KPIs & Metrics

How to Create a Customer Service Data Strategy in 4 Metrics

Successful customer service management requires a data strategy. Here are four metrics to explore if you’re starting from scratch.

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Offering an outstanding customer service experience is an essential part of successfully doing business. As the saying goes, what’s measured gets managed. Increasingly, businesses are turning to data strategies to help them find out where they’re excelling, where gaps are occurring, and how to create a road map for performance improvement. If you’re unsure how to proceed with a customer service data strategy or you haven’t implemented one before, here are four metrics every service department can consider.

Net Promoter Score

Net promoter score is a metric that looks at how likely existing customers are to recommend you to others. What this metric can help you quickly understand is how satisfied your current customers are—and how those satisfaction levels may be affecting referrals, word-of-mouth marketing, and your reputation in the marketplace. If scores are lower than expected, take the time to understand what’s not working, and put a plan in place to address those issues.

Average Wait Times

Lengthy delays and wait times to speak to a customer service representative are two of the most commonly cited frustrations in the customer service field. If you are not currently tracking these metrics, it is important to take steps to understand how much time is elapsing while phone customers wait to connect with an agent or receive a response to a customer service e-mail. Look at the bottlenecks and sources of delays, and see if you can take steps to reduce them. Shorter wait times can quickly translate into more satisfied customers.

Time to Resolution

Another time-based metric that’s important for customer service teams to measure is time to resolution. This looks at how much time it takes, overall, to resolve an issue after a customer gets in touch. For example, one company I worked with initially found its time to resolution was approximately 48 hours. That meant its customers were waiting at least 2 days before their concerns were addressed. By speeding up key parts of the process, the company was able to reduce that time to less than 1 day—and customer service satisfaction rates spiked as a result.

Agent-Level Efficiency

The aforementioned metrics relate to your overall brand or customer service experience. However, it can also be helpful to measure individual agent performance and efficiency. For example, how do agents’ caseloads relate to the average? How quickly do they resolve issues? By looking at performance at this level, managers can identify star performers and develop coaching and training opportunities for people who need more help.

By investing in a data strategy for your customer service department, you can more specifically understand the customer experience you’re delivering. With hard data, it’s possible to identify what’s going well and where you can invest for maximum impact to turn things around.