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