Customer Relations Automation and AI Chatbots

2017 will be remembered for the emergence (and endless discussions) of chatbots. A lot was talked about, heard about, and understood about the scope and efficacy of chatbots ─ some of these debates made sense while others were not so helpful.

There are a plethora of chatbot tools to make your own bots. However, 2018 is likely to be a defining year when great chatbots will be separated from mediocre ones, ensuring greater clarity. Almost all industry giants are unanimous about their optimism about the ability of these bots to tackle customer interactions deftly.

For instance, Gartner has forecasted that 20% of all brands will relinquish their mobile phone apps by 2019, adding that an average person is expected to initiate more conversations with chatbots than their own spouse by 2020.

Even customers seem to be lapping up to the prospect of interacting with cool automated chatbots to get their queries addressed. According to a recent survey from Aspect Software Research, which encompassed more than 1,000 US-based respondents aged between 18-65 years, nearly 44% pointed out that they would prefer an automated experience for CRM if their overall experience were good.

Automated Customer Interaction ─ A Growing Necessity

The initial days of automation were essentially about scheduling telephone calls for telemarketing in that people would make phone calls to inquire about a product or get their problems addressed without bothering to understand much about what was happening on the other side.

In the current digital era, consumers have become a lot more informed; they are smart enough to figure out the difference between interacting with a human and a bot. This begs the question: is it really worthwhile or feasible to automate a viscerally human domain?

The Context

Now more than ever, brands are realizing the importance of being accessible at all times. Some of them take as long as 24 hours to address a concern, which does not go over well for an industry whose reputation hinges on 24×7 support.

Inevitably, annoyed customers are increasingly contacting community managers via Facebook and Twitter, compelling them to undertake mundane customer service interactions. This is because most customers simply dread the prospect of making a phone call where they are made to wait or feel inadequate. Customer service is an integral component of any organization, which is why it helps to be proactive in your approach.

It is this emptiness that can be filled up by chatbots if the users are made to feel as if they are talking to a close buddy on a chat messenger, without having to wait endlessly on a phone call or download an app.

The Utility of Chatbots in Customer Relations

It is common knowledge that even a simple gesture such as acknowledging the customer’s concern can put them at ease and build a friendly rapport. When a chatbot responds to a user’s message in a warm, personalized manner and specifies a time-frame within which a solution will be presented, it goes a long way in building the elusive trust factor.

However, a chatbot needs to be able to sustain a smooth, intelligent conversation with a user. They must have the ability to compete with humans regarding maintaining the context and purpose of the conversation, which can be tricky given the inherent unpredictability of human conversations that can involve unusual words, expressions, and unexpected questions.

Cognitive Aspect of AI

This represents the reasoning aspect of a chatbot and reflects its ability to anticipate a customer’s requirements. Unlike conventional speech recognition, machines which decipher the meaning of what you say, modern AI-powered natural language systems understand both what you mean to say and what is it that you intend to do. Big data has a major role to play here because it imbues the knowledge about foreseeing the customer’s intent.

Learning to Get Better

The next big challenge for chatbot developers is to get the bots to learn how to do things better ─ better through human assisted-AI. Leaving chatbots to do the guesswork can leave them vulnerable to making serious mistakes.

While AI has come a long way in the context of automation, we are yet to reach the stage where the chatbot can ascertain the extent of a customer’s disillusionment while chatting with them. Unlike voice patterns which are relatively easier to identify, unusual chat patterns pose a serious challenge to the efficacy of bots.

To be successful, chatbots must no longer be mere standalone apps; they should deploy a range of tools that function as a human cognitive brain. Furthermore, bots must include a comprehensive Omni channel strategy that includes platforms such as mobile apps, messaging apps, live chat tool, social media, and the internet. Doing so will empower brands to ensure that they are providing a consistent and seamless experience to their customers.

On their part, brands must think from the viewpoint of potential customers before leveraging a chatbot. By intuitively understanding the responses of users, brands can expand or restrict the scope of their interaction with bots. For example, if the customers are inquiring about weather updates, they would want the interaction to be brief and precise.

Final Thoughts

Taking these factors into consideration will make it easier for businesses to adopt a feasible, AI-approach and make automated customer interaction work for them big time. While you can make your own chatbot you might want to find a chatbot expert to help you make it great.