Improving agent coaching and retention with AI and ML: Introducing SupportLogic ACE
Chief Evangelist & Head of Solutions Marketing, SupportLogic
Service Experienceagent coachingemployee retentiondigital transformation
I think by now, we’re all probably tired of hearing about the “Great Resignation” and the “Great Reshuffle” but the fact is, studies show that about four in 10 workers are potentially looking for a new job either inside or outside their current place of employment. And while potential recessionary environments tend to stem voluntary attrition – this time around it might not be the case.
So, for roles/functions like customer support – this is not an ideal scenario: losing workers, while also seeing economic uncertainty hamper your ability to backfill or competitively hire new agents.
And also by now, we in the support world know the downside of seeing agents voluntarily leave the support team. Departing agents – especially good ones – take with them institutional knowledge that isn’t easily replaced. Of course, fewer agents can be both a drain on overall productivity and have a negative effect on resolution metrics. This shift also drains the morale of the remaining agents who have to pick up the slack.
And ultimately, the bottom line gets hit hard when agents leave in above average numbers. Not only are companies losing the resources put into hiring, training and onboarding agents – but when they have to do it all again for a new agent after an early or unexpected departure – the negative ROI compounds. It’s a real problem facing support organizations today.
But what if we had the right kinds of tools in place to keep agents more engaged, and help managers be actual managers helping agents succeed, rather than administrators doing manual drudge work? What if we could shorten onboarding time by just a few weeks? What if we could lengthen average agent tenures by a few months or even years. For large support teams, those numbers add up to huge cost savings as well as increased morale and higher CSAT.
So, how can we do it?
One way is by changing the model when it comes to keeping managers truly in touch with how their agents are performing. This means making it easier for managers to identify “teachable moments” as well as those moments where agents go above and beyond, so they can be recognized and possibly rewarded for their efforts. But traditional approaches to agent coaching and case reviews are incredibly manual and very time consuming endeavors. Managers spend hours looking for the right cases to review, and might only be able to manually look into about 3-5% of total cases.
Enter SupportLogic’s new Agent Coaching and Evaluation (ACE) functionality. Now available, ACE streamlines the entire agent coaching process – giving managers more time and freedom to do what they do best – help their agents be the best they can be every day. ACE has three distinct capabilities that make it a game-changer for support managers:
It analyzes 100% of cases to find the right items to review
It allows managers to coach in near real-time to improve agent performance
It builds an agent scorecard to monitor performance over time
Analyzing 100% of cases to find coaching moments is a huge boon for managers. It means you’re truly getting a much more accurate and complete picture of the agent’s actions and where they may need help, and where they may be excelling. What’s more, ACE continuously analyzes case interactions and then automatically suggests which cases a manager should review by using our AI and machine learning to identify interesting moments. Users can do this against a predetermined rubric, or simply let the AI do its thing.
By constantly serving up cases worthy of review, that means managers can coach agents closer to when the notable moment in the case interaction occurred. This is important for a number of reasons, but mainly – the closer to an event the coaching happens, the more the agent will remember it, and be able to learn from the action. In a recent webinar with TSIA, Chris Todd, director, support programs at Snowflake explains in more detail why this is such an important facet of ACE.
Another great thing about ACE is the usability throughout the coaching process. The tool not only serves up the best cases for managers to use in coaching opportunities, it even walks them through the review process to ensure all pertinent areas are addressed. So, the coaching is not only in context and close to real-time – it’s just easier to perform.
Finally, one last gem of the ACE functionality is the agent scorecard capability. In traditional coaching models, managers have to perform their coaching outside of any system, basically in a vacuum. Coaching data and performance metrics are trapped in spreadsheets, at best, and that data cannot really be shared. It has little value other than in a small “snapshot” window in time during a coaching or review. But with ACE, the coaching and evaluations can be mixed with other agent performance metrics to view a time-series based analysis of agent performance. This centralized data set can serve multiple purposes, such as allowing managers (and agents!) to get ahead of any downturns in performance metrics, and it helps managers reward top performers. it also provides a simple point of reference, so managers can click in to discover the root cause of any negative trending, as well as positive spikes.
Ultimately, to reduce potential attrition – and do more with less managers and agents in the event of a downturn – you need the right tools. This means using technologies like AI to add the right mix of automation, and human augmentation – so your team can be more productive, feel empowered and stick around longer. Because longer tenured agents have more product and customer knowledge and provide a better customer experience. So, ACE in a way is the bridge between a great employee experience for support teams and a great customer experience.
You can learn more about ACE in our product overview HERE. To see a demo of ACE, click HERE.