Do You Have an Employee 35?

Mary Lou Joseph February 10, 2020

At our customer conference, Engage, an insurance company executive told an eye-popping story about a discovery they made with application usage data. They discovered an employee who was working a full day plus mandatory overtime, yet was only spending 25% of his time on actual production work. On average 61% of his time was spent idle or inactive!

No, he wasn’t a remote employee that the manager couldn’t physically see. He worked in the office.

No, he wasn’t a newbie who didn’t understand what was expected of him. He was fairly seasoned with 2 years of tenure and 1 year in his current role.

No, he wasn’t on a PIP (personal improvement plan). He was achieving his productivity and performance goals.

They dubbed him Employee 35.

They also were able to analyze their employee base and group them into four employee types:

Let me tell you about their story, what they uncovered, and how they are using that knowledge to improve employee performance for each employee type.

Implementing Desktop and Process Analytics

The insurer had been a long-time user of Verint’s Workforce Management (WFM) solution, extending it beyond their contact center to include 2,000+ back-office employees. But what they found is they didn’t have the data they needed on how back-office employees were spending their time to accurately forecast work volumes and schedule resources. They chose to implement Verint Desktop and Process Analytics (DPA).

What Is Desktop and Process Analytics?

DPA is a small applet that sits on an employee’s desktop and tracks what applications they are in, when, and for how long.  Users can easily analyze application usage, and categorize them as production-related and non-production related. An additional tool within the WFM solution call MyTime, allows employees to self-report how they are spending their time. DPA with WFM can compare WFM schedules (when they are supposed to be in production, on break, on lunch, etc.) with self-reported time, with applications that are active on the screen at any given time.

How Was It implemented?

The insurer deployed DPA “blind” for the first 30 days. This means they deployed it without telling the employees, which was done so they could get an accurate baseline of how work is typically performed. Because you know if the employees were told ahead of time, they’d be on their best behavior – at least at the beginning until old habits started to creep back.

Once the data was collected, the insurer coached managers on the insights gained and how to leverage this information in one-on-one and team discussions. They then met with each group or team to explain the solution, why it had been implemented, what they planned to do with the information, and how this data would help them in achieving their employee performance goals.

Sample Desktop and Process Analytics Insights

For example, this newfound insight and ability to compare data sources gave managers the knowledge they needed to identify opportunities to improve production rates, employee performance and utilization. Here’s an example of Jane – a Middle-of-the-Road employee (I’ll explain that more below). In the diagram below you see her:

  • WFM Schedule line, which shows a 9:15-5:30 schedule with 2 breaks (peach) and one lunch (light blue).
  • MyTime Self-Reported Time, which shows she took her lunch much later than scheduled, but in a back-office that’s not as big of a problem than if she was, say, in the contact center.
  • DPA Application Usage Line. You’ll notice that as the day progresses more red (Idle/inactive time) starts appearing, and for the last ½ hour she’s surfing the net (medium blue color).


With this data, Jane’s manager is able to work with Jane to create a motivational plan that keeps her focused throughout her work day.

What They Learned about Their Employees

So when they analyzed this data across their employee base, they saw similar characteristics that grouped people into the four employee types:  die-hards, middle-of-the-road, coasters, and Employee 35s (fortunately, there were very few of those). Each category had their own strengths, issues, and needed a different approach to correct and improve behaviors.

Die-Hard Employees

There are good and bad characteristics of die-hard employees.

  • The good:
    • They have high processing speed
    • They consistently process work throughout the day
    • They on the surface are the model employee
  • The bad:
    • They work through breaks and lunches
    • They work during their off hours.

While we all want hardworking, dedicated employees, there is a looming danger to being a die-hard – and that is burn out. We all need to take breaks to clear our heads, go for a walk, or talk with a friend.  In as competitive a talent market as we have now, it is essential that companies do what they can to maintain the health and wellbeing of their employees. Die-hards will typically put up a good front and when asked say everything is great, but in private are burned out, frustrated, and could jump ship at the first sign of greener pastures.

These employees need to be coached that it’s important to take the breaks and take the time off they’ve earned. They need to be made to feel valued and appreciated for their contributions beyond just items produced.

Middle-of-the-Road Employees

The good and bad characteristics of middle-of-the-road employees include:


  • The good:
    • They have an average processing time
    • They’re fairly consistent in processing work
    • They don’t realize the amount of idle/non-production time they incur.
  • The bad:
    • Their idle/non-production time increases throughout the day
    • They self-report time spent in production during actual idle/inactive times.

These employees are great to have, they just need a little motivation and enlightenment about reality vs. their perceptions. They have untapped potential that a little coaching and positive reinforcement from an engaged and informed manager could cultivate to increase productivity and improve employee performance.

The Coasters

I was personally surprised by one of the characteristics of a coaster. Let’s see if you can guess which one it is.

  • The good:
    • They have high processing speed
    • They have high tenure
    • They are considered high performers
  • The bad:
    • They are hitting their processing rates early in the day, and coasting for the rest of it – both during regular hours AND overtime.

So, can you guess what surprised me?

If you guessed that it was that coasters are senior/tenured employees, you’d be right. Maybe I’m a bit Pollyanna-ish in my thinking, but I would see tenured employees as more dedicated contributors to the company. But I suppose if you’ve been doing the same role for a long time, you could become bored and unmotivated.

Sharing the DPA data with coasters would certainly open their eyes to their behavior. Organizations might also consider gamifying performance and using a little positive competition to help keep more tenured employees motivated and driving toward higher production rates.

The Employee 35s

So I already gave you the synopsis of the gentleman they found and dubbed Employee 35, but for consistency sake:

  • The good:
    • They are achieving the standard processing rates
    • They are inconsistent in the time spent on processing work
  • The bad:
    • They overtly abuse the process/system.

Now some employees may abuse the system intentionally, which is unfortunate. But others, like the particular Employee 35 we discussed above, was not. He was just doing what was expected of him and nothing more.

In fact, when the insurer finally got around to the team Employee 35 worked in, and they shared the results of their DPA analysis, Employee 35 sought out his manager afterwards.  He acknowledged that he was Employee 35, and asked his manager to help him alter his behaviors so he could improve.  There was no malicious intent to defraud the company of honest work. He simply lacked the guidance and gumption to ask, what else can I do?

So in this case, and in many, the lack of focus and performance is not solely the employees’ fault. Read on to learn what the insurer realized about their management practices and expectations.

What They Learned about Themselves

After analyzing the data, the insurer learned that they had what they termed “leadership” issues: the wrong work, wrong expectations and wrong process.

Wrong Work

DPA showed that some units were spending 20+% of their time processing work in production-related applications that were NOT associated with their primary role. So individuals were spending time doing work for other groups or teams, and not the work they were hired to do. This practice greatly skewed their reporting:

  • It was not captured in their shrinkage reporting.
  • It was creating the need for mandatory overtime, because resources were spending time on the wrong activities.
  • It was causing unnecessary requests for additional staff.

Wrong Expectations

DPA clearly highlighted the need to create a new baseline for processing handle times and productivity metrics. In fact, remember Employee 35?  He managed to achieve ~70% of his daily production goals in a single hour.  Something was definitely wrong with the handle times for those work items!

This is particularly valuable to improve the performance of the coasters and middle-of-the-road employees, where it was clear that there was a lot more potential to process additional work beyond their current production rates.  The workload – and potential workload – of each business unit, team and individual needed to be reassessed in order to create accurate production rate goals and capacity plans.

You might be interested in the executive perspective, Your Workforce: an Underperforming Asset? to learn a three-phased approach to improving employee performance.

Wrong Process

DPA also highlighted for management process-related issues across the entire employee base.

  • Cherry picking. Employees purposely selecting the easy/faster processing tasks to beef up their production rates, leaving the more complex, time-consuming tasks for someone else to complete.
  • Grouped transactions. Some tasks came in as clusters but were counted as one – so employees were not getting the credit for all the tasks/items they processed.
  • Ancillary application usage. Employees were using tools such as Notepad and Word to execute their work, indicating a system issue such as the inability to directly cut/paste content from one system to the next.

The insurer is adding handle time by work type as a factor in their production rate calculations to ensure employees get fair and accurate credit for work processed. They are also analyzing the instances of when Notepad, Word and other tools are leveraged to find opportunities for better integrations and to streamline processes.

What They Did to Improve Employee Performance and Productivity

Employees were given access to their DPA data through a Performance Management scorecard. The raw DPA data was transformed into actionable, employee performance metrics that showed them time-spent-in-production-related applications, their self-reported MyTime, and their overall productivity goals. The scorecards show them how they are performing against processing standards, their personal goals and their peers.

Employees were also given a 30-day grace period to self-improve their performance based on their production goals, before managers started using the data to have performance discussions. As mentioned earlier, managers were coached on how to use the newfound insights from DPA.  They were not to focus on the negative, but rather use the information to say, “You are here – let’s create a plan to get you closer to your goal.”

Managers and employees can view the same data in the employee performance scorecards and now have a trusted, fact-based source for performance-related discussions.


What They Did Not Do

While DPA can capture specific URLs and webpages employees were visiting, managers were NOT given this granular detail. They learned from others who have implemented the solution that this data can just lead managers down the wrong path – focusing on the negative.

For example, employee X spent 20 minutes on social media sites, or an hour over the course of a day IM’ing a fellow employee who has nothing to do with their role. The specifics are just not helpful information for the manager to know. By grouping these non-production related activities together, managers focus on decreasing the amount of time spent on these tasks, vs. the tasks themselves.


So what are the takeaways from our insurer’s story?

  1. Your top-performing employees might be hiding behind inaccurate handle times
  2. Employees not reaching their full potential might be your fault, not theirs.
  3. DPA application usage data can become a key source of performance data, highlighting opportunities to improve employee performance
  4. There’s likely 10% to 15% opportunity to increase production rates by transforming application usage data into actionable performance metrics and scorecards.

The insurer continues to refine and evolve their DPA implementation they are implementing DPA in other business units and hope to expand the deployment to the entire enterprise. They continue to analyze processes to streamline and remove unnecessary steps, as well as improve employee training on processing standards.

Do they have an employee 35 (or two)?  Is your workforce made up of a lot of coasters and/or middle of the roaders?  How effective are you at managing your die-hards? What tactics have you taken to identify and coach different employee types?  Share your thoughts and stories below.

You may enjoy this content as well: