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Agent engagement is critical for a contact center as it directly impacts the customer experience. Many companies only interact with their customers a couple times a year, for just a few minutes each time, via an agent. The experience of those brief interactions is critical for customer satisfaction and retention, and happier agents tend to have more positive interactions with customers. Despite this important correlation, agent satisfaction in most industries is low. Some estimate that under 40% of contact center agents are extremely satisfied with their job.

Keeping agents engaged and satisfied is more challenging than ever due to recent events. Agents’ jobs are increasingly difficult and stressful with dramatically higher volumes, long wait times, and unhappy customers. Meanwhile, systems haven’t caught up. Most agents still have to toggle between many different, antiquated systems to solve a customer issue. And if agents are working remotely to limit health risks, they miss out on things that contribute to engagement—like coaching and a sense of community with their peers.

So what’s a company to do?

While modern, AI-based technology can’t alone solve these problems, it can provide some much needed relief for agents—and help your customers, too.

A well-designed AI solution can:

Help agents do their jobs more efficiently and effectively, with less stress.

Artificial intelligence capabilities can help agents more effectively handle volume spikes, unhappy customers, and whatever else might come their way in these unprecedented times. AI-driven predictions put the right content at their fingertips and suggest the right actions. This helps the agent, shaves time from each interaction, and improves the customer experience.

Provide an easy to use, self-learning system that agents love.

Many legacy systems that agents use were designed decades ago and can be hard to navigate. Modern, AI-based solutions developed by user experience specialists and data scientists provide a whole new experience for agents, helping them to do their jobs more easily and encouraging greater adoption. And the more agents use these new solutions, the better they get as AI technology continues to learn what works and what does not.

Automate mundane and routine tasks.

We’ve seen that upwards of 60% of customer interaction time is spent on mundane or routine tasks. An AI solution can automate away some of the tedious tasks that are part of an agent’s workflow, such as collecting customer information, timing out inactive threads, and summarizing interactions. This frees up an agent to focus on what they were hired to do: satisfy complex customer needs.

Improve agent satisfaction with virtual coaching.

If agents are working from home, modern AI solutions can help combat their sense of isolation with in-the-moment virtual coaching from supervisors. AI technology can also help supervisors better pinpoint which agents need help, so they can coach more effectively and improve the quality of interactions.

Job satisfaction is in many ways drawn from our own human needs—creative problem solving that engages our brains, a sense of purpose, and socialization within a broader community. With agents facing unprecedented challenges, potential isolation from their teams, and seemingly-endless routine work—it’s easy to see why customer experience suffers. This no longer needs to be true. The ASAPP AI platform can bring out the very best in each agent—which is great for your customers and your organization—and ultimately more satisfying for your agents.

I built my first artificial neural network powered chatbot in 2001 for my junior high school science fair. I spent my childhood fascinated by computers and I would type away long into the night to see what I could make a computer do. The most exciting goal was the chatbot—a program that could converse like a human.

I was incredibly excited as more and more news and discussion came out about language modeling and neural networks to build realistic chatbots and other programmers on the internet broke down the complex math so I could build one of my own. I hastily fed my creation the script of “Ferris Bueller’s Day Off” in hopes of evoking some of the titular character’s dry wit. When it had finished digesting I beamed when it started off a conversation with an inappropriate but grammatically correct statement. Unfortunately, while unintentionally hilarious, the balance of the chat was a non-grammatical soup of words.

However, nothing I built could compare to the systems that were actually winning competitions based on hundreds of thousands of hand-tuned rules. In the nearly two intervening decades it’s only been made more clear how far away we are from generating conversation without reliance on rules, even with the state of the art neural chat models. Despite all of the research available and decades upon decades of building up these conversational rules, the current record for the Alexa Prize challenge is under 10 minutes of sustained conversation.

The cost of keeping a rules-based system current and consistent is steep—and these systems are ineffective at handling anything more complex than basic interactions.

Joseph Hackman

Customer conversations are far more complex than small talk.

The further problem with business-oriented chatbots is that helping a customer through a problem is a different task than just making small-talk. Somehow, the bots need to learn all the details of the business and then learn to achieve the correct goals for the customer. Some of these, such as pricing details and promotions may change frequently. On current systems, that means spending time and money carefully curating responses and business information. Worse yet, the costs of keeping this data current and consistent scale exponentially. While this is incredible for consultants that reap thousands of billable hours, it’s not effective for businesses and their customers. The result seems to always be deploying half-finished products over budget or abandoning them entirely.

Machine learning informs human augmentation.

On the other hand, the successes of human augmentation are many. Siri, Alexa, and Google Assistant are terrible conversationalists, but fantastic at making their users more effective. I joined ASAPP on the promise of being able to apply this type of thinking to the business world. Rather than having subject matter experts or consultants maintain thousands of pages of carefully-written copy and logic in every language your business supports, we leverage the interactions agents have with customers. Every conversation between a customer service agent and a customer on the ASAPP platform provides thousands if not millions of opportunities for learning.

Continuous learning increases accuracy.

One simple example that comes to mind is that by noting what text the agents delete (and what they replace it with), our system learns a model of how agents edit their own messages in the specific context of the business. We then apply this data-driven model as agents type, and correct spelling mistakes automatically and with exceptionally high precision.

Even better, predictions of what an agent should say or do next turn out to be both feasible and effective. Although naturally requiring large and state-of-the-art models, no business rules or scripts are required, instead drawing all necessary information from the history of interactions had on the ASAPP platform. When an agent is interacting with our system, just by presenting suggestions of what types of responses are commonly used (or indeed even what types of personalized responses each specific agent uses) we can dramatically improve the response time.

I’ve always taken great pride in designing and building systems that can handle immense scale gracefully while serving customers with helpful AI technology. The systems we are building and designing here go beyond that aim: they adapt and improve ever faster the more they are used.

Contact center operating expenses are estimated at an annual 200 billion dollars in the US. Agents and supervisors account for 75% or more of this spend. Initiatives to reduce costs have become particularly pressing in these uncertain times—but increasing the bottom line of a business via higher sales is equally—if not more—important.

Many consumer company executives are actively working to reduce their operational expenses within their contact center operations. They may:

1. Set up new programs to push for greater digital adoption and fewer interactions
2. Try to shorten interaction time
3. Consolidate technology systems
4. Reduce agent turn-over
5. Establish a faster onboarding process for new agents

The most forward-looking contact center executives are also focused on improving the bottom line of their business. They’re looking for ways to improve their customer lifetime value (CLV), conversion funnels, conversion rates, and conversion volumes across web, self-service, voice, and digital channels.

Customer experience executives are challenged to reduce spending, and at the same time help drive top-line growth.

Pierre Zum Buttel

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However, balancing opex reduction initiatives with top-line growth ambitions can be a complex undertaking. In this pursuit companies often assemble a set of fragmented solutions, which results in inefficiencies because the various technologies were never designed to operate together in an optimal fashion. The mismash of systems can cause unexpected internal costs, poorer experience for both customers and agents, and lots of frustration on everyone’s part—including the executives who are eager to see meaningful results.

ASAPP offers a different approach

At ASAPP we help companies meet both cost savings and revenue growth goals with an AI Native® platform that augments agents—driving better results in both service and sales contact centers. The platform provides a wealth of capabilities, streamlining operations. And, with machine learning at the core it delivers outcomes that far exceed what’s possible with a hodge-podge of systems.

Companies using ASAPP realize millions of dollars in real, measurable value as the ASAPP platform helps their agents be more efficient and more effective in their day-to-day job activities on both voice and digital channels. The self-learning technology enables them to address customer needs faster and with more accuracy by predicting what they need to say and do—in real time—throughout every interaction.

A few examples of real results

Support agents are able to resolve issues faster, increasing overall productivity, while sharply improving first call resolution rates. Meanwhile sales agents increase both close rates and order size, using AI-driven insights to personalize each interaction and make the right offers.

What’s more, customer satisfaction scores rise at the same time. Agents are happier, too—which reduces churn that runs as high as 50% in many contact centers. There is far less frustration (and fewer unhappy customers for agents to deal with) when they are able to serve customer needs well.

Customers get proven value

Through numerous implementations in my years at ASAPP, I have seen customers double down on ASAPP with an incredible level of commitment, engagement, and enthusiasm. Generating value is at the core of the ASAPP platform, and customers really see it in their day-to-day operations.

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.