How to Design a Conversational Chatbot
⏱ Reading Time: 9 minutes
Chatbots are a trend in the tech world for quite some time now. We see the use of it in different fields, from customer service to marketing, logistics, eCommerce. They have even become our PA-s like Siri often its, having a voice and making conversation!
Smartphone era, along with the innovation-jumps in the smart-mobile technology, is making easier for brands to engage with their customers. That is why we see companies like Sephora joining Kik, Messenger or WeChat to become closer to their customers on the native networks they are most likely to connect
What is Conversational Chatbot?
In the essence of the word, it is a robot. A robot that enables a machine to simulate human-like conversations. As we said in the intro, more and more companies realize the importance of this technology. Businesses can use a chatbot to automate and improve customer experience – from social media platforms to their apps and websites.
Picture 1. Conversation with a chatbot
How to Design a Successful Conversational Chatbot?
Making a conversational chatbot is not an easy job to do, especially the conversational design part. When designing a conversation, one must understand chatbots artificial brain as well as humans. Copywrite skill is yet another key to the successful design of an exceptional chatbot. What do we mean by that? The designer must possess the vocabulary that both robots and a human can understand perfectly.
Having that in mind, we bring you a few steps that may help your company step up its conversational chatbot game.
Build a Personality
First and foremost, decide on a chatbot “personality”. Chatbots personality will be a reflection of your brand. Will it be serious and strictly business-oriented oriented or casual and funny, it’s up to you. Once decided, it is easy to imagine how a chatbot would respond if it was talking to a real customer.
Many companies still make the same mistake and create chatbots with conversation flow through buttons. Since the goal of a chatbot is to simulate a real-life conversation, that ain’t a way to go. If you want a conversational chatbot with a personality, forget buttons. Conversations that use buttons tend to look a lot like forms, and that’s probably something you don’t want. Ditching buttons give users a sense of freedom while communicating, it also provides the most information from the beginning.
This is often the trickiest part of designing a chatbot. Probably you’ve already faced a situation like this with a chatbot. You asked a simple question and chatbot didn’t understand what you mean.
Related: How to Make Your Chatbot More Human
To avoid this kind of frustration, it is best to use an effective NLP program that uses behavioral data to understand entirely what your customers are saying. While some processing capability can be built-in with AI Markup Language, chatbots can actually be trained over time by collecting customer data. That way, conversational chatbots learn how specific audience speaks, as well as what they want to happen next.
Picture 2. Different chatbot personalities
Create Intent Path
Chatbots that we find on e-commerce websites are often used to get an answer in the shortest amount of time possible, solve an issue, or to give a more insightful description about a product or service. Consequently, customers have a clear intention of why they reach out, so chatbot must be ready to guide them from the beginning to the end, no matter the inquiry.
Your customer data, as we said in the first step, is going to be a valuable source to see your customers’ common behavioral patterns and recognize common issues. Data is essential for this step. If customers have a problem in locating information about your products, then an automated chatbot popup message could be very helpful.
Give context to your conversations
Contextualization enables modification of a reply based on a previous request. It is important to have answers that don’t involve open questions. Those questions demand extensive answers, which will be difficult for chatbots to answer. Chatbots that way will have a hard time to distinguish a response from a new query.
It is rather simple to identify where you need to add contextualization. The first option includes you anticipating the answer with the copy. Another way is to analyze all of the chatbot’s confusions and note the message that is prior to misunderstanding to see if there is any link between them.
Analyze the results
As well as your brand, the conversational chatbot should have some goals, otherwise, you won’t be able to quantify the results. Some of the points that could be analyzed are the number of conversions, the number of issues resolved, or even just an improvement in overall customer satisfaction.
Applied AI’s report says that there are fifteen metrics to review when concluding the effectiveness of a chatbot.
- Number of users – total
- Number of users – active (the ones who open the message)
- Engaged users (people who communicate with a chatbot)
- Number of new users
- Conversation starter messages
- Bot messages ( sent out by the bot during a conversation)
- In messages ( sent by the customer)
- Missed messages (number of times a chatbot was unable to process a message)
- Total conversations
- New conversations
- Rate of how many customers used a chatbot more than once
- Goal completion rates (how many messages were required to meet that goal, how long did it take, etc.)
- Rate of how many times did the interaction fail
- User satisfaction rate
- Virality (has your brand’s recognition or customer base grown as a result)
Picture 3. Analyze the data gathered by a chatbot
The Principles of Conversational AI
Availability: Assistance should be offered on all the important touchpoints where consumers want to interact. Based on the customer’s preferences, a chatbot should be able to determine the best channel for the interaction. Instant response is the biggest benefit of a chatbot and the greatest example of its availability. Instantaneous reaction to customers inquiries is specifically what they need.
Intent-Driven: The ability to understand consumer intent is critical. Intent prediction goes beyond NLP. The platform needs to combine behavioral, transactional and other factors to anticipate intent or rephrase a customers request.
Personalized responses: Conversations are more effective when the message is meaningful and relevant. Especially when it is based on the individual’s preferences and interests. If a customer has a question concerning product information, the conversational chatbot should personalize the response and highlight features that are most likely to be useful. Moreover, the response should adapt to the customer’s writing style.
Know its limits: Some inquiries might require a human agent, thus chatbots should also know when they are failing to deliver, and turn inquiries to a real-life agent if available. In situations like these, a human agent should just continue where the bot left off, without expecting the customer to start over. The chatbot can even remain involved and suggest responses to aid the agent in real-time.
Integrations: Chatbots need to have the possibility to integrate into CRM and other business systems to apply business rules and execute transactions. As they handle sensitive customer data, security is at utmost importance. These solutions strive to achieve enterprise-level of security, scalability, and connectivity.
Conversational Chatbot Best Practices
Naturally, some practices are better than others, in the further text we bring you a shortlist of exceptional conversational chatbots. Some of these even won a Loebner prize, which is given annually for AI-powered programs.
Mitsuku chatbot is one the best chatbots out there. It is also a winner of the Loebner Prize. Mitsuku’s AI is so advanced that you can talk with it for hours without getting bored. It replies to your question in the most humane way and catches your mood with the language you’re using. While other conversational chatbots are made for specific tasks, this one can chat about anything.
Picture 4. Mitsuku
This one is also special in its own way. It is AI-powered conversational chatbot that will create websites. That’s right, you didn’t read it wrong. As it asks questions, it collects data which creates customized templates. However, if you try to divert from a theme, a chatbot will try to lead you to the right path.
Picture 5. Right click
No matter the AI development, it is still fairly hard to find a chatbot that sounds natural. However, Xiaoice, the Chinese conversational chatbot is an exception to that rule. It has fluid and natural speech, which is enabled by text mining. This conversational chatbot even recognizes images and can understand things depending on the context. Xiaoice is one of the most advanced conversational chatbots currently on the market.
Picture 6. Xiaoice
This chatbot was developed by a psychologist from Stanford University, Alison Darcy. It is an advanced conversational chatbot with an important mission, reducing depression. Active listening and giving positive feedback along with encouraging words make this chatbot a huge help to ones that fight with ever-growing depression.
Picture 7. Woebot
Replika is also a great example of a conversational chatbot. It learns through the conversation, and after some time it starts to mimic speech and behavior. Similar to Woebot, it can even help you with your emotional wellness. Replika allows you to name your conversational chatbot whatever you like. Unlike other chatbots, it waits a few moments after you’ve sent a message, this makes Replika even more human-like.
Picture 8. Replika
Problems with conversational chatbots
As companies are examining the uses of conversational chatbots for customer service, they find it tempting to think that they could just take existing FAQs and convert them into the form of a conversational chatbot. In fact, it is required a bit more to develop a conversational chatbot that actually does a good job of serving your customers. FAQs are generalized sets of questions and answers and cannot be implemented into conversational chatbot as such.
As conversational chatbots need to be more human-like, they need to collect user data. This leads us to the next problem regarding this kind of chatbots, privacy. Allowing chatbots to collect your personal information will make the interaction more “human” and understanding, but keep in mind that you’re giving away your personal data.
We’re trusting chatbots more by every day, even though they are still considered as an emerging technology. They take time and effort to develop, so it would be great to see them not going in the wrong direction. Conversational chatbots allow us to communicate with our customers even when we aren’t able to. As we said earlier, their uses are almost infinite, from curing depression, to building a website, all that just by chatting.
Some predictions say that more money will be spent on chatbots than on the apps, by 2021. Having that in mind, it is clear that significant investments will be made, and are already being made, by companies to build engaging chatbot experiences.
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