Every time your client chats with your bot, the system carefully analyses all of the input to match it with the right interaction. You can modify and change the matching system to provide your clients fluent and better user experience.
Matching systems are responsible for pairing user input with User Says field. The system weighs both values and gives the score. If the score is equal or higher than the setup Confidence Score, the bot response is triggered so choosing the right matching systems can be crucial for the seamless conversation flow.
To give you better control over your chatbots, we have introduced two matching systems, Machine Learning, and Keywords.
Machine Learning uses Natural Language Processing and Algorithmic probability. The system reads the full user input and carefully analyses it. The matching strength depends on the confidence score user setup. ML is the default matching system and it’s automatically enabled.
Keywords search for the defined word in the user input. When the system finds the keyword, the matching score is equal to 1 (100%).
The ChatBot’s matching system always selects the interaction with the higher matching score. But what about scenarios when the bot must decide between interactions that are ruled by different matching systems?
User intents with ML matching are prioritized.
When Keywords system find the expected word, the matching score equals 100%.
Check examples and exceptions to see when the above rules won’t be executed.