AI analysis of reservation notes and turn it into structured data
Hi!
Sometimes we receive a ton of information about the guest or the reservation via reservation notes from OTAs / TAs that are not structured and thus currently cannot be used to enhance the guest profile or reservation with structured data. LLM-AI analysis could potentiall understand the notes and make filter usable information and turn it into structured data.
Example:
Guest preferences: often times "high floor" "bath tub" "balcony" "quite room" "away from elevators" etc. are added to the reservation notes. matching those to the guest preferences available in profiles and space features before assigning a room would allow the system to align room asignment with guest preferences
Arrival Time: sometime the approx. arrival time is indicated in the notes -> turn that into setting the arrival time accordingly
Booking Purpose: set the booking purpose property if the AI can guess the purpose, like for example in case a company invoicing address is found in the notes, or an explicit purpose declaration
Company: if a company name is found in the notes, associate the reservation with a company profile if one allready exists in MEWS
I am sure there are further possibilites....
Jean-Philipp SPIESS
shared this idea
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Jean-Philipp SPIESS
commented
Another possible use of AI: create tasks and notes based on reservation details: for example if occupancy requires extra bed setup or extra/Packages have been requested create corresponding notes and tasks attached to the reservation…
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Sam
commented
Great idea!
Most OTAs are now sending notes, often not important but it means to check for special requests we need to check every single reservation. AI could scan this more efficiently and emove the junk notes we do not need.
Plus currently sometimes notes are in the channel notes section, sometimes in the general notes section, having them in 1 area would be a big help.