Poor Data Quality: An Often Overlooked Cause Of Poor Customer Satisfaction Scores
Customer service managers don’t often realize that data quality projects move the needle on customer satisfaction. In a recent Forrester survey of members of the Association of Business Process Management Professionals (ABPMP), of the 45% who reported that they are working on improving CRM processes, only 38% have evaluated the impact that poor-quality data has on the effectiveness of these processes. And of the 37% of respondents working on customer experience for external-facing processes, only 30% proactively monitor data quality impacts. That’s no good; lack of attention to data quality leads to a set of problems:
- Garbage in/garbage out erodes customer satisfaction. Agents need the right data about their customers, purchases, and prior service history at the right point in the service cycle to deliver the right answers. But when their tool sets pull data from low-quality data sources, agents don’t have the right information to answer their customers. An international bank, for example, could not meet its customer satisfaction goals because agents in its 23 contact centers all followed different operational processes, using up to 18 different apps — many of which contained duplicate data — to serve a single customer.
- Lack of trust in data negatively affects agent productivity. Agents start to question the validity of the underlying data when data inconsistencies are left unchecked. This means that agents often ask a customer to validate product, service, and customer data during an interaction — increasing handle times and eroding trust.
- Duplicate data increases operational costs. Agents often update customer, product, and service data in multiple disconnected applications, increasing the risk of errors. A major healthcare company calculates that agent process errors, like not updating member details like address or name changes when those details exist in multiple systems, cost the company $9 million per year.
- Data conflicts affect noncompliance rates. Consider a company that has duplicate customer records. If a customer opts out of a company newsletter, this change may not propagate to all instances of the customer record. The customer will still receive the newsletter — and the company may incur regulatory fines for this infraction.
What do you need to fix this problem? IT organizations can’t continue to drive technology investments with the ambiguous goal of “cleaning dirty customer data” within CRM. Customer service and IT have to work together to change the conversation so that it focuses on articulating the benefits of sound data in business terms like cost savings via agent productivity gains, reduced penalties for noncompliance, and increased customer satisfaction scores using language that resonates with your executive management. (You can find a sample ROI calculator here.) In addition, IT has to trust customer service to flag and fix data issues as they occur.