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.
How Machine System works
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.
Available Matching Systems
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%).
How to switch between Matching Systems
By default, interactions use Machine Learning matching system as we believe you’ll use it for most phrases. You can change that for each interaction.
Matching system is assign individually to each phrase in the User Says section.
- Create a new interaction or use an existing one.
- Go to the User Says section.
- Add phrases and keywords to the users says fields and decide matching system for each of them individually using the toggle.
- Click save to keep your changes.
Prioritised matching system
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?
- Interactions with
MLmatching are prioritized.
Keywordssystem find the expected word, the matching score equals 100%.
- ⚠️ When a bot meets two interactions with two different matching systems, Machine Learning has preference even if the matching score is lower than the matching score of Keywords.
Check examples and exceptions to see when the above rules won’t be executed.