Imagine a company that purchased a shiny new CRM system. Expectations are high, but when the entire team is up and running, the sales force spends hours calling unreachable phone numbers. Marketing sends out a mailer on which 10 percent of the addresses were undeliverable. Why? Because the data that serves as the foundation of the CRM system contains inconsistent, inaccurate, or outdated information.
A successful CRM program requires high-quality data. By following a five-step process methodology, companies can eliminate bad data from CRM systems and build better information to support the entire enterprise.
Step 1: Data profiling
Data profiling helps discover what's wrong with data and what needs to be addressed to improve the quality of corporate information. With profiling, users can analyze data before adding it to a CRM system to uncover problems with the true content and structure of information.
Through data profiling routines, companies understand the strengths and weaknesses of data, and can pinpoint areas that need improvement. This feedback about the data can help plan the next step: developing a data quality road map.
Step 2: Data quality
At the data quality phase, users can correct errors, standardize data, and validate information that is inconsistent and inaccurate. This step is about correcting existing data and making it more useful for sales and marketing purposes.
Data quality functionality also can verify that addresses or other contact information is accurate. Then, companies can use matching technology to identify logical "households" to refine marketing programs.
Step 3: Data integration
A common problem in many modern organizations is the spread of data. Over time, companies add new systems or databases to their operations. And each data source has its own unique values, nomenclatures, and protocols.
Proper data integration techniques can help companies avoid costly mistakes and embarrassment by reducing or eliminating duplicate messages to customers or prospects. By linking and joining information from a variety of sources, organizations can create a true 360-degree view of the customer to support sales and marketing efforts.
Step 4: Data augmentation
Data augmentation takes what data is available and enriches it with additional information. Here companies can find missing phone numbers through third-party databases using a customer's name and address.
Similarly, organizations can add behavioral data to customer records to help understand the customer's previous buying patterns and give a glimpse into potential purchases. With augmentation, companies add value to their existing data to strengthen customer records to gain a better understanding of their customer base.
Step 5: Data monitoring
Put simply, high-quality data takes constant vigilance. Data monitoring builds on previous data management initiatives by providing the technology needed to examine data over time and alert users when good data goes bad.
Continuous monitoring of data provides the insight to recognize immediately when quality falls below acceptable limits. Data monitoring can alert the appropriate data owner when information does not meet business requirements.
With these five steps organizations can implement a comprehensive structure for managing customer and prospect data. And remove the problem of bad data from a CRM implementation.
About the Author
Tony Fisher is the president and general manager of DataFlux Corp., the leading provider of end-to-end data management solutions that help companies analyze, improve, and control business-critical information. For more information on DataFlux, visit www.dataflux.com