Customer service leaders know that a good customer experience has a quantifiable impact on revenue, as measured by increased rates of repurchase, increased recommendations and decreased willingness to defect from a brand. They also conceptually understand that clean data is important, but many can’t make the correlation of how master data management and data quality investments directly improve customer service metrics. This means that data projects are IT-initiated over two-thirds of the time, and data projects that directly impact customer service processes rarely get funded.
What needs to happen is that customer service leaders have to partner with data management pros – often working within IT – to reframe the conversation. Historically, IT organizations would attempt to drive technology investments with the ambiguous goal of “cleaning dirty customer data” within CRM, customer service, and other applications. Instead of this approach, this team must articulate the impact poor quality data on critical business and customer-facing processes.
To do this, start by taking an inventory of the quality of data that is currently available:
- Chart out the customer service processes that are followed by customer service agents. 80% of customer calls can be attributed to 20% of issues handled.
- Understand what customer, product, order and past customer interaction data are needed to support these processes.
- Inventory the customer service applications and processes, as well as any applications upstream of customer service (e.g., eCommerce, order management) that capture this data. Identify which applications and processes account for a majority of the data volumes, and prioritize those to evaluate first.
- Work with customer service managers and supervisors to perform an analysis of the quality levels of the data captured in customer service systems.
- Evaluate the impact of poor data quality on cost of operations, customer satisfaction scores and policy non-compliance.
Once you have this baseline data, craft your business case. Articulate the benefits of sound data for customer service organizations in the language of business users – such as cost savings via agent productivity gains, reduced penalties for non-compliance and increased customer satisfaction scores.
And, as always, dont forget the human factor. Good customer service is the result of good technology, good customer service processes, good data, and most importantly, a well managed organization which values its employees. Communicate your data initiatives to your agents, focus on why they are important, and what impact these projects will have on them. Involve them in discovering data issues to get widespread buy-in by evolving customer service incentive and compensation plans to prioritize data efforts.
Join me on April 19 at Forrester’s Customer Intelligence Forum , for my talk on Leveraging Data Alignment To Deliver Optimized Customer Engagement
