That moment when you spend millions on chatbot training and making it more intelligent but every time there is a trick question, your bot goes…
A user visits your website with a certain expectation – to find what they are looking for. If you are able to fulfil their request with an intelligent bot then they’ll trust you and return for more. However, having a bad experience such as:
- Hanging on to a hold message
- Struggling to find some information on the FAQ page
- Receiving incorrect/incomplete information from the website bot, etc.
.. is a big turn off. Especially in these new forms of highly intertwined competition levels, your unsatisfied customers will gladly go to a competitor. Therefore, companies must orchestrate an intelligent ecosystem where they will have an advantage in competing for better learning. Further, technology offers a rich source of real-time data and digital platforms facilitate experimentation. Machine learning and autonomous action are giving humans more time to focus on their creative side and imagination. And these shifts will collectively create further unpredictability, which businesses can easily solve with flexible training.
Picking up from where we left off in the previous part of this blog, we’ll discuss 3 more key areas in which chatbots require diligent training. These features are inbuilt in Engati.
Keys To Build More Intelligent Chatbots
Easy training and integrations framework
Chatbots don’t come into existence on their own. We have to create, train, and maintain them throughout, on the basis of sets of data. These sets of data will widely vary from business to business, such as healthcare, banking, automobile, education, travel, hospitality, etc. However, training is imminent and therefore, we can build different types of chatbots to deal with data in different ways. These will, of course, be industry specific.
We can build a scripted bot but that can only offer a limited set of functions or questions. In fact, it will only accept a narrow range of responses. Hence, the process will not be very efficient. So, you must make use of machine learning that will let you develop a bot with a growing set of knowledge and understanding. It will learn on its own by studying previous examples of chats.
To mention a few cases:
- Advanced training: It includes sentiment analysis where the bot looks at the language used using NLP.
- A final set of data: It can come from customer satisfaction scores at the end of each chat. Whether your website visitors and customers are happy/unhappy you will get to know with the satisfaction score towards the end.
- Time: Your bot will become smarter with time as and when it gains more knowledge. The more time your customers spend with your chatbot, the more it learns. It will make mistakes but it will also learn from them with time.
And remember, it’s easy to build a mediocre chatbot . All you have to do is just connect some APIs, write (or copy/paste) some lines of code, and that’s it. The difficulty and high effort begin when you implement a process for training the bot. Give it good data to feed on and train with, and it will work perfectly well.
Creating FAQs manually is tedious and a waste of time. It’s a lot better to train the chatbot that will automatically identify and surface common questions from the conversation history. Further, it will recognise potential variations of those questions to make conversations seamless. Therefore, proper training of your chatbot would mean less work for your team on catering to individual customer queries and allowing them to focus on resolving more complex questions that require hands-on assistance.
Therefore, with the FAQ builder feature on the Engati chatbot platform, you can upload an entire FAQ document and let the bot do the rest. It will identify questions and relate them with relevant answers to make your job simpler.
Also, remember that training a bot isn’t a one-off task but an on-going process. Allow one of your team members to do a regular check to ensure that the customer-chatbot conversations are going as they should.
The best aspect of the e.sense engine is that you require minimal setup data to get started with. Training the models is super easy and real-time. A lot of the aspects here can be customised according to the domain or the particular customer including custom synonyms, contextual handling, as well as intents and entity determination. Also, the core capability is available in multiple languages that makes it a very versatile offering.
For more on chatbot technology, please visit Engati!
Read our blog on How Engati Learns | Part 3