Part 2: Technology and Workforce Management’s Role in Driving Higher Concurrency and Throughput
In our last post we discussed how the ASAPP self-learning platform augments your agents and automates micro-processes, enabling agents to focus on the truly value-adding parts of their job. This substantially reduces the cognitive load (amount of your agents’ focus and attention) required to resolve any customer issue.
The result is more efficient conversations (shorter handle times) and freed up agent mental capacity. This increased capacity can be thought of as slack in the system—allowing for additional conversations to be handled by the same number of agents, all while improving the customer experience and reducing wait times. Taking advantage of this slack in the system by driving higher concurrency requires the alignment of both technology and workforce management.
Let’s define two terms for this discussion:
- Agent capacity: The number of customer conversations each agent can handle in a given time period
- Volume: The number of inbound messaging conversations each agent receives in a given time period
If we increase agents’ capacity (by freeing-up their focus), and don’t increase the volume, we’ve missed one whole side of the equation. There will be increased slack in the system, though agents will still handle one conversation at a time. Only with greater volume can we increase concurrency and savings.
We have to take operational action to increase the volume of conversations per agent. This action can take two forms:
Drive volume from calls to messaging—resulting in additional interactions at $0 cost
Give your customers a digital option everywhere they might engage to call you—and provide them with delightful digital experiences and you’ll drive customers from phone to digital. With more customers using messaging, agents will more frequently engage in two conversations at once.
Rightsize staffing—resulting in the same same number of interactions for less cost
Many common and outdated models for workforce management, such as Erlang-C, are based on agents having a concurrency of one. This assumption results in over-staffing and agents frequently interacting with only one customer at a time. Contact center managers must re-examine staffing models given agents’ increased capacity. With fewer agents, agents would more frequently engage in two conversations at once driving meaningful throughput improvements and savings.
In the absence of either measure above, we’ll increase each agent’s capacity only to find they’re still working with only one customer at a time. But if we increase the ratio of customer conversations to agents (after increasing their capacity), there will be enough conversations to ensure agents are handling several messages at once, resulting in massive concurrency improvements, higher throughput, and meaningful savings.
Mike Friedman is Head of Business Operations at ASAPP. In this role he works to improve the value and delivery of each ASAPP deployment. He has over a decade of experience spanning finance, technology and M&A. He holds an engineering degree from University of Michigan and studied Finance & Entrepreneurship at the Wharton School at the University of Pennsylvania.