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 connection between how master data management and data quality investments directly improve customer service metrics. This means that IT initiates data projects more than two-thirds of the time, while data projects that directly affect 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 that poor-quality data has 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 the customer service processes that are followed by customer service agents. 80% of customer calls can be attributed to 20% of the 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 and order management) that capture this data. Identify which applications and processes account for the majority of the data volumes and evaluate them first.
- Work with customer service managers and supervisors to analyze the quality levels of the data captured in customer service systems.
- Evaluate the impact of poor data quality on the cost of operations, customer satisfaction scores, and noncompliance with policy.
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 noncompliance, and increased customer satisfaction scores.
And, as always, don’t 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 that values its employees. Communicate your data initiatives to your agents, focusing 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.