Introduction
A CRM can have thousands of customers, leads, conversations, transactions, and follow-up activities, but it can fail to deliver information. It is not necessarily the software. However, in many cases the truth is that it all starts much earlier: the way that data is entered, updated, and managed.
As staff fill in incomplete data, format it differently, add duplicate records, or leave out critical data points, it becomes increasingly hard to believe in the CRM. It may appear to be a technology issue,e but this can actually be a data quality issue.
But if it’s bad data for reporting, data automation, and decision-making, it can impact the entire customer journey.
The costs and consequences of poor CRM data.
Poor CRM data doesn’t normally lead to an immediate disaster. Rather, little errors add up.
A salesperson might phone a lead with an old phone number. An existing customer can receive a message from a marketing team that is incorrect. A support representative may not be able to view previous interactions. A report that has duplicate or incomplete records can be reviewed by management.
These issues individually can be quite trivial. When combined, they can lead to confusion, lost time, missed opportunities, and confusing customer experiences.
Employees enter data incorrectly because:
One of the largest issues is that CRM data requires a lot of human effort.
Staff could feel pressured to finish the job quickly, not be aware of the significance of specific fields, or the CRM procedure might be too cumbersome. When inconsistencies exist between different departments, they are more likely to become worse if each department conducts its data entry in a different manner.
For instance, a customer’s name could be one employee’s full name while another uses an abbreviation. One person might call a lead “New” and another person “New Lead. These small differences can impact searches, reports, segmentation, and automation over thousands of records.
The more data, the worse the data,
It’s a common belief in businesses that gathering more customer data yields more insights. But it’s not quantity that will make up for low quality.
The 100,000 bad records in a CRM could be of less value than the 30,000 reliable, well-maintained records.
Businesses should thus consider gathering information with a meaningful purpose. All fields should contribute to employees’ understanding of customers, completion of a process, decision-making, or the quality of the service.
The problem of automation can be made bigger.
With CRM automation, employees can save a lot of time. When set up properly, automated reminders, lead assignments, emails, notifications, and customer workflows can help streamline workflows.
Automation relies on the information it is fed.
An automated workflow could send the wrong message if there is an incorrect status in the customer record. Follow-up actions might not be sent if a contact’s details are incorrect. When there are multiple records, the same person may be contacted multiple times.
Bad data will not be corrected by automation. It can just “drive” bad data faster.
Establishing a data accuracy culture in your organization.
Cleaning up old data is not sufficient to improve the quality of the CRM data. There’s a need for a consistent procedure to keep information maintained in businesses.
The teams should have well-defined data entry guidelines, make it clear what fields are important, eliminate unnecessary duplication, and periodically audit outdated data. Staff should also be aware of the importance of accurate data to them and how this contributes to their customers’ overall experience.
Training is a very crucial aspect. Staff will adhere more easily to data standards if they know why the standard has been created.
Minimize manual errors with the help of technology.
CRM systems can also be set to cut down on unnecessary human error. Consistency can be maintained with required fields, dropdowns, validation rules, duplicate detection, automated updates, and integrations.
Integrating the CRM with other business programs can help minimize duplication of effort. For instance, data can be synced, as appropriate, from sales, customer service, billing, or marketing systems.
The goal is not to do away with human intervention. The aim is to make accurate data more easily produced and maintained.
Conclusion
A CRM may become untrustworthy, and replacing the software may not fix the issue. The actual problem may lie in the way that customer data is getting into and handled.
Good data is the basis for valuable CRM reporting, automation, personalization, and customer service. Companies that define the criteria and practices of data and embed accuracy into their daily business processes can transform their CRM from a data morass into a reliable business asset.
The next time a CRM report goes awry, it may not be because it’s another new feature of the software. It could be in the data itself.
FAQs
1. What is CRM data quality?
CRM data quality is the consistency, completeness, reliability, accuracy, and relevance of the information held within a CRM system.
2. Why bad CRM data is a problem?
Bad data can lead to missed follow-ups, faulty reporting, duplicate communications, poor customer experiences, and questionable business decisions.
3. Is CRM automation the solution to incorrect data?
No. Automation relies on the information in the system. Automation can propagate or react to the wrong information if the base data is wrong.
4. What are businesses doing to ensure the accuracy of CRM data?
Businesses can set up data-entry standards, train employees, employ validation rules, find duplicate records, flag out-of-date ones, and automatically update data where applicable.
5. Does a company need to change its CRM due to bad data?
Not necessarily. Businesses should first find out if the issue is related to the software itself, if it is related to the configuration, if it is related to weak processes, if it is not related to training, or if it is related to low-quality data.