I began my involvement with contact centers – actually they were called call centers in those days -more than 20 years ago. I quickly learned that almost everyone involved in running a contact center is obsessed with metrics: queue times, average call handling times, agent utilization, average length of after-call work – the list seemed to be endless. Since joining Ventana Research I have carried out numerous benchmark studies into customer and contact center performance, and found things haven’t changed a great deal. The number of metrics has increased and old favorites are still high up on the list.
In my earlier research into contact center analytics, I divided the metrics into two broad categories: financial and process. From a financial perspective, companies are most focused on seeing a comparison between actual operational costs and budgeted costs, and the average cost of handling different interaction types – calls, email and letters. Meanwhile, the research shows that as far as process metrics are concerned, things haven’t progressed a great deal. What I call efficiency metrics still dominate, and average call handling time is still number one. The one positive I take for the results is that compared to my earlier benchmarks in first contact resolution rates (FCR) have increased in importance, with 68 percent of respondents indicating it is an important metric. I view FCR as a mix of an efficiency and outcome metric because it reflects efficiency of operations, in that there will not need to be as many call-backs, and it is also an outcome metric, in that customers are likely to be more satisfied if they get their issues resolved at the first attempt. However, companies need to be careful how they measures FCR, because a customer might raise the same issue through another channel or come back at a later date with the same issue.
The most disturbing insight from the research is the percentage of companies that still rely on spreadsheets to produce their contact center and customer reports and analysis. Just under two-thirds (62%) of the respondents indicated they use spreadsheets as their primary tool, and 90 percent said they use spreadsheets on very regular basis. The challenges of using spreadsheets are manyfold: they are labor-intensive and prone to data entry errors, and there are often long gaps between the data being available and the final reports being distributed. However, as the research shows, by far the biggest issue is accessing all the data sources that include customer-related data. These now include transactional data in structured files, call recordings, text-based data such as CRM notes, email, survey forms, letters and social media posts, and machine-based data such as failed calls. To gain a full view of contact center performance and customers, organizations need to access all these forms of data, aggregate them, then produced consolidated reports and analysis to support all customer-facing activities. Given the sheer scale and complexity of these data sources, customer data falls squarely into the big data arena, requiring companies to investigate specialist tools that can not only access all forms of customer data but can do so at the speed of light so that the information is available to support real-time activities such as answering a customer call. The most advanced organizations also look for these tools to include predictive analytics capabilities so they can predict potential future customer activities, such as a propensity to terminate an agreement because of bad customer service.
In one of my recent blogs about the 2.0 world I noted that consumers have changed their communication habits, the channels through which they investigate and buy new products, and the ways they collaborate with other like-minded consumers. This means companies have to run to catch up. They need better views of their customers, interaction-handling performance and the outcomes of interactions. They therefore need to reevaluate the metrics they use to monitor and assess these critical activities and the technologies they use to produce them. In the past, companies struggled to build the business case to invest in analytics; my research shows that if companies use a better balanced set of metrics, including outcome metrics, then it is easier to build a case and derive full benefit from such investments.
Richard J. Snow
VP & Research Director