The customer service industry is among the fastest-growing industries in the world right now. As per 2020 research, the customer experience management market was valued at $7.6 billion, expected to grow at a compound annual growth rate of 17.7 per cent between 2020 and 2027. It has also been found that a good customer experience is a great way to enhance a company’s business prospects. It is hardly surprising that companies are pumping huge amounts of resources into this field.

At the turn of the last decade, AI-enabled customer service or chatbots started gaining much traction. But with increasing dependence on this technology, newer challenges have also emerged.

The language barrier

Nelson Mandela once said, “If you talk to a man in a Language he understands, that goes to his head. If you talk to him in his language, that goes to his heart.” An individual’s identity is directly linked to their culture, of which language forms a major part. In the world of increased globalisation, a need has been felt to standardise the communications between two parties to smoothen out all the possible creases that may arise due to language barriers. That said, people feel more comfortable, connected and understood in their own language. 

In this regard, the biggest challenge before companies is to build chatbots that overcome this language barrier and serve their customer the best possible way. Today, most chatbots are designed for specific markets and target audiences, leaving out a major population of people who do not speak the predominant language. The good news is that chatbot developers are moving towards building bots that can switch from one language to another. While the progress is slow (consisting of mainly bi-language translation), we are moving towards better days.

Switching between languages involves more than just exact translation. There is a need to understand the context and specific language differences to provide a properly adapted version of the chatbot. The foundation to be able to achieve such seamless translation lies in how strong the knowledge base is. A strong chatbot program is built on a knowledge base that consists of primary data, facts, assumptions, and the rules of the system available to solve a problem. The chatbot’s ability to connect and interact with the customer is dependent on how well-built and expansive this knowledge base is.

Navigating the platform with chatbot’s help

Imagine accessing a website for a very important piece of information, but instead, you get tangled between the numerous pages while trying to locate the correct content. This problem is a major roadblock and may even negatively influence your customers from visiting your website in the first place. 

One way to mitigate this challenge is by allowing the chatbot to take over the navigation duties and act as a direct portal to the required information. A chatbot can streamline website navigation, remove lengthy drop-downs and the number of clicks needed to locate answers.

An ideal chatbot conversation should be designed in a way to be not too stiff and mechanical – a conversational tone is a way to go. Another key is brevity – the instructions should be short, crisp and clear. The customer need not be overwhelmed with a lot of information, especially if it is not necessary. 

Voice chatbot 

At the NVIDIA GTC 2021 event this year, leaders from companies like Hugging Face, T-Mobile, and RingCentral discussed how deploying speech and language technologies helped them enhance their business and face some of the pressing challenges of customer service.

Many incorrectly define voice bots as chatbots but with additional voice processing capabilities. This is a wrong assumption. To begin with, a text chat is very turn-based; it means that a text query is entered, and the bot on the other side understands the query before responding to it. On the other hand, the parties involved frequently interrupt each other. The concept of ‘speak at your turn’ takes a backseat. A spoken conversation involves exchanging words, non-verbal cues and other external noises of any form.

Companies now have better voice recognition systems in place with a far bigger trove of information to be trained on. However, voice bots used in even the most technically advanced organisations suffer from challenges like contextual understanding. For example, try dictating email ID to a voice bot; it would try to give the tech without really understanding the context.

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