How does your contact center track performance data? Standard industry metrics like average handle time (AHT) and first contact resolution (FCR) are important but don’t always tell the whole story, since agents often use multiple tools, systems, and data repositories that don’t communicate cross-platform. What if there was a way to pull siloed data from all of your CX tools into one reporting suite? Then contact center leaders could more easily spot patterns, glean insights, and get an overall holistic view of agent performance and how agents make decisions.
Using rapidly advancing technologies, such as robotic process automation (RPA) and machine learning (ML), to monitor comprehensive agent activity across all tools and systems—not just activity in a CRM—is the missing link in many contact center tech stacks available today.
Many tools provide analytics on activity within the tool itself, unable to track what’s happening when an agent toggles to another tab or window. Other solutions only monitor what the customer is doing—but this approach doesn’t take into consideration the complete interaction between a customer and agent.
Think about the last time you were with somebody who was talking on the phone. Maybe you could sort of understand what they were chatting about, but trying to follow a one-sided conversation obviously leaves out a lot of context.
The same goes for analyzing a contact center case. When you have a full view of all agent activity, as well as customer data, you get a more complete picture. But how does a CX program address this very common challenge? That’s what inspired Laivly to design our insight and analytics solution, Silent Sidd, and give contact centers the data they’d been missing.
Monitoring customer activity vs monitoring agent activity
While monitoring customer activity can provide great insights for your brand, understanding what’s going on behind the scenes with contact center agents gives a fresh perspective.
Many tools out there are designed to track the customer journey. For instance, a tool might log customer activity as soon as they visit your website, following along as they browse pages, click a help center article, and send a message to your chatbot. Or it might create a heat map of where customers spend the most time on a given page.
Laivly’s data and analytics tool, however, follows the agent process from start to end. And because Laivly’s digital intelligence, Sidd, lives right on a contact center agent’s desktop without complicated backend integration, it can track an agent’s activity across the entire case and across the agent’s entire CS tech stack—not just within a specific tool. Where are they getting stuck the most? What workflows are the most difficult for them? Which steps eat up the most time? Where do they need more training? These questions can be answered with an AI-powered insights and analytics tool.
Benefits of analyzing agent activity throughout a customer service case
Sidd can track how many times agents perform manual searches and order lookups, as well handle times and case outcomes. It can see that an agent clicked on an inbound email, then four minutes later processed a refund instead of a reship. This information is two-fold, as it uncovers valuable information on both the customer and the agent side of things. Did the agent follow appropriate steps to encourage a reship before processing the refund? If not, this could highlight a coaching opportunity or a pain point within a workflow. If so, and paired with additional data, it could indicate a customer trend or a problem with a specific item.
By analyzing agent analytics on product return cases, your brand can uncover customer tolerance for reship scripts and workflows, trends in why customers want to make returns, where in the process customers most often change their minds or opt for a reship, and other incredibly useful insights.
With analytics on an agent’s workflow, steps taken, the order of the steps taken, and the final result, your brand can make data-driven decisions around training, coaching, workflows, appeasement policies, customer retention tactics, and more. In short, it allows your brand to be more strategic and proactive.
Platform-agnostic analytics tools provide a comprehensive view
Remember the days when the only metric you could track for a website was its number of visitors? When Google Analytics came along, suddenly you were able to get a much more comprehensive view of visitor activity, from session duration to which pages were visited in what order—all this data compiled for you in one place simply by adding a Google tracking code to your website. Similarly, Sidd provides a high-level overview by using intelligent automation to piece together data from across your tech stack and compiling all the information in one place.
Sidd is unique in its ability to silently track, understand, and support metrics across multiple and varied platforms at the same time. Instead of having all these little snippets of data scattered across individual systems and tools, Sidd is able to do it cross-platform. Putting all this data together gives a big-picture view that otherwise wouldn’t exist. This is especially useful for brands that don’t already have baseline data for their handle times, or those that don’t have set KPIs. When your brand is armed with data, you’re able to make better decisions for your customers, your agents, and your bottom line.
To find out more about how you can use Sidd to analyze agent activity in your contact center, book a demo with the Laivly team.