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USING MACHINE LEARNINGTO PREDICT HOTELBOOKINGS CANCELLATIONSNuno AntónioITBase / WareGuest
Machine Learning and AI in Travel
Smart Travel Data Summit North America 2018February 27-28 • Ritz Carlton Coconut Grove, Miami
1 “THE PROBLEM”
BOOKINGS
the customer has the right to usethe service in the future at a settled price,with an option to cancel
[Talluri and Van Ryzin 2004]
COMPREHENSIBLE CANCELLATION REASONS
DEAL-SEEKING CUSTOMERS
Honor bookings
Bear opportunity costs of vacant rooms
HOTELS ASSUME THE RISK
RISKOverbooking
Cancellation policies
[Mehrotra & Ruttley, 2006; Smith, Parsa, Bujisic, & van der Rest, 2015; Talluri & Van Ryzin, 2005 ]
STRATEGIES AND TACTICS
Terrible experience for customer
Social reputation damage
Reallocation costs
Loss of immediate and potential future revenue
[Noon & Lee, 2010; Smith, et al., 2015]
OVERBOOKING DOWNSIDES
Reduce the number of bookings
Reduce revenue due to discounts on price
RESTRICTIVE CANCELLATION POLICIES DOWNSIDES
[C.-C. Chen, Schwartz, & Vargas, 2011; Smith, et al., 2015]
Improve forecasts Prevent cancellations
Improve overbooking and
cancellation policies
Reduce uncertainty in management
decisions
REVENUE MANAGEMENT SYSTEM
BOOKING CANCELLATIONPREDICTION MODEL
RESEARCH OBJECTIVE
2 WHAT WE DID
EMPLOYED PMS DATA FROM 8 HOTELS
CA
NC
EL
LA
TIO
N R
AT
IO
OTA’S SHARE
XGBOOST – DECISION TREEBASED MACHINELEARNINGALGORITHM
Children_cleanMarketSegment_catB
ReservedRoomType_catBMarketSegment_lev_x.GroupsStaysInWeekendNights_clean
DistributionChannel_lev_x.DirectStaysInWeekNights_clean
MarketSegment_lev_x.DirectIsRepeatedGuest_lev_x.0BookingChanges_clean
DepositType_lev_x.No.DepositCountry_lev_x.PRT
Country_catBLeadTime_cleanAgent_lev_x.240
0.00 0.05 0.10 0.15 0.20Importance
Features
Cluster 1 2 3
HOTEL R1
Agent_lev_x.287DepositType_lev_x.Non.Refund
ReservedRoomType_catBIsRepeatedGuest_lev_x.0
DepositType_lev_x.No.DepositChildren_clean
MarketSegment_catBBookingChanges_clean
Agent_lev_x.288Adults_clean
StaysInWeekendNights_cleanStaysInWeekNights_clean
Country_catBMarketSegment_lev_x.OTA
LeadTime_clean
0.0 0.1 0.2Importance
Features
Cluster 1 2 3
HOTEL R2
Country_lev_x.DDistributionChannel_catB
ReservedRoomType_lev_x.AChildren_cleanAdults_clean
DistributionChannel_lev_x.DirectIsRepeatedGuest_lev_x.0
StaysInWeekendNights_cleanReservedRoomType_catBStaysInWeekNights_cleanBookingChanges_clean
Country_catBDepositType_lev_x.No.Deposit
Agent_catBLeadTime_clean
0.0 0.1 0.2 0.3Importance
Features
Cluster 1 2
HOTEL R3
DistributionChannel_lev_x.DirectMeal_lev_x.SCAdults_clean
IsRepeatedGuest_lev_x.0StaysInWeekendNights_clean
Children_cleanCountry_lev_x.PRT
Meal_catBAgent_lev_x.760
StaysInWeekNights_cleanDistributionChannel_catBBookingChanges_clean
DepositType_lev_x.No.DepositCountry_catB
LeadTime_clean
0.0 0.1 0.2 0.3Importance
Features
Cluster 1 2 3
HOTEL R4
Meal_lev_x.SCStaysInWeekendNights_clean
ReservedRoomType_catBIsRepeatedGuest_lev_x.0
Country_lev_x.PRTMarketSegment_lev_x.Groups
StaysInWeekNights_cleanMarketSegment_lev_x.Offline.TA.TO
MarketSegment_catBDepositType_lev_x.Non.Refund
BookingChanges_cleanMarketSegment_lev_x.Online.TA
Country_catBLeadTime_clean
DepositType_lev_x.No.Deposit
0.0 0.1 0.2 0.3Importance
Features
Cluster 1 2 3 4
HOTEL C1
StaysInWeekendNights_cleanMarketSegment_catB
Agent_lev_x.5Meal_lev_x.SC
Agent_lev_x.238Agent_lev_x.13
DistributionChannel_lev_x.OnlineReservedRoomType_catB
MarketSegment_lev_x.E.CommerceStaysInWeekNights_clean
DepositType_lev_x.No.DepositAdults_clean
BookingChanges_cleanCountry_catB
LeadTime_clean
0.0 0.1 0.2Importance
Features
Cluster 1 2
HOTEL C2
Country_lev_x.PRTMeal_lev_x.BB
DistributionChannel_lev_x.EmailAdults_clean
ReservedRoomType_catBStaysInWeekendNights_clean
IsRepeatedGuest_lev_x.0DistributionChannel_lev_x.Booking.com
Agent_lev_x.0BookingChanges_clean
StaysInWeekNights_cleanDepositType_lev_x.No.Deposit
DistributionChannel_catBCountry_catB
LeadTime_clean
0.0 0.1 0.2Importance
Features
Cluster 1 2 3
HOTEL C3
DistributionChannel_lev_x.TA.TOMeal_lev_x.SCAgent_lev_x.0
ReservedRoomType_catBAgent_lev_x.5403
ReservedRoomType_lev_x.AIsRepeatedGuest_lev_x.0StaysInWeekNights_clean
StaysInWeekendNights_cleanDepositType_lev_x.No.Deposit
Country_lev_x.PRTAdults_clean
BookingChanges_cleanCountry_catB
LeadTime_clean
0.0 0.1 0.2 0.3 0.4Importance
Features
Cluster 1 2 3
HOTEL C4
FEATURE IMPORTANCE (EXAMPLE H1)
FEA
TU
RE
S
IMPORTANCE
3 TEST IN PRODUCTION ENVIRONMENT
PROTOTYPEDEPLOYMENT
SYSTEMARCHITECTURE
PROTOTYPEMAIN SCREEN
By email, in the language of the customer
Initially offered discounts and free services
Change to asking type of bed, time of arrival, children age, car plate or credit card details
Always offered to answer any questions about the hotel, region or events
HOTELS’ CONTACT WITH CUSTOMERS
With at least a frequency of prediction of 50%
Arrival date in 3 or more days
High ADR or high room revenue
Existence of contact means(e.g. Direct, Booking or Expedia)
Speak a known language (ENG, SPA, POR, GER or FRE)
HOTELS’ CRITERION TO SELECT BOOKINGS TO CONTACT
4 RESULTS
Dataset Accuracy Precision F1Score AUC Sensi. Spec.
H1Train 0.865 0.848 0.741 0.923 0.658 0.951
Test 0.849 0.821 0.702 0.886 0.613 0.945
H2Train 0.870 0.885 0.846 0.944 0.810 0.917
Test 0.856 0.873 0.827 0.928 0.786 0.911
PERFORMANCE METRICS FOR THE 31ST OF AUGUST
Group CanceledNot
Canceled Total%
Canceled Actions%
Actions
H1A 486 1,489 1 975 24.6% - -
B 483 1526 2 009 24.0% 109 5.4%
H2A 1 043 3 060 4 103 25.4% - -
B 1 025 3 086 4 111 24.9% 196 4.8%
A/B TESTING EFFECTIVE CANCELLATION SUMMARY
H1
H2
30%
40%
50%
60%
0% 50% 75% 90% 100%Minimum frequency
Rat
io
CANCELLATIONRATIO BY MINIMUMFREQUENCY (MF)
RA
TIO
MINIMUM FREQUENCY
Action Canc. Not Canc. Total % Canc.
H1No 125 153 278 45.0%
Yes 6 70 76 7.9%
H2No 269 325 594 45.3%
Yes 9 111 120 7.5%
B GROUP CANCELLATION RESULTS SUMMARY (MF>=50%)
Decrease in cancellations in contacted bookings with a MF>=50%
37.8 ppH2
37.1 ppH1
4/15H2
2/12H1
Bookings contacted who canceled on the same or on the following day of the contact
H2H1
Estimated room revenue of prevented cancellations
€ 16 680.97 € 22 144.77
NEXT STEPS5
FUTURE WORK
Incorporate additional data sources
Automation
CREDITS
◦ Presentation template by SlidesCarnival◦ Overbooking image: https://pt.linkedin.com/pulse/o-que-fazer-em-caso-de-
overbooking-rodrigo-azevedo◦ No refund image: http://www.budgetyatri.com/wp-content/uploads/Tips-to-save-hotel-
rent-India-non-refundable.jpg◦ Leaky bucket image: https://2myyi21mszez474t4ur4n573-wpengine.netdna-
ssl.com/wp-content/uploads/2014/10/leaky-bucket1.jpg◦ Machine learning image: https://www.innovecs.com/wp-
content/uploads/2017/03/1040035.jpg◦ Chess board image: http://usportt.com/wp-content/uploads/2017/09/Chess.jpg◦ Robot call center: https://tr1.cbsistatic.com/hub/i/2015/11/23/0c943127-5fea-4702-
878f-cf11fe9e7864/robot-customer-service.jpg◦ Tree ensemble and gradient boosting: adapted from
https://www.kdnuggets.com/2017/10/understanding-machine-learning-algorithms.html
