How to make my chatbot smarter? 4 Ways for businesses to train up your bot

Do you know that 56% of your consumers prefer to speak with chatbots to interact with companies in a more direct, efficient and quicker way?   Reference

When messaging becomes part of life in the post-pandemic era, businesses start developing a chatbot to create 24 hours, always on virtual assistants. However, a simply built chatbot may not be enough to handle all your customer enquiries. You may soon find that your chatbot is not “smart” enough to understand every conversation with customers. To make an enterprise-level chatbot, we need to identify the key problems that chatbots usually encounter and provide the best customer experience that your customers are looking for.

Key signs that your chatbot needs to elevate its experience:

1. • Give out repetitive pre-set template message

   • Difficulty in context interpretation

   • Inability to resolve complex / personalized questions

   • Unable to answer follow-up questions

   • Not building enough data to train intelligent chatbots

The logic of a chatbot is it provides an automated pre-set response through keyword matching. When a chatbot receives an enquiry, it will scan through the sentence and find out if there are any matched keywords in the chatbot tree.

A basic chatbot is simply built according to this logic. It could be confusing as the same keywords can mean differently in different contexts. It could have difficulty in answering complex sentence structures and personalized conversation. Besides, it may result in difficulty in processing the conversation and giving a proper response. In some scenarios, customers may be unable to find a satisfactory answer to their questions as chatbot keeps repeating the same fail-safe response “sorry, I don’t understand” message if chatbot gets confused or fails to understand the questions

Ultimate Guide to train up your bot:

So, with all the limitations, what can we do to train up the bot? Machine learning, big data, and natural language processing (NLP) can help chatbots to better interpret human language, which will return a more appropriate response and sound more intelligent.

1. Set up FAQ database

A FAQ database is the source of questions that answer templates suitable for your business. Companies can list out the top 10 – 20 most commonly asked questions, draft a template message to answer them and turn your customers’ generic enquiries into a datasource. It can be any questions, for example basic company information like “Where is your store?”  Or product centric questions like. “What is your best seller?” “How to use [product A]?”

The standard reply to certain keywords will become a FAQ database so when a user asks a question to your chatbot, the FAQ module will scan the entire data source to see if there is an exact match in the keyword group(s). Once matched with an FAQ entry, the chatbot will display the answer directly. You can also add the diversion feature to allow your FAQ chatbot to display multiple FAQ entries (i.e. questions) if numerous keyword groups share the same keyword in the data source.

You can upload this data source to our chatbot builder and apply it across channels while constantly updating your datasource to tailor your audience’s needs.

You can read this blog post to learn how to choose the suitable platform to place your chatbot– Why omnichannel may not be the best solution for your company and how to adapt chatbot to the right platforms.

From the above demo, we can see that the “WhatsApp” keyword triggered a list of potential questions in the FAQ database, and users can choose the most suitable item to ensure chatbot does not misinterpret their intention. Hence, improve the accuracy of the chatbot response.

2. Leverage NLP engine to for complex sentences and human-like response

Companies need to invest a lot of time in training chatbots manually for each potential conversation. You can improve your chatbot’s intelligence by integrating with existing natural language processing engines like Google DialogueFlow, Microsoft Luis. In this way, when a user types a sentence without matching any keywords in the FAQ data source, the system will be able to send the user input to the NLP engine for analysis and finally, return the correct intent to chatbot for sending the corresponding response.

Sanuker provides professional consultation in chatbot design, contact us here to learn how to build a smart chatbot for your business.

3. Invite your human agent to help with complex enquiries

No matter how well you build a chatbot, it is undeniable that chatbots take time to learn and get better over time. Therefore, you should train the chatbot templates and functions regularly. Most importantly, it is helpful to always have a live agent team to support chatbots in necessary cases.

Sanuker’s Teams Inbox solution enables a perfect tool for you to organize and keep track of customers’ enquiries in all channels, learn more about the solution.

4. Satisfaction survey to collect feedback from customers

Customers are the heart of your business and so are your chatbot. You can add a satisfaction score survey to the end of each conversation, allowing website visitors or customers to put a satisfaction score at the end of the conversation. Companies can better understand chatbots’ sensitivity to human language and let customer service team members conduct regular checks to ensure conversations between customer and chatbots continue to evolve.

Hope the above information can help you. Don’t worry! Sanuker creates a chatbot with your target customers’ unique requirements and desires in mind. You can schedule a free consultation with our team now, and we’ll be pleased to help you sketch out use cases for conversational AI to help you automate your operations!