5 Principles to Managing CRM Data Quality

In response to increasing sales complexity, many organizations are “doubling down” on CRM to drive rep productivity, generate better insight, and improve customer experience. That said, your reps won’t use CRM if the data is not good, and it’s unwise for executives to make strategy decisions using data that is poor or incomplete. In fact, in our recent poll, 48% of respondents indicated that maintaining data quality was the most challenging aspect of CRM.

Hewlett Packard (HP), a global technology company, experienced a similar issue. A study of their CRM data revealed serious flaws in data quality such as duplicate and incomplete entries, and obsolete and inaccurate data—preventing HP from forecasting revenue accurately and supporting reps in sales activities. Consequently, HP experienced not only low levels of CRM adoption amongst reps but also a drop in customer confidence levels.

To overcome these challenges, HP created a CRM data philosophy that centered around five key principles:

 Define what “Good” CRM Data Looks Like: Establish data benchmarks that define “good” data, guiding reps’ data entry and helping achieve data consistency. Benchmarks include aspects that are critical to account management such as data category descriptions, contact field descriptions, and taxonomy norms.
Appoint Data Custodians: Identify frontline CRM data solution providers to examine CRM data records, identify deficient or flawed data records, and take corrective action. HP appointed “data custodians” to examine all CRM data records and process those records that are either incomplete or flawed in other ways. Data custodians also ensured that the CRM data for lead generation and sales activities was complete and fully updated as per established data quality standards.
Constitute Data Audit Teams: Use data audit teams to review and certify data quality. To validate quality of CRM data entered by reps, HP performed regular data quality certification using established CRM benchmarks and measures as reference.
Deploy Dashboards: Deploy internal dashboards to assess data compliance and performance. HP created dynamic dashboards for data custodians to highlight blank fields, overdue tasks, and track updates at country, region, and worldwide levels.
Create a Monitoring Protocol: To drive data quality improvement throughout the organization, HP established an explicit plan to measure data quality. This included a methodology for frequent data quality monitoring.

CEB Sales members, learn more on how HP tackled its data quality challenges to provide reliable CRM data to its sales force. Also, visit our CRM topic center for more resources on managing CRM data quality, driving CRM adoption, and selecting CRM vendors.

Related Resources:

CRM Data Quality
Getting Reps to Use CRM
Extracting More Value from CRM

Related Blogs:

How to Not Waste a $20 Million CRM Investment
What You’re Overlooking with CRM Adoption
12 Principles of World-Class CRM