Chatbots are fast becoming a new pillar in Customer Experience. But as you may have experienced yourself, not all chatbots meet requirements. And what separates a good chatbot from a bad one is spelled t.e.c.h.n.o.l.o.g.y. In this article, I will take you through the three technologies you need to create: The. Best. Chatbot. 😏

In only a few years, chatbots have gone from being a novelty to becoming an essential part of the customer experience online. They are easy to implement and can be used for everything from customer service to conversion optimization and even lead generation. But far from all chatbots are intelligent.

Have you ever had a frustrating conversation with a chatbot that ended with the line:

“Sorry, I didn’t quite get that" ?

I would be really surprised if your answer is no. Because even if the quality on chatbots overall has improved over the years, there are still chatbots that are – to be frank – quite dumb.

But why is that?

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What separates a smart chatbot from a dumb one

As stated in the intro of this article, what distinguishes a smart chatbot from a dumb one is technology.

“No shit, Sherlock '' you may think. Like, aren't chatbots kind of the definition of technology?

Let me explain:

When I say it all comes down to technology. I'm not only referring to the underlying technologies. To create a powerful chatbot there are a few supporting technologies that you should consider adding.

Let's have a look at 3 of them:

Related: What is a chatbot?

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Artificial Intelligence (AI) & Machine Learning (ML)

The underlying technology of all chatbots is Artificial Intelligence (AI) and Machine Learning (ML).

When I say AI, I’m not talking about robots taking over Earth — rather, I’m talking about computers that are capable of performing tasks that normally require human intelligence. In short, AI and machine learning provide machines the ability to learn from data and past experiences to identify patterns and make predictions with minimal human intervention. This is, as you could imagine, a very convenient technology for a chatbot since its main purpose is to simulate human conversations.

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Natural language processing (NLP)

A subset of AI that is one of the most important technologies to use if you want to build a powerful chatbot is Natural Language Processing (NLP).

NLP allows computers to understand and respond to human language as it's spoken or written. By using NLP you’ll make the chatbot more engaging as well as more effective.

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Understand intent

NLP helps your chatbot understand what users want when they interact with it, meaning it won't waste time trying to decipher their message before responding. For example, if someone asks for help with a technical issue, their request might sound like this: "My printer is jammed. How do I fix it?" Your chatbot will recognize this as an inquiry about printer problems and respond appropriately, without having to parse every word and sentence for meaning first.

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Enable personalized responses

Once your chatbot understands what users want from it, it can tailor its responses accordingly. This means providing personalized answers for every customer who interacts with the chatbot. This is much more effective than using predetermined responses as well as it is a better experience for the user.

Related: How Ebbot interprets human language

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Sentiment analysis

The last supporting technology I want to promote to level up your chatbot game is sentiment analysis.

Sentiment analysis (also known as opinion mining or emotion AI) is a technique driven by AI for processing text data to classify the different emotions (positive, neutral, or negative) that text reflects. When it comes to chatbots this technology is used to help them understand the mood of your customers and respond accordingly.

For example, if you have a customer service chatbot, you can use sentiment analysis to determine if your customer is happy with the service they received or not. If they are happy, then you can thank them for their business and offer them something extra as a reward for being so loyal to your brand. If they are unhappy, then you can use sentiment analysis to understand why and see if there’s anything that can be done about it so that future interactions are more satisfactory for all parties involved. For example, maybe someone accidentally ended up with the wrong product, or perhaps there was an error in delivery timescales which caused problems for both parties involved in this transaction.

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Wrapping up

I know that many still have a negative attitude towards chatbots. But with this article, I hope you’ve learned that there is a difference between chatbots and chatbots. And if you are in the running of starting your own bot project, I hope you'll keep in mind to include these three technologies.

But if you rather prefer a bot specialist to help you build the best chatbot (😏), you are more than welcome to talk to us. At Ebbot, we have a lot of experience setting up chatbots and are specialists in customer experience and creating intearactive customer journeys. Feel free to reach out for a free demo or consultation.

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