Customer Service Chatbots in 2021: 3 Myths to Dispel

There’s something about the idea of talking to a machine that has captured the imagination since the first such software was trialled in the 1960’s. In 2020, 2 out of 3 online customers engaged with some form of bot and this shows no sign of slowing down. However, when customer care managers think of chatbots, they often get it wrong. Instead of creating a solution that works for their team and their customers, they get hung up on decisions that are inherently superficial such as what the bot’s name should be. They should rather be focusing on the nuts and bolts of what it’s designed to do.  

The History   

To understand the power of chatbots, it’s worth briefly revisiting how they were developed. Some of the myths I’ll discuss have a direct link back to how the technology evolved and, without this context, it can be easy to repeat previous mistakes. 

The first chatbot is widely accredited to Joseph Weizenbaum in 1966, meaning they’re already over 50 years old. Named Eliza, this bot worked in a remarkably similar way to much of today’s technology. It used a form of Natural Language Processing (NLP) to take words entered by a user and associate them with a relevant, scripted response. The most basic of today’s bots work in exactly the same way, with the main change being the medium of entry (often social customer service) for the customer. 

Following the development of iterative technologies – including Parry and Jabberwacky – a big milestone came in 2001 with the development of SmarterChild. For people of a certain (read: my) generation, SmarterChild was our first interaction with bot technology. An instant–messenger–based bot (available via AOL, MSN Messenger, and other platforms), SmarterChild was a contact method to ask questions and request data from a system which – at that time – was relatively rare at a mass scale. 

SmarterChild worked in a very similar way to Eliza back in the 60’s. Users would ask questions which were processed in the form of keywords, and SmarterChild would return a response. One of the features of modern NLP is the ability to learn; as bots answer more questions, they can understand more meanings/contexts from the user’s language. Using this framework, SmarterChild was able to improve its accuracy throughout its lifespan. Although, all too frequently, it struggled to provide a valid response. 

In many ways, SmarterChild can be seen as the direct predecessor of many of today’s leading bot networks including SIRI, Alexa, Google Assistant, and more.   

The Myths   

From SmarterChild in the early 2000’s, it’s now time to jump forward to the present day and the way bots are used in digital customer service. Firstly, an important point: when used right, bots are a key part of providing a high–class customer service solution. The following three myths are a guide to focusing in the right areas, not ignoring bots altogether. 

Myth 1: Bots are an Instant Messenger Solution 

The first myth is that bots exist purely in an instant messenger environment. Too often, the assumption is that a chatbot is a piece of technology that lives on a webchat or messenger channel, rather than a holistic response tool.    

As recently as 2019, over half of customers surveyed had used email to contact a brand. With the amount of email contacts still received on a daily basis, this is a prime channel for bot–style automation. Indeed, with the inevitably higher contacts per resolution rate (as people will generally need to send a second email with more information to resolve a query), this channel almost makes more sense than instant messaging for a bot-based solution. 

  The reality is that good communication tools already offer NLP–enabled automated email responses that will answer relevant queries without them ever having to touch a human advisor, but this is often underused by brands. From the basic gathering of data protection information to answering routine stock or tracking queries, non-live communication such as email should form a huge part of any bot-based strategy. 

Myth 2: Bots Need a Personality 

Possibly the biggest misnomer in customer support is the name itself: “chatbot”. The idea that a bot needs a personality is pervasive in the age of Siri and Alexa, but the reality is no customer wants to converse with a bot for anything other than a transactional query.  

Customers understand and accept the utilisation of bots (indeed an Uberall study found 80% of customers had a positive experience with bots), but we shouldn’t mistake it for social interaction. 

However, it’s all too easy to get bogged down in trying to present the system as an alternative to human agents. This could be through the language used or the interface (such as adding a human image). Whilst these aspects matter, they should come second to the customer need you’re trying to accommodate. After all, the underlying purpose of a bot in customer service is to transmit information to the customer as quickly and as accurately as possible. 

  A good bot system will instead be clear that the user is engaged with software. Whilst the ethics of AI and automation are up for continued debate, your bot doesn’t need to be the battleground. 69% of customers still put speed at the top of their judgement criteria when assessing a brand’s response, so making sure your bots are firing relevant information as quickly and accurately as possible is the name of the game.   

Myth 3: Bots are the Entire Solution 

As a SaaS business whose automation is the key to our product, this may seem counterintuitive but here it goes: humans continue to play a major role in answering customer service questions. From the empathy a human agent displays through to the need to occasionally break process and think outside the box, a fully automated system is not the panacea some think it is.   

Customer care is made up of relationships. In digital customer service these relationships will be between your bots, your human agents, and your customers. Ensuring all three parts of this web are maintained is key to your continued success. A speedy information transfer is important, but so is the ability to offer meaningful interactions. However tempting it can be to view your customer communication as a cost-cutting exercise, that is short–term thinking. A blend of human and system is key. 

Bots continue to develop at pace, but the fact remains that there will still be responses when they cannot provide an answer. Astoundingly, roughly 15% of Google searches are still brand new, meaning the NLP behind your bot is still learning at a huge pace. Whilst offering a self-serve knowledge base can feel like a suitable alternative, it’s a poor cousin to a human agent able to take the transferred contact and resolve the query. When building your automated infrastructure, don’t forget that human touch

Bots are great. They’re continuing to revolutionise digital customer service and the percentage of contacts they handle is likely to increase over the coming years. However, without taking the time to understand your system and the customer pain points, it can be all too easy to facilitate unexpected problems to the detriment of your customer experience. I hope the myths above help you start asking the questions that lead to a better solution

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