The Benefits of Voice Technology in Reducing Customer Frustrations

Andrei Papancea's Guest Post In The MarTech Series

Andrei Papancea

06/09/2021
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One question posed by the pandemic is how business can efficiently and effectively deliver outstanding customer service at scale. The does not mean removing humans from the equation but harnessing the power of voice technology to augment the services that people provide for greater customer service efficiency. It means using voice technology to properly understand the customer service issues your business is addressing and then leveraging the technology to drive efficiency through an organization for the benefit of its end users.

But how can voice AI technology enhance the customer experience, improve the efficiency of the selling organisation and, most importantly, how can brands leverage this kind of technology to reinforce brand loyalty and secure repeat custom moving forward?

The Known Problem

Research by PWC has shown that 73% of US consumers rated customer experience as very important factor in their buying decisions1. So critical is this, that even if they love a particular company or product, 59% say they would still find an alternative if they were subjected to several bad experiences. As many as 17% say they would walk away after just a single bad experience. So, clearly, in a post-pandemic world, customer experience is likely to become an only more important factor for consumers as they decide which businesses or brands to support.

From a customer service standpoint, holistically rather than for individual vertical sectors, there has always been evidence of customers hanging up before their enquiry has been brought to a satisfactory conclusion, meaning the need for operational efficiency within that sector of the market has always been there. The difference is that now the technology is available that can actually drive value.

Solving Old Problems New Ways

The pain points we hear about now when it comes to customer service are not new pain points. But when you ask companies what their solution is, many have been looking at only slightly savvier variants of what they have already had in place. But Voice AI is a totally different solution. It draws on cloud processing power and advances in machine learning, with the ability to leverage data to identify and understand the top use cases that can are so these can be addressed. Leveraging modern technological advances to solve these old problems makes a lot of sense, but the difference is the new capability to do so in a very user centric way. We are thinking about the user experience, the end user. People respond really well to hearing support, and especially the empathy we build in to the voice narrative.

It’s important to recognise that there are pain points on both sides of the conversation. There are the pain points of the end user – “I need service now”, and “I need my issue resolved”. Accessibility can be an issue here because end users don’t necessarily know where to go to get support; they have to wait on hold for a while or experience incomplete support that may be unable to resolve their issue.

There are other pain points too:

  • As an end user, timely access to support is important – everyone has experienced the frustration of having to wait for a while to get support, but an automated system that doesn’t understand what you’re saying and cannot get you to a human is an equally negative experience.
  • Misuse of technology – trying to fit everything through chatbots, or through voice assistants, without actually appreciating what customer experience transformation the technology can bring. The technology is meant to solve a problem or to be part of the solution. Too many organizations walk backwards from the technology and just try to fix the problems through it. Implicitly this just amplifies an existing poor customer experience.
  • Cost. Frequently, the nature of existing automated solutions means that there remains a requirement to invest in both human capital and ongoing training and retraining. Here, the pandemic exposed significant operational inefficiencies – because, for example, how can you train people effectively at scale when you can’t get them in one place?
  • Operational scale. For many, providing the infrastructure for effective customer service is a necessary evil. No company really wants to spend money on call center operations. They pay for it because they have to, and that, again, can have a poor impact on the user experience. It’s a little like insurance. It’s a distress purchase – the item nobody wants to buy. This risks additional pain points for the end user, because the entity that’s looking to provide them with the service doesn’t really want to provide them that service in the first place.

So, how aware are companies that the inefficiencies or the failings of their existing customer service operation are having a negative impact on consumer perception of their brand, their organisation?

The truth is: some are, some are not. The emergence of services like Amazon Connect and other cloud-based call center services has transformed the sector by enabling organizations to only buy the technology they need and reduce their infrastructure overheads. By facilitating global distribution, it has removed the need for centers to be contained within single buildings and has facilitated efficiency and effective home working from anywhere in the world. However, many traditional contact center organizations haven’t necessarily kept up to speed with new technology and new practices. Some are even afraid of new technology because they view it as having the potential to displace them. The reality, though, is that the power of this technology is to augment people and help make humans more efficient. It returns operators attention to the things that they do best, which is using their cognitive power to deal with complex problems.

New Challenges & New Solutions

Many companies are failing to analyse their incoming data; they either don’t have it annotated, or they don’t have the right tools or skill sets to make sense of all the traffic that comes in. So instead, they end up being very reactive. For example, they get an influx of calls and divert people to answer the calls. But they fail to take a step back to understand why people are calling in and what they are calling about. They don’t look at different potential resolution options for different calls or chats, or how those conversations could be used to drive automation. Unless you understand what your customers are calling you about and have concrete data points that support it, you’re not going to be able to automate. It’s putting the cart before the horse.

Scale can also be an issue. Imagine you’re at an airport, you can visualise the people waiting in line. If you’re at the customer service desk, you will know what type of questions are going to be asked again and again. But imagine it’s 10,000 people a month in that line. How do you identify and filter all the information that’s coming in? You don’t see the people angry on the phone, or at the chatbot. How do you tag that data? How do you determine the patterns in these conversations? What are the indicators for an escalation and how can this create give a great user experience?

We are already seeing the rise of self-service activities – tag your own baggage, for example – so you can automate a line of 50 people waiting to get their bags checked. We need to be optimising these systems and creating better efficiencies.

There are already numerous examples of self-service activities that are being effectively delivered through voice AI technology using Smart IVR and multi-modal formats from I’m trying to replace my card to I’m trying to book a room or I’m having trouble accessing my account. However, there remain some calls that create frustration among callers and which require escalation to a human for resolution and an automated pathway for this escalation to occur is best for both parties. We have tried to make our technology as comprehensive as possible for use cases across the board: from AI chatbots to smart home assistants, and from smart IVR applications to multi-modal experiences, we are aiming to both deliver and optimize almost any conversational experience. As such, there are various solutions in our voice technology that allows us to annotate and analyze calls as they go to a live agent. We can then extract as much context from that voice interaction or the multimodal interaction, including the event leading to a prospective escalation to a human agent. Prospective is a key word here because there are lots of situations where the technology can effectively automate end to end. The customer gets resolution without ever coming into contact with a human. That’s the ideal scenario.

But in the case of calls where you do have to take someone to a human agent, it’s important to do so with as much context as possible, because that then makes the call between the end user and the human agent more efficient. It just gives companies better insight into what people are calling about and, after a friendly greeting, the agent can immediately get into call resolution at a very granular level – using the structured annotated data that flows directly into the contact center. So, the more we can capture that context and encode it in the support tickets that are escalated to the human agent, the more we are providing valuable information for companies as they look to make their contact centers more efficient.

Unquestionably, there will be tasks out there that will continue to require human assistance. But we can use Voice AI technology and analytics and all the other capabilities that we have in our ecosystem to add value to that human agent.

The Future

In the next two to three years, we are going to see a maturing of applications. There will be innovations and advancements in technology. The current state of the art is already great; it’s the applications that are lacking. That’s what we’re working on now, pushing the dial on our Voice Compass™ application that enabled breaking the pattern of what companies are used to, and giving new capabilities for giving end user more satisfying and engaging self-service. You can have a chat experience, or visit a website, or have a voice experience. But why not have the voice experience within the visual experience and make it interactive so it basically takes the end user to task completion, as if a human were sitting right behind them and guiding them through? That’s where the innovation is happening. We are going to see a lot more of that in the next two to three years as more people learn how to use the technology and understand it and understand what it can and should do to deflect call center traffic without compromising customer satisfaction.

The technology isn’t going to solve all of the world’s problems. But that doesn’t mean that technology doesn’t have amazing potential and high prospective value. It’s just, you need the imagination to adopt it and to select the right tool for the right job.

Originally published in the MarTech Series.

Andrei Papancea

Andrei is our CEO and swiss-army knife for all things natural language-related.

He built the Natural Language Understanding platform for American Express, processing millions of conversations across AmEx’s main servicing channels.

As Director of Engineering, he deployed AWS across the business units of Argo Group, a publicly traded US company, and successfully passed the implementation through a technical audit (30+ AWS accounts managed).

He teaches graduate lectures on Cloud Computing and Big Data at Columbia University.

He holds a M.S. in Computer Science from Columbia University.