Most customer support teams use a ‘scorecard’ to track key efficiency (resolutions per hour, average handle time) and quality (CSAT, NPS) metrics for each member of the team. While these metrics are important to understanding outcomes of your support operations, none of these metrics tells you why an agent makes the decisions they do in service of a particular ticket. Fin Analytics bridges that gap.
Most customer support teams use a ‘scorecard’ to track key efficiency (resolutions per hour, average handle time) and quality (CSAT, NPS) metrics for each member of the team. While these metrics are important to understanding outcomes of your support operations, the typical scorecard metrics have a few major limitations. First, these metrics only track results of work that happens in the CRM. Second, none of these metrics tells you why an agent makes the decisions they do in service of a particular ticket (so they don’t reveal the root cause of failures).
Consider these scenarios: an agent may make every ‘right’ process decision during a customer interaction, but then her browser crashes, driving up handle time on the ticket. Or, an agent might get a particularly difficult call which requires him to access several internal docs, ping the manager on call, and navigate around a bug in the CRM, all in the service of one ticket.
Traditional outcome metrics might lead you to conclude that agent is underperforming, with higher than average call times, lower than average calls per hour, and maybe even lower CSAT scores.
In both of these examples cases, however, the agent did their job correctly; these are not a performance problem where the remedy is training or coaching, but process and tools problems. In order to drive the results you care about, it is important to properly attribute root causes of failures like these.
Fin Analytics bridges the operations data gap between specific agent work behaviors and the outcome metrics you care about with new tools for properly attributing the root causes of failure that block your team from doing their best work. Fin logs a full data stream of every action taken and a complete screen recording of each agent’s workday, providing teams with the missing pieces to understand operational processes and spot opportunities for improvement.
Here are a few of the many metrics Fin Analytics provides out of the box, which help operations leaders achieve greater productivity across their whole team:
1. % Utilization. What percentage of an agent’s total day is spent actively engaged and working? Does that track with how much output you see them generating?
2. % of Time Spent in Different Resources. What percentage of an agent’s active working time is spent inside your internal CRM? What docs are they spending the most time in? How much time is spent on Slack each day / week / month?
3. Within each resource, what behaviors are you seeing? What are the most common URLs visited? What is the breakdown of Production (i.e. typing a response) vs Consumption (i.e. scrolling or reading) behavior you are observing within each resource?
Now that you have these new productivity metrics, what do you do next?
Set baselines and observe outliers. Now that you have the measurement in place, you can begin to set productivity baselines for your team, and spot any outliers. With Fin, you can now deep dive each outlier case to pinpoint exactly what went wrong, in negative cases (Was is a broken tool? An outdated process? A result of bad training?). And, you can discover what went well in positive cases (perhaps an agent developed a more efficient way to handle a certain workflow), and then bake this into best practices and training for the rest of the team.
Accelerate your QA process. Video review in Fin Analytics makes QA far more efficient by eliminating the need for manual shadowing, and giving your team the ability to play back any case or moment of an agent’s work day at up to 5x speed. With Fin Analytics, you can also focus your QA team on the most important cases to review - problematic workflows or teams, and the outliers within these segments - rather than reviewing random samples.
Traditional quality and efficiency metrics are valuable indicators of your team’s performance, but can only provide part of the whole picture of the team’s work. Fin Analytics fills the operational data gap with its robust data stream and video recording capabilities, empowering teams to increase productivity and drive toward their goals.