Humans Hate Each Other But Want Chatbots That Behave Like Humans

Building Human Like Chatbots — The Future Of Chatbot Technology

Image Source — Unsplash

Isn’t it funny how humans despise each other but still want to build human-like chatbots? Maybe we should take a look at it from a different perspective — there’s a growing demand for human support because it’s practically diminishing as we progress (because of greed, self-interest, envy, etc., etc.). Machines are doing a great job at replicating human behaviour, which is basically the need of the hour to stand out and flourish in the industry. Therefore, the world is looking at a future that promises more human like and personalised chatbots. To name a few leaders- Amazon has cracked it with Alexa, iPhone with Siri and Google with Google Home. Others are also competing to achieve customer satisfaction with chatbots. If not then at least they are trying to get there.

However, there are certain challenges as well. It’s been quite some time since industry and technology leaders have been worried about chatbots giving out the wrong information to customers. Or not being able to understand what the customer is asking for. Probably this is one of the many reasons why engineers and developers build machines that can exactly replicate a natural human response. Chatbots, of course, being one of them. But as humans, we make mistakes too. The best we do to mitigate this is by learning from the mistakes and train ourselves better.

Well, guess what? That’s exactly what is being done with the chatbots as well. There are a few areas where developers put their key focus. One-by-one let’s take a look at and accentuate these key focus areas.

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A population of 7.7 billion speaks roughly 6,500 languages. I bet that this huge number justifies the kind of controversies we have over language but I don’t support them, of course. Anyway, the point is that language is and must remain a creator of bond and not controversies. When 2 people on a foreign land meet and discover that they speak the same language, they automatically connect with each other. There is a growing sense of trust, comfort and safety. That’s exactly how businesses are trying to make their customers feel when they land on their website. That’s exactly how and why multilingual chatbots came into the picture.

NLP or Natural Language Processing, a type of artificial intelligence, takes customer input to interpret, recognise and understand the customer requirement before sending out an answer. That’s how a wise person behaves in real life and developers try to imitate this behavioural pattern for enhancing the chatbot technology.

Now there’s a lot of debate going on over NLP versus conversational interfaces. I believe this is one of those cases where you must pick both and not settle for either of the two. While conversational interfaces has a much wider scope to manage, NLP is at a nascent stage but still quite necessary. The average precision rate comes somewhere between 60 to 70%, which is not bad but something tells us that 100 is viable.

In fact, Alexandru Iliescu, CEO and co-founder of ATi Studios, would agree. It is the company that built Mondly, a VR technology that allows users to learn 33 international languages by talking to artificially designed characters. He says,

“Fluency in a language is not how many words you know, but how well you communicate with the words you do.”

So, your chatbot doesn’t have to use any of the big fancy words to engage customers. All it needs to do is to communicate well enough to make them stay and come back.

Rule number 1 of every conversation- pay full attention when the other person is talking. Now you know where the concept of ‘mobile phone in your bag during lecture’ comes from. It is absolutely necessary for all participants to be on the same page if they want common, desired results.

Further, if we want chatbots to become more human-like then we must train them to have a good memory and remember the context of a conversation. No user or customer likes to repeat what they are searching for or what they want from your business. They won’t stay otherwise. They will jump to a competitor chatbot that understands them better. Therefore, we need chatbots that are smart, intelligent and remember the context of a conversation.

Image Source — Unsplash

Say for example a customer asks about a product from a clothing brand and directly asks “What’s the price?” in the next question. So, the bot should be smart enough to understand that the customer is talking about the same product as mentioned in the previous message. We call such a feature Contextual Conversations with chatbots. It’s an easy integration and significantly important at the same time. The feature brings tremendous boost to customer engagement and satisfaction.

Support in itself is a broad term that can be categorised into different segments. There are a number of ways in which a business can provide seamless support to their customers.

Chatbot building platforms

The simple art of being present — be where your customers are. If they are active WhatsApp users then build a WhatsApp business chatbot. If they tweet more than they blink then build a bot on Twitter. Be present where your customers are most likely to engage with other people and businesses. This is the basic beauty of target marketing where you look for the big fish in the pond or focus on the area with fish majority looking for food.

Lead Generation with chatbot

Keep a close look at the active users coming to your website. If the data shows daily interactions then it’s better to capture the user data and see what they were looking for. Timely and effective follow-ups are a great way to convert leads into potential customers. In fact, easy integrations through Zapier or JSON API are available on many chatbot platforms to better facilitate lead gen.

Chatbot Voice Conversations

The millennial generation believes more in DMs than having direct conversations. However, that doesn’t stop voice support from being highly in demand. It’s easier for users to talk to the chatbot and find solutions to their queries and problems rather than typing out the whole issue. It further proves to be useful for the physically disabled. Businesses can build continuous or non continuous voice support depending on their need.

LIVE chat support

Users usually know that the chatbot is just a machine but hysterical users don’t seem to care. In such situations it is always better for the chatbot to have the human takeover the conversation with the Live chat feature.

Not everybody is good at small talk or empathising with how the other person is feeling. So, chatbots come in handy for this purpose because we can train them to do empathise with the users. Sentiment analysis allows a chatbot to identify the underlying intent, language, context, tone of the user, and even jokes or sarcasm in certain cases. In fact, Amazon is working on a technology that intelligently predicts what a customer might order next within a particular time frame through predictive analysis.

Image Source — Unsplash

In some cases, customers begin the conversation with a simple ‘hello’. In other cases, they want to talk about something that is out of the scope of business. Like for example, a customer while talking to the chatbot of a pet store might start talking about a pet that they have. Now such details will differ for different customers. Either way, the chatbot is trained to understand the human personality and maintain consistency even while handling distinct personas.

The process gets better with deeper analysis and it solves 2 of the biggest business concerns-

  1. Customer satisfaction: Based on individual behaviour and personality, the chatbot can predict which type of notifications or alerts to share with which customer. This is how artificial intelligence becomes less artificial and more personal. Customers want this and they like the importance that they receive.
  2. Business cost: As I mentioned previously, Amazon is making use of predictive analysis to a great extent. They are predicting which customer will order which product during which time period and are pre-shipping the products in bulk to region-wise storage areas. This is a much better process to manage dispatch. There is incredible management and coordination involved and the process is highly cost-effective.

The major advantage that engineers have while training a chatbot is that the chatbot, unlike most humans, will listen. It will remember whatever you tell it, it will do whatever you ask and it will improve on its own with time. All thanks to easy chatbot training that lets you train your bot without any downtime. Chatbots pick user queries and voila, are ready to give solutions in no time. Automation keeps the training process fast and efficient so that the chances to make a mistake reduces.

If you look at the image given below you will see the different stages at which a chatbot learns and trains on its own. The learning is based on pre-mentioned rules and supervised machine learning that is covered in Generation 1 and 2. Generation 3 is quite a far-fetched idea for now but who knows. Chatbots might soon learn through unsupervised machine learning in the near future.

Image Source

In the meantime, there are many products that are training their chatbots to learn from the mistakes that they make. That’s how a hybrid imitation and reinforcement learning method helps. The neural network based design of the proposed learning method helps chatbots learn from previous mistakes and retain in the information at a later stage. If the chatbots provides incorrect or insufficient answer then the customer can rate the response. This rating will help developers give better training to the bot. Further, there are cases in which the chatbot response is irrelevant. So, it asks the customer what the response should have been. Accordingly, the chatbot will train itself and prepare for future conversations.

Can’t expect such assiduity from all humans, can we? It is equally challenging for the management because for this, the team has to be trained time and again, given commissions, appreciations, feedback, acknowledgement and yearly appraisals. On the other hand, a chatbot demands no such thing. As the number of interactions increase, the chatbot constantly improves on its own. The plan is to eventually let all humans set free from support and other monotonous tasks.

This way we get to invest more time on ourselves for skill building, development and improvement. We might even want to teach these skills to others and simultaneously learn something new from them. Could be just another way to hate each other less. Just saying.

If you’d like to know more about chatbot technology, please feel free to reach out to Engati!

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