Users speak the way they choose, not as you wish. Developers are building tens of bots and almost all of them bump into the same problem: they are not properly working. Most of the bots don’t understand users and the problem lies in their training. That’s the simple truth every bot developer must face sooner or later. The question is: are they facing it the right way?
A bot needs tons of training sentences with the same meaning to react efficiently. If you don’t have enough variants and data for the bot, it won’t understand every user unless they also talk like robots. Let’s say that someone wants to buy a train ticket from Barcelona to Madrid. The bot must know all kind of sentence variations as, for example: ‘I want a ticket to Madrid…. I am in Barcelona and want to travel to Madrid… Train pass to Madrid from Barcelona…’. Until now, all these variants were manually made by humans – what really takes ages and much effort of many people.
Generating all the data needed for bots to understand thousands of queries is a hard but rewarding task after all. If users feel they are not understood, they will end up turning to usual contact forms or calling call centers to be assisted by a human agent. Those sales agents will invest their time in answering routine questions instead of doing more productive tasks like direct sales or research, for instance. As a result, the customer experience will be awful too since users will get frustrated by the simple fact of not being understood right away. How to solve this issue in three steps? Keep reading.
Make Your Bot More Conversational
Everything has a life cycle, even bots. The life journey of a bot is split into three phases that can be defined as:
- Bot Creation. This first phase begins when you start a bot from scratch by generating intents and training data. In Bitext, we have a tool that helps generate sentence variations automatically. This is a system that can be told ‘turn on the lights in the kitchen’ and it will, on its own, generate hundreds of variants for that sentence, namely, different ways of saying: ‘turn on the lights in the kitchen’.
- Bot Deployment. After creating a bot, it’s time to check if it works and what could be improved. What our technology does here is introducing a query simplification tool in the ‘release’ phase. This tool takes user queries, gets the core meaning and makes that query simpler for the bot.
- Bot Analytics. Once a bot is released, it would be great to get insights from user-generated queries to improve customer experience. Thanks to the Bitext analytics tool, you will be able to compile statistics and have some clear ideas as to how to go about it. This is a topic extraction tool that examines all queries and identifies what the trending topics are. What’s more, there is a sentiment analysis tool too to check if users are complaining or satisfied…
This is an easy-to-implement solution that can be integrated either through an API or on-premise. It can be smoothly adapted to any platform, format, and language with the click of a button. Indeed, one of the most significant features is the fact that the bot will be working within a few days, instead of months. Want to know more detail?