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Using Analytics to Improve Agent Performance: Anchors, Sleepers, and Weak Links 

Bill Price | Jan 19, 2017 252 views No Comments

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In my last column “Using Big Data Analytics to Find Your Best Agents and Supervisors” I introduced a new way to find more agents (and supervisors) who are like your best performing agents (and supervisors) using Big Data analytics. After collecting the metrics on two opposing axes, being efficient vs. being effective, and adjusting using DIM or Dynamic Individual Metrics, you can plot all agents on the X-Y axis as this example shows:

bp_agents

The Stars or “best agents” in the upper right corner somehow are able to achieve both high levels of efficiency and effectiveness, so that’s where we start: You can collect and model using Big Data potential drivers such as prior jobs, hiring and initial training class, distance from work or commute time, number of supervisors (and which ones), and more. By running multiple models and finding best fit, you are able to learn what determines a “best agent” and how to re-tool your hiring profile, trainer profile, supervisory assignments, and the other key drivers.

Then you can run the same models for all other agents, revealing key differences that begin to explain their weaker performance, and that’s what I’ll address in this column.

Retrain “Anchors” to Slow Down

Let’s first start with the Anchors, those agents (and supervisors’ teams) who are efficient but not effective. They might be the folks who have always been rewarded for being fast, but whose effect on customers hasn’t been collected or analyzed. When you do that you discover that they routinely upset customers and cut short calls or interactions that cause repeat contacts (what I like to call “snowballs”1). Not good!

But when you “mash up” multiple data sources using Big Data, for example agent-level repeat contacts or c-sat scores, you discover that in some cases fast isn’t the right solution. What to do with the Anchors is really hard, since they have been so successful at being so one-sided, but it’s essential to re-train them to slow down, use established processes to do the right thing for customers, and understand the negative impact of being so fast.

Leverage “Sleepers” as Experts or Trainers

Now let’s spend some time with the Sleepers, the agents (and supervisors’ teams) who are slow but highly effective. They might be the folks who are methodical in their work, carefully listening to customers’ issues and complaints, or patiently collecting customer insights, oblivious to the clock.

While some companies quickly place the Sleepers “in the penalty box”, perhaps with a draconian PIP (Personal Improvement Plan), the best solution that I’ve seen is to re-position them to become trainers or process experts in your operations.

Help “Weak Links” Find Another Opportunity

In the third corner are the Weak Links, those agents (and supervisors’ teams) who are both inefficient and ineffective, a damaging combination. Instead of placing them on a PIP or trying to improve their performance, it’s probably best to acknowledge that these folks are really hiring mistakes and need to go somewhere else.

Finding the Root Cause of Performance Differences

For these three corners, as well as the much sought after Stars, Big Data can play another critical role. Not only does Big Data pull together (mash up, as I noted) disparate data sources, but it can tell you why these agents (and supervisors’ teams) are performing in these ways. Is it because of…

  • Their prior experiences?
  • Their training at your company?
  • Their first supervisor or manager?
  • Your expectations and requirements?
  • Rewards and recognition?
  • The reader service boards festooned in your contact centers exhorting agents to take more calls?

It might be these reasons, or not, and other ones. Only using statistical modeling can you find out, and be sure not to repeat the same mistakes.

After all, don’t we all want our agents and supervisors to be Stars? Of course we do!

Don’t Forget to Motivate

I’ll close with this comment from when I ran global customer service at Amazon, in early 2001. We had just completed our holiday in the year 2000, growing revenues by over 90% quarter over quarter, relying partly on an outsourcer in Gurgaon, India that handled much of our email traffic. bp_award

Replicating an Amazon captive center recognition program called “Over The Top” that I carried over from my years at MCI, that outsourcer sensed that we had to over-deliver on quality for our holiday customers so they asked if they could weight effectiveness 75% and efficiency 25% for their “best performers” (the Stars) to join me at their 1st Over the Top event in February 2001.

I then joined the top 15% of agents who balanced excellent efficiency and effectiveness, the Stars, at a gala off site in a converted maharaja’s palace northwest of Gurgaon. These agents relished their night, and many of the became repeat winners; the other 85% heard about the events and clamored to figure out how they could become Stars – A solid tradition was born.


1 The Best Service is No Service: Liberating Your Customers From Customer Service, Keep Them Happy, and Control Costs, Bill Price & David Jaffe (Wiley 2008).

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