This document discusses the rise of big data and the shift towards fast data in revenue management. Some key points: - 80% of all data was created in the last 18 months and data creation is expected to increase 10 times by 2020, but the human brain cannot process such large amounts of data. - Big data is now at the core of revenue management jobs and decisions, with companies using algorithms, reporting, and pricing based on large datasets. - The shift is now towards "fast data" and having information just seconds or minutes in advance, which is more valuable than having all information months later. - An example is given of how Accor uses big data in dynamic pricing solutions, analyzing price

1. Eye For Travel, Amsterdam, November 24 2014Eye For Travel, Amsterdam, November 24 2014
Source: © INMAGINE.com

2. WE ARE EXERIENCING A PIVOTAL
MOMENT IN THE HISTORY OF DATA…
2
80%
Of all data were
created in the
last 18 months
10X
by 2020

3. UPSELL UPSELL
ROUND THE CLOCK RM ACTIVITY
… BUT A NORMAL HUMAN BRAIN
CANNOT AFFORD SO BIG DATA
3

4. BIG DATA IS ALREADY AT THE CORE
OF RM JOB AND DECISIONS
BIG DATA
Forecast
Algorithms
+1M 3.5K
reports/year users/month
Reporting
Pricing
…

5. « If you have just a little bit of the right information
a couple of seconds or minutes in advance, it’s
more valuable than all of the information in the
world six months after the fact.” Vivek Ranadive
THE NEW ERA OF FAST DATA,
‘‘the 2-second advantage’’

6. AN EXAMPLE OF BIG DATA
Dynamic pricing solution for ibis
HOW WE DO IT ?
We look for a fine understanding of clients and hotel sensitivity
Price sensitivity: client & calendar Competitive environment & place
capacity
Psychological and min / max prices
(depending on hotel cost structure)
Lead time & booking curve impacts
SMART
Pricing
per hotel
What is the right price every day (high & low season)
for each type of client (business & leisure)?
How do prices evolve depending on competition
intensity?
How does price sensitivity evolve along booking
curve and lead time?
What are the psychological prices (min and max) for
each hotel?

7. PRICE SENSITIVITY
7
0.5
1.2
2.0
We find a strong relationship between the occupancy rate and the price index.
The hotel strategy has to be based on the understanding of this relationship
Low elasticity
High elasticity
Synthesizewithavaluescale
SEASON
High Season
Low Season +
-
DAY
Monday
Friday & Sunday
Tuesday,
Wednesday,
Thursday & Saturday
+
-
LEAD TIME
Early
booking
Last minute
booking
+
-
COMPETITIVE INTENSITY &
ENVIRONMENT
High
Low
+
-
HIGH ELASTICITY:
“I RAISE MY PRICES BY 10%,
I DECREASE MY VOLUME BY 20%,
SMART PRICING IS GOING TO
RECOMMEND A « VOLUME
STRATEGY » WITH A PRICE DROP
LOW ELASTICITY:
“I RAISE MY PRICES BY 10%,
I DECREASE MY VOLUME BY 5%,
SMART PRICING IS GOING TO
RECOMMEND A « MARGIN
STRATEGY » WITH A PRICE
INCREASE.
AN EXAMPLE OF BIG DATA
Accor Dynamic pricing solution

8. SHIFTING TO FAST DATA NOW
All in one solution, decision driven data
BENEFITS
ALL INCLUSIVE TOOL
OUTPERFORMING OTHER HOTELS BY 5% IN REVPAR,
1 REVENUE ANALYST MANAGING UP TO 25 HOTELS
offers an “all inclusive” tool enabling hotels to monitor
all Pricing related matters in a powerful, efficient & user
friendly environment.
PICK UP Follow your hotel
pickup for the last 21 days
Calculate & finalize
price grid & strategy
RATE SHOPPER
Supervise the
competitors prices
TARSiPMS
Daily Pricing Monitoring
through a fully
automated & integrated
INTERFACE
Access reliable
and aggregated
REPORTS
SUPPORT Register and
follow any bug online

9. FAST DATA, WHAT’S NEXT ?
CUSTOMER CENTRIC REVENUE MANAGEMENT
2.5TIMES HIGHER
A customer value
LOYAL
CUSTOMERS
3TIMES HIGHER
vs traditional approach
Click & conversion rate
CUSTOMIZED
OFFERS BY PROFILE
8

10. Data scientist :
new profile in RM,
BI, CRM, web
FAST DATA: What is going to change for RM
and Analytics ?
Customer centric RM
optimization
vs. transaction
Integration
All in one solutions
Real-time data

11. 11
agnes.roquefort@accor.com
THANK YOU