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As the product manager for the agent experience on the ASAPP platform, I get many inputs to my team’s roadmap. In shaping our roadmap as a Product, Design and Research team we try to balance cutting-edge experimentation and data-driven advancements with ideas that come directly from talking to and observing users. We find enormous value in doing what we call side-by-sides with agents.

Agents are power users—they are adept with all the ins-and-outs of the product, which makes them incredible sources of insight into potential areas for growth. On a recent site visit we sat side by side with agents to get feedback on recent product updates and observe ongoing workday pain points. While we were there our team noticed an interesting behavior in how agents were composing messages.

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Quick typing led to many typos—which some agents took the time to correct while others did not.

Cosima Travis

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Whenever agents chose to type a message freehand, they would try to type quickly, sometimes on old, sticky keyboards, which led to lots of typos. After a typo, different agents reacted differently. Some would ignore the typos and send the message as is, favoring a quick response over grammatical correctness. Others would finish typing their full response and then spend time right clicking their typos which were underlined in red by Chrome. Many agents would notice typos midway through typing, stop, and backspace in order to manually correct their errors.

My team saw an opportunity to improve quality and efficiency. We’ve all had the experience of trying to get a thought out, mistyping, and feeling interrupted by the consideration of whether to stop and correct it, or just keep going and come back later. For agents, this is happening all day long as they respond to customers, slowing down their response time and distracting from the content of the message they’re composing.

Looking deeper into the data around misspellings, the team observed that agents tend to make the same mistakes over and over again. By simply focusing on the couple thousand most common typos, we could address the vast majority of typo occurrences. Instead of making agents manually correct the typo by right clicking in Chrome or manually retyping, why not just correct it as they go?

As we built our autocorrect feature we wanted to make sure to not over-zealously correct. We all hate when the iPhone corrects a word we meant to type right before we send a message. The team set a high bar– we would only consider the feature successful if agents undid less than 1% of the typos we corrected. When we released this capability in an A/B test, the results were staggering. Not only was the undo rate far lower than 1%, but the impact to response time and overall handle time was substantial. This feature alone was able to cut average handle time by as much as 30 seconds, not to mention raise the quality bar of the responses that agents were sending to customers.

Sometimes the biggest wins come from small simple changes. Making sure our team takes the time to sit with agents often, side by side, ensures we keep a close pulse on what changes will really make an impact, however simple.

The last couple of months have been some of the hardest for many of us—including ASAPP and our customers. What this crisis has proven is the critical importance of our contact centers to the success of our companies—across every industry.

COVID-19 has tested CX teams in new ways, in harder ways. But during these last couple of months, I’ve been asking myself this question lately—“Why haven’t some things in the contact center really changed in the last 30 years?” There are still the same pressure points. For example, agents seeking to solve customer problems that continue to increase in complexity. In the meantime, customer expectations continue to increase in terms of response and resolution time, and COVID-19 has exacerbated this need.

A customer’s experience with support can make or break your chance at repeat business.

Rick Hoefert

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Customers live in a world of instant interactions, so they don’t have a lot of patience for even slight hold times or repeated requests for information. In fact, a customer’s experience with support can make or break your chance at repeat business, as we have learned especially in the last few months.

While you’re hanging onto legacy contact center technology and processes, your competitors are using the lessons learned from this extraordinary period to break new ground in customer experience. (And I’m not talking about using simple chatbots.)

So, it’s time for change. It’s time for us to provide experiences that delight our customers, enable us to meet pressured budgets to come, and allow us to scale for the next 30 years.

Following are the five capabilities your contact center should have in a post-COVID-19 world

#1 Real-time agent augmentation

Real-time agent augmentation means that you have the capability to learn from what your very best agents are doing and saying, and then predict what all your other agents should say and do in the same situation. It means that you have the capability to listen, record, and transcribe every voice and every chat interaction. And it means you can do this in real-time—without programming FAQs and workflows into a knowledge base. It means turning every agent into your best agent.

#2 End-to-end view of your customer’s journey

To solve complex problems that occur in contact centers, agents need a comprehensive view of a customer’s journey as soon as they connect.
With legacy technology, agents only see information from a customer on one channel. Imagine the customer experience you could provide if agents could see a schedule of past interactions, regardless of channel, as well as the current context of activity—like which web page your customer is viewing now. This contextual information in real-time is a superpower that provides higher business value, better service, better customer retention, and personalized case handling.

#3: Access to real-time customer insights and sentiment

Real-time access means that you have technology in place that is learning the most common customer questions and concerns—not just historically—but that day, that hour. So, when phones get flooded with questions about the impending hurricane, the drop in the market, the current network outage—your agents have the right answers to share—in real-time—even if they’ve not been registered yet in your knowledge base.

#4 Elimination of “channel surfing” to get a problem resolved

Does this seem familiar to you?
Your customer has a question or a problem.

• They look on your web site and can’t find what they’re looking for.
• They see a chat icon and ask their question. Your chatbot crawls through FAQs and answers that have been pre-programmed and can’t help them, so suggests they call your 1-800 number.
• Your customer dials the 1-800 number and speaks or types in basic answers—problem, account number, last four of SSN. And still can’t get an answer.
• They ask to speak to an agent, who then proceeds to ask them for their problem, account number and last four of SSN.

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That’s at least (hopefully just?) four steps. Three times they’ve had to explain their question or problem, and at least two times they’ve had to re-tell who they were. You need integration between channels, and you need agent assistance on both voice and digital channels.

#5 Text and Native-Mobile Device Support as a Channel

The future of customer communication can’t just be messenger apps, social media, or ongoing voice support. It also has to include texting. Businesses that aren’t texting need to consider how text messaging fits into your communication strategy. You probably already know your competitors are doing it. Unlike chatting, texting support for your customer is like having an ongoing text conversation with a friend. They can put the phone down for a few hours — without getting disconnected — and continue the conversation later. With texting, your customer can respond when they’re ready, and go about their life the rest of the time.

CX is the new competitive battlefield. This is the team that can drive new revenue from cross-selling and up-selling. This is the team that can improve customer retention. This is the team that can improve customer satisfaction. It’s time for CX to change, to help drive positive change for their companies.

Automation in customer service is all about reducing costs—but it only pays off if it resolves customer issues and makes them happy.

For decades, bot technology (in the form of chatbots and IVRs) has been an ‘all or nothing’ proposition: an issue is either solved by a bot, or it’s sent to an agent, with agent involvement being seen as a failure of automation. It’s time to engage automation beyond the bot –  and think about the process of addressing customer needs as a continuum.

Automation is labor-saving technology, which means it can be used both instead of an agent and alongside an agent, to help him or her be more efficient and deliver a better customer experience.

Innovations in AI and machine learning (ML) are eliminating the compromise between efficiency and awesome customer experiences. By expanding the potential of automation, companies can streamline workflows and dramatically increase agent productivity—while also delivering a more personalized and pleasing customer experience, and a great agent experience, too.

Why it’s time to ‘break the rules’

It’s true that a call that never reaches an agent is the most efficient—if the customer’s need is addressed.  But if you’re measuring containment as success and relying on rules-based technology for automation, the situations the chatbot can handle are severely limited, as is your ability to effectively measure success.

Rules-based bots aren’t well-suited to address complex issues. It’s simply not possible to imagine every possible customer scenario and manually program the associated rules for anything more than simple tasks. And because these systems are brittle—costly and time-intensive to maintain—the rules set quickly grows out of date. As a result, a significant number of customers still need an agent to solve their problem.

Automation and agents need to learn from each other

The next bottleneck to efficiency is that automation and agent software are often managed on completely separate systems—so they don’t work together and neither learns from or informs the other. The rules-driven bots aren’t improving agent efficiency, and the bot isn’t getting smarter over time. And neither bot nor agent have access to the best knowledge and approaches of the organization’s best agents—a critical source of data for improving outcomes on any channel.

To achieve transformative results, you need to integrate automation in a unified way, with AI at the core.

A single platform, built on a foundation of groundbreaking AI and machine learning, enables automation to start with agents. That may seem counter-intuitive, but it’s an incredibly powerful way to fuel a self-learning system. The simple truth is that decades after the introduction of bots, large companies are still spending millions, even billions, on agents. It makes sense to focus attention there.

A unified solution with AI at the core enables you to:

• Amplify productivity with automated predictive suggestions.
• An ML-driven system constantly learns as agents use it, capturing the right thing to say and do in every scenario. Those learnings are then used to automate more and more of each interaction in the form of predictive suggestions throughout the conversation. Agents are empowered to serve customers faster, easier, and better—significantly increasing productivity.
• Increase automated efficiency over time.
• As the self-learning system gets smarter, accuracy of prediction increases. Suggestions then become actions the system takes on behalf of the agent—increasing overall automation. In some cases, that shift helps agents more efficiently handle concurrent interactions. In others, automation can eventually handle the entire scenario, freeing agents to address nuanced situations that need the human touch. All with no programming required.
• Improve customer experience.
• A unified system preserves continuity between the bot and live assistance, so when customers move from one channel to another they don’t have to start over. Agents get the full interaction history so they start with the right context to resolve the issue more quickly. And with predictive guidance every step of the way, agents can focus more attention on providing personalized service to improve customer satisfaction.
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Automation needs to go beyond the limits of ‘all or nothing.’ When CX organizations use automation to make an agent’s job easier and more efficient, that’s a huge win for both customers and the business. It’s infusing labor-saving potential at every level to improve bottom-line results.

Michael Lawder

Amplifying the value of automation

The customer service industry has come a long way since I started as an agent 25 years ago. Yet it’s taken all that time to really elevate what automation can do. It’s all a matter of perspective… Automation is more than bots. Anytime an algorithm can reduce the labor it takes for an agent to address a customer need, that too is automation.

The ASAPP AI Native®, self-learning platform is forging new frontiers to break the ‘all or nothing’ paradigm.

Companies can fully automate situations where it’s easy to satisfy a customer need without an agent. And they can use ML-driven automation to reduce the time and cognitive effort required by an agent to address more complex customer issues. No more compromising between cost-saving efficiency and great customer experiences. Now you can do both. As opportunities for automation increase over time, your customer experience team can focus on what really matters: your customers.

Automation has become the Holy Grail of the customer experience domain. Since the rise of Interactive Voice Response systems in the mid-1980s through to today’s chatbots, the promise of automation has always been the same: keep customers away from talking to agents, reducing the need for agents, ultimately lowering costs. Vendors have (dubiously) added one more message—customers actually prefer an automated interaction—implying that if given the choice we’d rather use a chatbot instead of chatting with a human agent. (I personally don’t believe it and surveys validate my skepticism.)

Unfortunately the result of this technology enthusiasm is that if a customer does reach an agent, that is considered an automation failure. It is binary—either it’s automated (no agent) or not automated (live agent).

This is a deeply flawed view.

Customers talking to agents is not a failure of automation. But thinking that the sole use of automation is to keep customers away from agents is a failure of the imagination.

Automation is a rich capability that should be engaged throughout the customer journey and agent workflows to provide an effective and efficient experience that’s fantastic for all involved.

When automation is integrated into workflows agents are significantly more efficient and customers benefit from faster problem resolution.

Macario Namie

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Agents are hired because we need their brains—to solve complex problems, to bring emotional empathy, and to positively represent the company brand. Despite what many vendors would have you believe, the state of technology is simply not good enough to replace all your agents. And it won’t be anytime soon. But that doesn’t mean we can’t use technology—and AI-driven automation in particular—to augment human agents and make them radically more productive.

Inspecting the work of a customer service agent highlights tremendous opportunities for automation. An agent’s day is filled with an assortment of workflows, processes, and tasks—looking up customer information, entering data, sharing documents with customers, collecting payment, typing contact summaries, and such. While it might seem simple to automate these efforts, that’s not the reality. Interactions are dynamic and context varies, so it’s nearly impossible for someone to write enough rules to intelligently invoke automation for all the possible scenarios.

While complex, dynamic and data-rich scenarios overwhelm the human brain, machines are perfectly capable of processing it all. Artificial intelligence technologies are designed to recognize patterns and meanings in what appear to be randomness—and through machine learning, to make predictions of the logical steps to take when a similar situation arises. When we use AI to drive the right automation at the right time—interspersing it with agent actions, we improve results in a way that’s not possible with a binary automaton-or-agent approach.

The economic impact of augmenting agents with automation can be massive. At ASAPP, we’ve seen customers dramatically increase their throughput, with one customer reporting an incredible 2x gain. That’s the economic equivalent of deflecting 50% of the calls away from agents—but easier to achieve, and more pleasing to customers.