Analytics tell a story. In a call center environment, real-time data analytics can trace a customer journey, track the lifecycle of call, or uncover agent availability and staffing needs. These analytics provide basic metrics such as calls in queue, inbound and outbound calls, the number of available agents, and more nuanced analytics such as real-time speech analysis or voice transcripts. When used in part or in whole, real-time analytics allow call center managers to be proactive instead of reactive in managing day-to-day operations.
Consistent level of customer satisfaction
The foundation of real-time analytics in a call center is to provide call data metrics. Call volume is monitored, as well as calls in queue and customer wait time. The most frequent customer complaint when making an inbound call is the wait time, which occurs when a large number of calls is placed in queue. When viewing call center analytics, lengthy wait times are easily identifiable, and managers can act to minimize the wait time. If using cloud-based software, additional agents can be immediately seated to answer calls in queue.
In traditional call center environments, the audio content from voice calls is recorded, transcribed, and analyzed; all of which occurs after a call has taken place. Although this information is valuable in identifying trending customer issues and agent training, issues cannot be resolved until after they have occurred. In instances where a customer interaction turns from sunny to stormy, the damage can be harder to repair without encountering fallout. Real-time speech analytics can be configured to listen for specific words, language cues, and vocal patterns, which can then be used to alert managers to escalations on an interaction-by-interaction basis. This alert allows for the immediate implementation of de-escalation tactics, and customer satisfaction to be preserved.
Elevate agents
Analytics can also help boost agent performance, and provide context-based feedback for agent development. Metrics specific to a single agent – such as calls answered per hour and number of product upsells – can establish a baseline, qualify an agent’s strengths, and help them ascend the call center career ladder. Leveraging an agent’s strengths can also provide mentorship opportunities. For example, agents strong in upselling customers can provide a skill uplift to agents still developing their skillset.
These same metrics can also provide data-backed feedback for developing agents. Because agents cannot improve upon that which they do not know, analytics provide quantifiable examples of areas which require improvement. This information is dually beneficial as it provides managers with data to support their observations, and helps to inform a developmental improvement plan.
Actionable outcomes
One of the most powerful pieces of information real-time call center analytics provides is the ability to compare data across agents, groups, and departments. This data set can be a treasure trove of information for managers to analyze patterns and trends.
Perhaps one department is flourishing while another is floundering. At the surface level, the differences between the two department’s practices may seem similar, but the analytics can tell a different story. The inverse is also true; one department’s pitfalls may be another department’s cautionary tale. Once the data from the analytics is synthesized, you can use that data to construct a narrative. For example, when comparing the number of calls answered by a specific group of agents to the length of time each agent spent on the line with the customer, a conclusion about effective resolution can be made. Layer on top of this real-time speech analytics to search for keywords, and a story begins to emerge about best practices in resolving customer issues.
Metrics gathered from the real-time analytics can also help create training and onboarding materials. Analytics can help establish a baseline to compare a new hire’s performance to.
Overall
Real-time call analytics hold a treasure trove of information which can be used to fine-tune call center performance. Whether using basic call data analytics or advanced speech analytics, each piece of data can help tailor training and best practices to maximize customer satisfaction and agent performance.
