Despite establishing business objectives focused on customers, most contact centers still use key performance metrics that measure efficiency of internal operations. Our benchmark research shows, for example, that the main objective for centers is to improve customer satisfaction, but the top metric is average call-handling time. This disconnect between efficiency and effectiveness can push agents into behaviors that undermine the achievement of objectives; hurrying to finish a call, for example, can leave the issue unresolved and the customer dissatisfied. We urge companies to review their key performance metrics to determine whether they help or hinder progress toward achieving business goals and whether they encourage the right behavior. We also recommend that they review how they produce their metrics, looking particularly at the data sources on which they base them, and that they adopt software that can analyze the data and help align both processes and information with enterprise objectives.
Our research shows that contact centers focus primarily on efficiency metrics related to people and processes. Two-thirds of companies use four or more metrics, and these typically include queue lengths, average handling times, hold times, transfer rates, call volumes and agent quality scores. Yet these do not measure factors that matter most to executives, whom the results show are more interested in customer satisfaction, product or service profitability and customer retention. These disconnects indicate that companies need to review their key performance metrics to balance efficiency with effectiveness and to ensure the metrics they use generate the right behavior.
How? A metric based on first-contact-resolution rate, for example, can help achieve such balance. The company saves money if issues are resolved the first time, and customers are more satisfied if issues are resolved promptly to their benefit. And first-contact-resolution rate can be even more useful when linked with other metrics and actions. Applied to agents, for example, it lets companies identify best practices and adjust processes and training so more agents can resolve more issues the first time. Linked to customers, it can identify who the difficult customers are and how best to handle them in the future. It can help identify why issues occur and what can be done to reduce the number of calls. It can influence behavior, because agents will strive harder to resolve more calls at the first attempt. It can even shape call-routing rules, resulting in more calls being routed to agents who resolve more issues the first time.
As they do this, companies need to look at how they produce their metrics. Most business-related metrics cannot be derived from a single source of data, and the “simple” task of cutting and pasting data into spreadsheets is in fact neither simple nor efficient; it is time-consuming and prone to error. Data analysis involving the integration of data from multiple sources is too complex and important a process to attempt with a blunt instrument like a spreadsheet. Products are available that can automate the analysis of data drawn from multiple business applications, speech recording, text (including social media) and desktop usage and subject it to various analytics to produce actionable information.
It is past time for contact centers to harmonize their business objectives with the performance metrics they use. If they really care about customer satisfaction and the business benefits it can produce, they have to rebalance their longstanding focus on internal efficiency. As they craft new metrics that can track and improve this kind of performance, we advise them to consider adopting analytic products that support this effort.