What’s new in AgentBalance 1.3
Apart from obvious minor enhancements and bug fixes, we have focused on our reporting section. Until the recent update, we were displaying agents’ scores only in charts, which is fine for getting deeper understanding of current situation in teams, but could be inconvenient for getting a quick overview of what’s going on right now.
That’s why we are introducing an Overview section.
We also call it a “Trouble Heat Map”, because this view marks problematic results with colors, making it really easy to instantly see where particular trouble might be. In the case depicted above the team leader of this team would quickly realise that agent Portman Paul reached alarming values in several results – decreased level of mental energy and extremely low job fulfillment.
This Overview shows last four tests and calculates median values of agents’ results. Four test results mean one month of testing, which is a perfect frame for team leaders’ observations – not too short to make premature assumptions and not too long to deal with history that is far too old for evaluating actionable data.
The second major update brings the ability to define custom levels of observed values – extreme, high/low and balance. AgentBalance automatically calculates with distribution of response patterns within population but contact centers could present different values because contact center agents represent a specific group of people with unique characteristics. What could be considered an extreme value in population, might be still considered as an acceptable or just increased value in a contact center.
In the case of the team in this screenshot there is a balanced area shifted by 15 points up, because in general, without extremes, the team presents a higher level of job fulfillment than the rest of population.
In upcoming AgentBalance releases it is our plan to suggest the optimum balanced area for each team based on its long-term results and also to display a benchmark zone by comparing the contact center with averages from the same geographical region. This will help team leaders to compare their teams’ psychometric data with teams from other contact centers in order to focus on areas that might need an additional care.