. The result is increased revenue from customers and prospects and fewer costs from unnecessary data rework.
While administrators may eventually want to labor through a lengthy technology implementation, data users will benefit from a small-scale contact data quality improvements in the short term.
Managing data quality does not have to be challenging; it just needs proper planning, measurement and a clear understanding of its business value. By starting a data quality strategy now, businesses will increase efficiencies and improve prospect and customer performance.
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