The Customer Care industry has its footprints all over the world and scale of companies in this industry has a wide continuous range. Let’s consider an example of a call center where agents take incoming calls for a wireless carrier such as AT&T, Verizon or Vodafone. The activities and availability of agents taking calls are generally tracked using switches. So on a broad level, the detailed and comprehensive tracking data for each agent is available in call centers especially to the Operations and Technical teams.
But, is looking at this “raw” tracking data sufficient to form decisions about an agent’s Presence, Performance and productivity? We have defined these 3 parameters as the 3Ps.
Based on my experience looking and examining volumes of call center agent data, my answer would be “No”.
The raw tracked data is difficult to be formulated into decisions/actions to be taken on agent’s activity over a period of time. Why? The reason is, qualitative analysis of the raw data consumes a lot of time and energy of the operations team and therefore, it might not be a feasible approach. However, the good thing is that the solution to this issue does not lie very far away from this tracking dataset. The tracking switch data could be cleansed, processed and analyzed (ETL, stands for Extract Transform and Load) to produce actionable Agent Level Dashboards. These dashboards are vital for both the floor TLs (Team Leads) and Operations teams in order to identify and correct agent performance, Productivity and Presence related challenges. Agent Level Dashboards show aggregated and calculated numbers that are well organized. Some of a few important metrics (mind you, there are plenty more that have not mentioned below) which outline the activity level of an agent are:
- Schedule adherence
- Occupancy rate
- Average handling time
These agent level statistics are then compared against the SLA’s followed in the call center process and necessary actions and improvement roadmap can be formulated for each agent. For example, if the average occupancy rate of an agent is 60% which might be lower than the SLA occupancy rate for the call center process or Average Handling Time (AHT) is significantly high, then such an agent could be pulled out by TLs to take corrective steps such as coaching, training, etc. This forms an important tool for the Call centers’ Quality process/back office processes/ back office automation services to ensure an efficient and smooth process of managing the activity of agents on the floor.
In my next blog post, I will discuss some of the widely used statistical method/approaches that could be used to take the idea of the Interactive Dashboard Accountability Tool to more robust and predictive capabilities.





