Call Center Performance Tracking - What's Your Level ?

How does your call center measure performance? Is it solely agent/employee level based? Is it manual or automated? Adherence, AHT, First Call Resolution, Quality and Speed of Answer... all great metrics to track but again more of a starting point. That's why we've put together a level based blueprint showing a different level of sophistication when it comes to performance tracking at call centers. 


Level 1 - Basic KPI's & a call center app

You have a selected few KPIs and a dedicated tool to track performance across your team (Please tell me it's not a spreadsheet - and it's a small app that tracks KPI's or possibly an open source solution). This level is a starting point - with a room to grow. If you are at this level - my advice is to focus on quality and pick the right list of KPI's that fit your organization, team, and process. 

There are many performance indicators to choose from: Adherence, Average Handle Time - AHT, First Call Resolution, Active Waiting Calls, Avg. Sales Per Agent, Call Arrival Rate, Callback Messaging, Repeat Calls.  The goal is not to track everything but to select a few that make the most sense and attempt to automate some of the performance monitoring with a simple app or opensource tool.


Level 2 - call center platform & manual rules

A broader set of KPIs with a few custom indicators and or KPI's combined into a performance indicator(s) - an automated tool to track it all with a contact center functionality focus. Rule-based "what-if," boolean logic, and simple logic rule chains that drive some insights and send them as daily alerts or emails.

Level 2 - is where things start to get a bit more interesting. Companies usually begin looking into a dedicated platform. Workforce management, performance tracking and maybe even gamification functionality (often gamification platforms will have all of the necessary KPI tracking capabilities within them).

Companies that have invested in a platform for automation purposes have started looking into aggregated indicators that factor in performance from several variables into a weighted KPI's. Very often it is also the case that a platform at this level would allow for rule-based insights. i.e., tracking max and min, deviation of averages, "what if" type logic. The final result of rule-based automation is usually an email alert that goes out daily to warn the management of trouble areas or give a couple of optimization hints.

Addition of group/team based performance tracking is an additional and common parameter for this level. Challenges are common especially when it comes to building customer goals on per agent level and accessing knowledge gaps is still done with 3rd party vendors poorly integrated into the platform in place.


Level 3 - omnichannel contact center

KPI's standard and custom, blended performance indicators, cross-channel performance metrics (factoring in email, text, social...and other channels into the equation) and call center related platform - all of these and more are the highlights of Level 3.

Companies at this level have a workforce management in place, gamification and or additional performance tracking in place via a hosted platform. A dozen to a couple dozen KPIs and a handful of weighted aggregated indicators in place (all tracked in real-time).

KPI tracking starts factoring in support and performance data from other channels. This is where we see companies embrace the omnichannel customer journey world and support beyond the phone while delivering accurate tracking counting in an email, social, text/chat, other messaging and channels into performance tracking.

Manual alerts are still in place, but not there is a struggle in building rules that adjust to all of the data sources and complexity of the multi-channel environment. At this level, it's common to see both website based platform and a dedicated mobile app with a "diet" view zoomed in on top metrics that are updated in real time.


Level 4 - call center kpi's & machine learning

This is when a company starts looking either into machine learning or artificial intelligence to help it get the most out of data. Usually, it's the company that has been at level 3 for at least a few years and is not realizing that it is sitting on a treasure trove of data that covers both company workforce and clients. It's time automate data crunching and analysis with artificial intelligence or machine learning.

The final output of this level is that a company can gain new insights: look into employee risks (attrition or performance), discover new optimization opportunities (dynamic daily goals - learning from past agent performance). Gaining a better understanding of team performance fluctuations throughout the day becomes possible - leading to understanding in real-time what to expect - at least in a short-term.


Level 5 - call center performance & AI

Level 5  company is repeating all of the steps in the previous level with one crucial difference - machine learning starts absorbing more data from additional/complimentary sources. AI-driven insights become a part of the call center performance tracking UI, and you look into a real-time performance optimization advice driven by machine intelligence.

This final level is all about getting better at capitalizing on AI and machine learning opportunities. Real-time insights are coming in and now you can see employee risks (performance, attrition, knowledge gaps... etc.), dynamic goals are gauged and delivered automatically, KPI's control and how to blend or combine them into indicators are controlled and overseen by both AI and your team.

AI is discovering new chains of indicators and performance metrics correlations - signaling you about new findings or abnormalities in performance. AI and machine learning is testing millions of possibilities and bringing the findings and or results to your team.

nGUVU team would like to invite you to a personalized session and demonstrate how our gamification platform can deliver across last three levels.

Let us show you how we combine gamification, performance tracking and machine learning to drive results for contact centers.