It takes more than AI to build a successful chatbot

No human involvement, round the clock access, and prompt replies cover almost all the USPs of a chatbot? Well, not that of an exceptional chatbot for sure. To build a successful chatbot, along with AI, companies need to focus on gathering critical data points so that chatbots handle conversations not just intelligently but appropriately.

One of the most critical resources for organizations to achieve success on their digital transformation journey are chatbots. In addition to providing satisfactory customer service experience, chatbots are offering organizations benefits of cost saving, workflow optimization, and enhanced workforce productivity. While the benefits are many, not all the chatbots are worthy of being called ‘useful’. Or let me put it down like this - Have you ever come across a clueless chatbot that has annoyed you? Have you ever encountered a situation where the chatbot keeps repeating the questions asked before?Or, have you ever interacted with chatbots that don’t even understand your question? Well, we all must have experienced one or all of these atleast once, for sure. Even though chatbots have already taken a superior position in this digital world, the truth about chatbots is that there are still big areas of improvement that need to be fixed.

With the rising enthusiasm for the ongoing trend for messaging platforms, we see organizations across the world making huge investments to step on their digital transformation initiatives. Estimated to cross 1.34 billion US dollars by 2024, it is clear that chatbots indeed have a long way to go. The intensifying chatbot development leaves us with a question to organizations out there - how do you differentiate from your competitors? To gain a competitive advantage, it is mandatory to think exceptional. The traditional way of building chatbots will only result in average chatbots, capable of answering just general questions. To build a successful chatbot, not just technologies, but also other useful constraints have to be taken care of. Let’s now move to explaining what these constraints are.

How to build a successful chatbot?

Before talking about how to build a successful chatbot, let’s us first have a comprehension of the difference between an average chatbot and a successful chatbot.

No doubt, every chatbot is meant to provide information to users, carry out a task that is asked by them, or clarify their doubts. But, there is a huge difference between an average and a successful chatbot. Average chatbots are excellent in performing basic tasks such as, scheduling an appointment, booking a movie ticket, or ordering food from your favorite restaurant. But unfortunately, they can only help you with general and basic tasks. They highly lack in understanding user intent, humor, and sentiments. Queries that contain a tinge of humor or sarcasm can leave chatbots clueless. On the other hand, a successful chatbot has no flaws in it. Along with performing the intended job seamlessly, these chatbots understand the conversation flow, interpret the context, and make decisions based on the user query. With chatbots like these, organizations can not only meet their accuracy and efficiency goals, but also fulfill the hungry demands for enhanced customer service experience and satisfaction, which will help them improve their bottom line.

Now that the line differentiating chatbots with unique ones is clear, let’s move ahead with the criteria and requirements to build a successful chatbot.

The first thing that might pop in anyone’s mind when developing a chatbot is technology, of course. While AI, ML, and NLP do play a significant role in the chatbot development process, other prerequisites decide a chatbot’s success. The power of chatbot to interact with any given situation depends on how well you train the technologies with data. Simply put, chatbots are meant to interpret the provided information, and if they aren’t able to do so, then there is a need for having a closer look on the data that is being fed to train the system.

Data is the key

Feeding relevant information about the brand, the products you sell, the services you offer, client stories, and every vital information about your organization to chatbots is mandatory. Before publicizing your chatbot, organizations should ensure that it can:

• answer all questions that a human asks
• understand human sentiments, words, and intent
• learn and improve each time it interacts with humans

For achieving all of these, technologies are utterly important but they alone cannot power chatbots to make it unique. AI thrives on data and its performance depends on the quality and quantity of data that is fed. This again comes back to discussing the importance of information for chatbot success. Apart from these, let’s bring out more ideas and tips on building a successful chatbot.

Context storage is vital

Customers would never want to interact with chatbots that forget the flow if the conversation breaks in the middle. To avoid such a situation, organizations should essentially personalize conversations such that chatbots never forget the past interactions they had with users. This can be achieved by simply setting up a context storage system at the back-end.

Human-like chatbots

For providing compelling customer experience, chatbots should get rid of only robot-like and formal usage of sentences while having a conversation. Some people might like the tone to be professional, some might want it to be casual, while others might be humorous and sarcastic. Train chatbots such that it uses both formal and informal tone (depending on the person they chat), make some typos (just like humans do), use emoticons (for human-like feel), and be a little humorous (depending on the context).

Response patterns

Situations when a chatbot is unable to comprehend the user intent or provide users with answers, the most common replies users come across are ‘I don’t understand’ or ‘I’m still a baby bot, trying to learn things as I grow’ Answers like these can frustrate customers, leaving a bad impression on brands. Instead, designers building the chatbot can alter the reply to something like - ‘I am not sure of the answer. Should I connect you with the concerned person?’ In addition to these responses, designers should also include feedback questions from chatbots to users about their conversation, which will help organizations to understand and identify the loopholes, and then accordingly act on it.

Until now, technology was considered as the prime reason for chatbot development and also success. But, the myth has been busted now. Along with the potential of incredible, new-age technologies like AI, NLP, ML, smart dialog management, sentiment analysis, voice recognition, and so much more, collecting relevant information is the key to build a successful chatbot. Combining all of these with an appealing and easy-to-use interface will help users to use and interact with chatbots seamlessly. Powered with the required advanced technologies and trained with quality data, chatbots will definitely have the potential to interact as per the keywords or queries entered. Such a well-built, best-in-class, comprehensive chatbot would fetch a good ROI, which is indeed a true meaning of chatbot success.