Benchmarks: What We’re Seeing For Average Handle Time and First Resolution Time in Q2 & Q3 2020

Benchmarks: What We’re Seeing For Average Handle Time and First Resolution Time in Q2 & Q3 2020 TW

“Unprecedented times” feels like such an overplayed phrase at this point, but it’s true. As a Customer Success Manager at Kustomer, I’ve had a front-row seat to how the pandemic has impacted (and still impacts) the businesses that are under my care. Some are struggling, some are booming. As I collaborate with my clients in building out business strategies, examining year-over-year performance trends is a tricky endeavor. It’s a bit like trying to judge the size of a hurricane when you’re sitting in the eye of the storm. 2019 feels like aeons ago at this point, and what does it really tell us if a business’ first response time increased by 30 seconds from 2019 to 2020?

As a personal project, I began studying the performance of our clients from March 2020 to August 2020. Many companies have been focused on this window of time as it relates to their performance in a post-COVID world. While there are several metrics that I could have focused on for this project, I chose to spotlight two: First Resolution Time and Average Handle Time. In my opinion, these metrics are some of the most impactful when it comes to judging your team’s performance.

First, I gathered the Average Handle Time (AHT) and First Resolution Time (FRT) metrics for each of our clients. Then, I defined the industry category of each organization. I used the following overarching categories:

  • Delivery
  • Marketplace
  • Retail
  • Services

Once I had the data, I first explored it by sorting clients by their industry categories. I built a pivot table and gathered the minimum value, maximum value, mean, and median of those respective categories. Then, I explored the data without pre-emptively sorting them into industries – this is important because I didn’t want my industry sorting from the first exercise to lead me to any false conclusions. For the second exercise, I re-sorted the data into ranges of values for both Average Handle Time and First Resolution Time metrics without grouping by industry. I then took note of how industries aligned or did not align to my first analysis. Finally, I documented the correlations I observed.

As I began analyzing the data, I approached my research with a central hypothesis: Average Handle Times will be higher for clients in our Marketplace and Service industries and lower for clients in our Delivery and Retail industries. Additionally, First Resolution Times will be higher for Marketplace and Service clients and lower for Delivery and Retail clients. At a high-level, I found that my hypotheses were supported.

There is a wide spread of data for Average Handle Time and First Resolution Time across all of our clients. There are organizations that operate at opposite extremes within the same industry, ultimately skewing the data. A quick example: the retail category of clients has a minimum value of 0.82 minutes for Average Handle Time but a maximum value of 46.6 minutes for the same metric. To circumvent this skewing, I used the median values of these metrics as they are better indicators for general benchmarks.

I developed the following recommendations for client benchmarks as they relate to Average Handle Time and First Resolution Time:

  • Delivery: 4.45 minutes AHT | 10.2 hours FRT
  • Marketplace: 7.5 minutes AHT | 106.8 hours FRT
  • Retail: 6.25 minutes AHT | 9.15 hours FRT
  • Services: 8.7 minutes AHT | 22.2 hours FRT

To supplement my research, I also read about academic studies on benchmarking (and how to successfully apply them to improve team performance). A fascinating read that I uncovered was a study completed by Peter Dickson that examines the competitive advantage businesses gain by implementing customer improvement practices. Benchmarking is considered to be a customer improvement practice, and it was enlightening to learn more about how this particular project could lead to more successful outcomes for our clients. Dickson writes the following: “Both management and evolutionary economics describe a behavioral theory of the firm where an organization’s routines determine its competitiveness. Higher-order search and learning processes improve organization routines that are defined as ‘ways of doing things that show strong elements of continuity.’ According to these theories, the long-term survival, evolution, and growth of organizations in competitive markets depends, in large part, on the superiority of an organization’s routine process improvement practices”.

While I don’t believe that using these benchmarks will make or break the future success of an organization, it is important to consider the implications of encouraging customer service teams to think about improvements. These improvements promote successful businesses, and giving your agents pursuable goals builds accountability and ownership.

Something important to consider: There may be times when an organization willfully ignores benchmarking – particularly if they are implementing a cost-saving strategy. Always consider what’s best for your brand and your team.