AWS Machine Learning Blog

Best practices in customer service automation

Chatbots, virtual assistants, and Interactive Voice Response (IVR) systems are key components of successful customer service strategies.

We had the pleasure of hearing from three AWS Contact Center Intelligence (AWS CCI) Partners as part of our Best Practices in Customer Service Automation webinar, who provided valuable insights and tips for building automated, customer-service solutions.

The panel included:

Why build a chatbot or IVR?

Customers expect great customer service. At the same time, enterprises struggle with the costs and resources necessary to provide high-quality, highly available, live-agent solutions. Automated solutions, like chatbots and IVR, enable enterprises to provide quality support, 24/7, while reducing costs and increasing customer satisfaction.

Although reducing costs is important, a big reason enterprises are implementing automated solutions is to provide a better overall user-experience. As Brad Beumer of UIPath points out, it is what customers are asking for. Customers want a 24/7/365 experience—especially for common tasks they can handle on their own without an agent.

Self-serve, automated solutions help take the pressure off live agents. As Rebecca Owens of Genesys mentions, self-service can help handle the upfront tasks, leaving the more complex tasks to the live agents, who are the contact centers’ most valuable assets.

The impact of COVID-19

COVID-19 has had a significant impact on the interest in chatbots. Shelter-in-place rules affected both the consumers’ ability to go into locations, and the live agents’ ability to work in the same contact center. The need for automated solutions skyrocketed. Genesys saw a large increase in call volumes—in some cases, nearly triple the volume.

Chatbots are not only helping consumers during COVID-19, but work-from-home agents as well. As Beumer mentions, automated solutions help offload more of the agents’ tasks and help them with compliance, security, and even VPN issues related to working from home.

COVID-19 resulted in more stress on existing chatbots too. As Pat Higbie of XAPP AI shares, existing chatbots were not set up to handle the additional use cases people wanted them to handle. These are opportunities to take advantage of AI, through tools like Amazon Lex or Amazon Kendra, for chatbots and natural language search, to enable users to get what they need and improve the customer experience.

Five best practices

Building automated solutions is an iterative process. Our panelists provided insights and best practices when facing common issues.

Getting started

Building conversational interfaces can be challenging because it is hard to know all the things a user may request, or even how they pose the request.

Our panelists see three basic use cases:

  • Task completion – Collecting user information to make an update, like an address change
  • Information requests – Providing information like delivery status or a bank balance
  • Efficient routing – Collecting information to route the user to the most appropriate agent

Our panelists recommend getting started with simpler use cases that have a high impact. As Beumer recommends, start with high-volume, low-complexity tasks like password resets or lost credit cards. Owens adds that starting with high-level Natural Language Understanding (NLU) menus to understand user intent and routing them to the right agent is a simple investment with a significant ROI. Afterwards, move to simple task automation and information requests, and then move into the more advanced use cases that were not possible before conversational AI. As Higbie puts it, start with a quick win, like informational chatbots, especially if you have not done this before. The level of complexity can go up quite dramatically, especially with transactional use cases.

As complexity increases, there are opportunities for more advanced use cases, like transactional or even proactive use cases. Owens mentioned an example of using AI to monitor activity on a website and proactively offering a chatbot when needed. For example, if you can predict the likelihood of an ecommerce user having an issue at checkout, a chatbot can proactively offer to help the user, to lead them through completion so the user does not abandon their cart.

Handling fallbacks gracefully

Fallbacks occur when the automated solution cannot understand the user or cannot handle the request. It is important to handle fallbacks gracefully.

In the past with contact centers, users were often routed to an agent when a fallback occurred. Now with AI, you can better understand the user’s intent and context, and either send them to another AI solution, or more efficiently transfer them to an agent, sending the full context so the user does not have to repeat themselves.

Fallbacks are an opportunity to educate users on what they can say and do—to help get users back on the “happy path.” For example, if the user asks for something the chatbot cannot do, have it respond with a list of what it can do. Predefined buttons, referred to as quick replies, can also help let a user know what the chatbot can do.

Supporting multimodal channels

Our panelists see enterprises building automated solutions across multiple channels, including multi-modal text and voice options on the web, IVR, social media, and email. Enterprises are building solutions where their customers are interacting. There are additional factors to consider when supporting multiple channels.

People ask questions differently across channels. As Higbie points out, users communicating via text tend do so in “keyword style” with incomplete sentences, whereas in voice, they tend to ask the full question.

The way the chatbot responds across channels can be different as well. In text, the chatbot can provide a menu of options for the user to click. With voice, if there are more than three options, it can be difficult for the user to remember.

Regardless of the channel, it is important to understand the user’s intent. As Beumer mentions, if the intent can be understood, the right automation can be triggered.

It can be helpful to have a common interaction model for understanding across channels, but it is important to optimize the actual responses for each particular channel. As Higbie indicates, model management, dialog management, and content management are all needed to handle the complexities in conversational AI.

Keeping context in mind

Context is important—what is known about the user, where they are, or what they are doing can help improve the user experience.

Chatbots and IVRs can connect to backend CRMs to have additional information to personalize and tailor the experience. They can also pass along information gathered from a user to a live agent for more efficient handling so the user does not have to repeat themselves.

In the case of voice, knowing if the user has been in recent contact before can be helpful. While introductory prompts can be great to educate people, if the user contacts again, it is better to use a tapered approach that reduces some of the default messaging in order to have a quicker opening response.

The context can also be used with proactive solutions that monitor user activity and prompt if help is needed.

Measuring success

Our panelists use a variety of metrics to measure success, such as call deflection rates, self-service containment rates, first response time, and customer satisfaction. The metrics can also be used to calculate operational cost savings by knowing the cost of live agents and the deflection rates.

Customer satisfaction is very important—one of the goals of automated solutions is to provide a better user experience. One way UIPath does this is to look at Net Promoter Scores (NPS) before and after an automated solution is launched. Surveys can be used as well, via outbound calls after an interaction to gather customer feedback. With chatbots, you can immediately ask the user whether the response was helpful and take further action depending on the response.

Automated solutions like chatbots and IVRs need continuous optimization. It is difficult to anticipate all the things a user may ask, or how they may ask them. Monitoring the interactions to understand what users are asking for, how the automated solution is responding, and where it needs improvement is important. It is an iterative process.

What the future looks like

Our panelists shared their thoughts on the future of automated solutions.

Owens sees an increase in usage of automated solutions across all channels as chatbot technologies gain momentum and AI is able to handle even more tasks and complexity. Although customer service is heavily voice today, she is seeing a push to digital, and expects the trend to continue. One area of growth is in the expansion of language support in AI beyond English to support worldwide coverage.

Beumer envisions expansion of automated solutions across all channels, for a more consistent user experience. While automation will increase, it is important to continue to make sure that when a chatbot hands off to a live agent, that it is done so seamlessly.

Higbie sees a lot of exciting opportunity for automated solutions, and believes we are only in the “first inning” of AI automation. Customers will ask for even more than what chatbots currently do, and they will get the responses instantly. Solutions will move more to the proactive side as well. He sees this as a bigger paradigm shift than either web or mobile. It is important to commit now and not be displaced. As he summarizes, enterprises need to get started, get a quick win, and then expand the sophistication of their AI initiatives.

As the underlying technologies continue to evolve, the opportunities for automated chatbots continue to grow. It is exciting to learn from our panelists and see where automated solutions are going in the future.

About AWS Contact Center Intelligence

AWS CCI solutions can quickly and easily add AI and ML to your existing contact center to improve customer satisfaction and reduce costs. AWS CCI covers three key areas of the contact center workflow: self-service automation, real-time analytics with agent assist, and post-call analytics. Each solution is created using a specific combination of AWS AI services, and is available through select AWS Partners. Join the next CCI Webinar, “Banking on Bots”, on May 25, 2021.

About the Author

Arte Merritt leads partnerships for Contact Center Intelligence and Conversational AI. He is a frequent author and speaker in the conversational AI space. He was the co-founder and CEO of the leading analytics platform for conversational interfaces, leading the company to 20,000 customers, 90B messages, and multiple acquisition offers. Previously he founded Motally, a mobile analytics platform he sold to Nokia. Arte has more than 20 years experience in big data analytics. Arte is an MIT alum.