Chatbots are a fast-growing tool for any business to help handle customer service, sales or booking requests. They can also help act as resources for workers within the business or take over mundane tasks, freeing up workers for more productive or creative functions. Reading about how a bot can boost the business is one thing, but when it comes to creating them, many businesses are concerned about having to train their bot, fortunately, this isn’t as complicated as it sounds.
Robots need to learn how to talk
Chatbots continue their relentless drive to broad adoption, appearing on more sites, social media networks and within mobile apps. However, they aren’t the result of some coding magic. Most bots have to learn what the right responses are to customer questions through training.
The good news is script-based bots can be created in a day, and launched instantly. These operate on something similar to a customer services script that most call centres use, and anyone can create. Smarter bots can look at user input and guage intent or better understand questions through training. However, these more complex bots must be trained based on sets of data. Any business will already own plenty of data to use, but it could be stored in unstructured forms, making things a little trickier.
Different types of chatbots handle data differently, simple scripted bots can only offer a limited set of functions or questions, and only recognise a narrow range of responses or user input.
Machine learning allows smarter bots to develop a growing set of knowledge and understanding. This can be done by studying previous examples of chats or watching live conversations in action, or transcripts of phone calls or other data, learning from these to refine answers. To find more about the practical aspects of bot training, this article will help.
Training data can be sourced from a range of areas within a business:
- If your business doesn’t have a stash of data, it can find some in short order. This could be a list of queries or interactions that your reception staff, support channels or staff field on a daily basis.
- Information can also be found in FAQs, manuals, support chat scripts, call logs, email trails and other written resources. If it can be saved as a text file, the bot can learn from it.
How chatbots learn through training
The data is converted into a structured form that a chatbot can learn from, this can be delimited text files, real-time analytics files or other sources that are imported into the bot. The data is analysed by the bot and it reports trends or answers that it can use. Trainers can help it learn semantics, when it misunderstandings something, and personalization by identifying names, user’s sex, age and other information to make a conversation more meaningful.
When using a scripted bot, the designers focus on the most commonly asked questions first, making conversations short and sweet. The bot can be designed to highlight the most common answers, working down a list of useful information by priority. Perhaps signing off with a cheery farewell.
Bots can train themselves using Natural Language Processing (NLP), Natural Language Understanding (NLU) and other Machine Learning skills. These advanced technologies sound technical but are part of most bot services. Feed them data, and the bots can best understand questions,
Machine learning bots, such as those you can build on SnatchBot’s platform, can be fed a set of data to teach them likely scenarios before going live. SnatchBot’s NLP uses what is known as a declarative approach to intent “doing” and entity “things” recognition. Example sentences show the bot what terms are important in the conversation and what users want to achieve.
Once up and running, analytics from the conversations allow bot builders to learn where the bot has difficulties analyzing sentences. Trainers can give them the right answer to ambiguous statements and guide automated machine learning. A ‘supervised machine learning’ approach, allows bot builders to add critical new sentence examples manually.
Benefits of machine learning
Advanced training on some bots includes sentiment analysis where the bot looks at the language used using NLP. It could understand if a user is upset based on their use of language and adopt a suitable tone to help resolve their problem. If someone is confused, the bot can resort to simpler language and so on.
A final set of data can come from customer satisfaction scores at the end of a chat. The usual “rate your experience” option helps the team understand if the chatbot meets customer expectations. If a user is unhappy, analysing the chat can help find out what went wrong.
As bots get smarter, they can expand the range of features they offer, making them of more value to the business by saving time. That frees people up for more important jobs, saving revenue. As AI gets smarter, bots can be left more to their own devices to automatically learn and improve the service they offer.
Even so, any company looking to build or evolve their chatbot service should ensure they test and check through user interactions on a regular basis to ensure it meets the needs of the business and is as simple (and fun, businesses should ensure their bots have some mote of personality) to use for the customer.