Mike Friedman

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
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Once you’ve increased agent capacity, you must have a plan to drive additional conversation volume to dramatically increase concurrency.

Mike Friedman

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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:

1. Drive volume from calls to messaging—resulting in additional interactions at $0 cost
2. 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.
3. Rightsize staffing—resulting in the same same number of interactions for less cost
4. Many common and outdated models for workforce management, such as Erlang-C, are based on agents having a concurrency of one.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.

Making Conversations Easier for Agents

There are a number of levers you can pull to drive dramatic cost savings in your digital messaging program while maintaining high levels of customer satisfaction. One of the most impactful is concurrency—the ability for agents to manage multiple conversations at once. Concurrency can substantially reduce labor costs by empowering agents to manage up to 6 conversations at once. But isn’t that overly ambitious, demanding for the agent, and challenging to staff? All of these factors are easily solvable in pursuit of massive customer care cost savings.

Your agents can manage multiple conversations at once—and keep your customers happy—when you use AI to support them well.

Mike Friedman

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Achieving high concurrency is all about managing the cognitive load—the amount of your agents’ focus and attention—required by a conversation at any given time. The truth is, most interactions don’t require your agents’ full focus and attention. That means there is an opportunity to productively engage your agents’ remaining brainpower. Further, by reducing the cognitive load of each interaction, you can enable your agents to manage multiple conversations concurrently—all while preserving quality.

At ASAPP, we’ve designed our platform to support your agents and enable high concurrency. We do this through four tactics that substantially reduce cognitive load:

• Micro-Process Automation
• The ASAPP platform exchanges information with your customers, drafts messages and summary notes, predicts needed content, and writes updates to other systems of record (like your CRM).
• Flex-Concurrency AI
• Through machine learning, we’re able to monitor conversations to understand the level of engagement required by the agent based on many factors to dynamically shift concurrency up or down.
• AI-Driven Intelligent Routing
• Sophisticated routing models match your customers with the best agents to meet their needs; agents who’ve solved similar problems before and are more efficient.
• Highly Instrumented and Researched User Interface
• Optimized user interface designed to manage agents’ focus, reduce swivel-chair behavior, and help agents engage in concurrent conversations without missing a beat.

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By applying the tactics and technologies above, we can drastically reduce the cognitive load of each customer conversation and free up their attention to handle multiple conversations at once.

Technology and Workforce Management’s Role in Driving Higher Concurrency and Throughput—Part 2