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Fabrice Otaño – Chief Data Officer
2016, November 23rd
DATA, ANALYTICS& REVENUE MANAGEMENTSmart travel data - Amsterdam
Titre du slide en GeorgiaSOUS-TITRE EN TREBUCHET COLOREM IPSUM
02/12/2016ACCORHOTELS | Titre de la présentation 2
ACCORHOTELS BRANDS PORTFOLIO
D I G I T A L S E R V I C E ST O H O T E L I E R S
P R I V A T E C O N C I E R G ES E R V I C E S
L O Y A L T Y P R O G R A M M E
D I S T R I B U T I O N
ECO
NO
MY
LUX
UR
Y
Data & RM is positioned at CEO deputy level
CEO Deputy
DATA &
A think / build / Run approach…
5
Revenue Management2016 KEY FIGURES
1,000 2,000 25+ 8 Bn€
REVENUE MANAGERS WORKING
FOR HOTELS
HOTELS WITH A
RM SOLUTION
GLOBAL & REGIONAL
RM SUPPORT LOCATIONS
HOTEL REVENUE
OPTIMIZED BY REVENUE MANAGERS
ACCORHOTELS | Performance Capabilities for Luxe Division
Data & RMVISION & MISSIONS @ ACCORHOTELS
FUEL UP NET REVENUE
BE MORE PREDICTIVE
ENLARGE DATA CAPABILITIES
BECOME A DATA DRIVEN
COMPANY
&
GROW TOTAL CUSTOMER LIFETIME
VALUE
02/12/20167
Enlarge data capabilitiesTHE UNIVERSE of ACCORHOTELS DATA SOURCES
02/12/20168
Enlarge data capabilitiesDATALAKE – LEVELS AND ACCESS
02/12/2016ACCORHOTELS | Performance Capabilities for Luxe Division9
360°PERFORMANCE
WITH DATA SCIENCE
MAXIMISE INTEGRATED MARKETING
IMPACT
ELEVATE SALES & RM
PERFORMANCE
INCREASE OPERATIONAL EFFICIENCY
OPTIMISE CUSTOMER JOURNEY & LIFECYCLE
Be More Predictive3600 PERFORMANCE WITH DATA SCIENCE & ALGORITHMS
• Predictive Modelling for Campaigns
• Optimizing Campaign Performance
• LCAH Campaign Analysis
• Pricing Power• Channel Optimisation -
Drive Direct Biz• Forecast Excellence –
impact commercial actions
• Forecasting Market Demand• Guest Sentiment Analysis• Understand Satisfaction Drivers
• Customer Lifetime Score• Anticipate Guest
Preference• Upsell Recommendations
RM DRIVEN FORECAST
RM DRIVEN FORECASTS
02/12/201610
DATA & ADVANCED ANALYTICS ENABLERS
Be More PredictiveTHE CUSTOMER JOURNEY - UNDERPINNED BY A CONSISTENT RM & DATA
FOCUS TO DRIVE CUSTOMER LIFETIME VALUE
Acquisition Investment Optimization
Mobile and site Personalization
Pricing and channel management
Point of interests analysis
Customer satisfaction drivers
Social Network Dialog
1to1 marketingMarketing Optimization
Enlarge Data CapabilitiesHOSPITALYTICS … put INSIGHT into ACTIONS
… AND STEER OUR REVENUE
DEVELOP INSIGHT FROM DATA
ACTIONS
HOSPITALYTI
CS
MEASURES
• RM based Forecast• Customer Knowledge• Web Analytics• Channel Management
• Net TRevPar• Topline Indicators• Promotion & Pricing Uplift• Campaign ROI
• Pricing and Channel Recommendations
• CRM• Sales Automation • Programmatic Acquisition
Optimization
… TO ACTION DIGITAL CAPABILITIES
Examples of Big Data projects creating value
2 USE CASES
Customer Lifetime Value
• The longer a company can retain a customer, the more
profitable that customer becomes
Loyal customers have a customer value
x 2.5TIMES HIGHER
Net RevParOptimizer
• AccorHotels dynamic Occupancy/Pricing/Channel
optimization
Outperforming other hotels
by 3 to 5% in RevPAR
Use Case #1Customer Lifetime Value
Source Format Utilisation Nb lignes Nb ColonnesCosts Allegiance XLS calcul des coûts à affecter aux clients et réservations 100 10Costs Marketing XLS calcul des coûts à affecter aux clients et réservations 100 10Costs Profid XLS calcul des coûts à affecter aux clients et réservations 500 10Costs Brésil CC XLS calcul des coûts à affecter aux clients et réservations 100 10Costs Distrib Sales XLS calcul des coûts à affecter aux clients et réservations 3 000 20Costs Other XLS calcul des coûts à affecter aux clients et réservations 5 1Fees XLS calcul des Fees à affecter aux clients et réservations 3 000 100Commissions OLTA XLS calcul des coûts commission à affecter aux clients et réservations 100 5Clients Pyramide XLS calcul du nombre de clients total vs connus 10 10BDD CLIENTS CONNUS txt base de calcul CLV 50 000 000 10MEMBRES LE CLUB AH txt base de calcul CLV 650 000 000 10SEJOURS LE CLUB AH txt base de calcul CLV 50 000 000 10RESERVATIONS CLIENTS CONNUS txt base de calcul CLV 75 000 000 40RESERVATIONS TARS txt allocation des coûts et des fees 50 000 40ACTIVITE HOTELS txt allocation des coûts et des fees 10 000 40EMAILING txt analyse de la CLV autour du comportement emailing 1 050 000 000 20OPT IN OUT ENEWS txt analyse de la CLV autour du comportement emailing 55 000 000 5EARN & BURN LCAH txt analyse de la CLV autour du comportement Fid 40 000 000 10
Volumes of data & calculations
CLV per RFM segment
THE VALUE EXPLOSES WITH THE # OF STAYS…SO WHAT?
X13
72% 16% 5% 4% 2% 1%
47€
avg=9€
Maxiheavy
customer
worth13x
theaverage
Very High Cluster Heat MapK-MEANS CLUSTERING WITH DATAIKU
Life Time ValueSURVIVAL SCORE X RECENT VALUE
Use Case #2Net RevPar Optimizer
Focus on Revenue Management Lab
F&B revenue enhancement
DynamicPricing
Channel management
Personalized offers
Predictive forecasts
Data-Driven & AutomatizedRecommendations
Availability, Pricing, ChannelAnd customer valueOptimized together
User Friendly Interface to supervise Recos from GMs and
RMS
Performance KPIs
MaximizeOccupation Rate at the best price
for our best customersthrough the cheapest channel
How maximize the net contribution at hotel level ?
Maximise Net Contribution for HotelsUSING A RECOMMANDATION ENGINE
3…AN EXPERT CAN CORRECT (IN CASE HE HAS AN
INFORMATION)…
1COLLECT DATA FROM EVERYWHERE…
2…TO NOURRISH
AlGORITHMS SENDING A COMPLETE HOTEL
STRATEGY…
Net RevPar OptimizerLOGICAL STRUCTURE
4BEFORE IMPLEMENTATION
Market demand & revenue estimator are the algorithm’s heartÞ Improved version of ForecastÞ Estimate revenue for each strategy /
each hotelÞ Consider costs, added revenue
Strategy Optimizer defines the Optimal strategyÞ Using the revenue estimatorÞ Constrained optimization process
Recommandation Engine Features
• Data pre-processing
• Clustering
• Text Mining
• Classification
• Patternrecognition
• Signal processing
• Supervised learning
• Reinforcement learning
• Deep learning
• Gradient boosting
• Constrained optimization
• Network optimization
• Combinatorial optimization
Algorithm Approach
Project Structure
ALGORITHMS DATA LAKE COORDINATION
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