Presentation done by Noelia Oses, Jon Kepa Gerrikagoitia and Aurkene Alzua-Sorzabal, during "Dynamic pricing" workshop, of the ENTER2015 eTourism conference.

1. ENTER 2015 Research Track Slide Number 1
Dynamic pricing patterns on an
Internet distribution
channel: the case study of Bilbao's
hotels in 2013
Noelia Oses, Jon Kepa Gerrikagoitia and Aurkene
Alzua-Sorzabal
CICtourGUNE
Donostia - San Sebastián, Spain
http://www.tourgune.org

2. ENTER 2015 Research Track Slide Number 2
1. Introduction
2. Related Work
3. Methodology
4. Results
5. Conclusion and future work

3. ENTER 2015 Research Track Slide Number 3
Introduction
• The Internet has become part of society’s basic infrastructure. More and more
activities are taking place through the Internet, leaving behind digital footprints,
which can be detected and measured in real time. This also applies for tourism
accommodations and their characteristics. This information can be collected from
the internet by software programs named crawlers. (Heerschap et al, 2014).
• The aim of contributing to new technologies and methods of tourism measurement
has been included at CICtourGUNE´s research agenda. A technological platform
and a robust methodology to get online hotel price and availability named
Dynamic Pricing Monitor has been implemented (Gerrikagoitia, Alzua et al, 2011)

4. ENTER 2015 Research Track Slide Number 4
Introduction
• The motivation of the research is to understand and determine the current
dynamic pricing practices of the hotels in Bilbao.
• A data analysis based on CICtourGUNE´s accommodation industry data
assets has been conducted with hotels in Bilbao during 2013.
• The main contributions of the research work have been:
• Definition of monitoring, measurement and visualization framework
at micro level (property)
• The identification of different dynamic princing patterns
• The working hypothesis relating patterns and causality

5. ENTER 2015 Research Track Slide Number 5
Related Work
• Kimes & Wirtz (2003) define Revenue Management as the application of information systems
and pricing strategies to allocate the right capacity to the right client at the right time to
maximise revenue. Characteristics: perishable inventory, limited capacity, demand
volatility, microsegmentation, advance booking availability, and high fixed costs (Ivanov &
Zhechev, 2012).
• Dynamic pricing is a pricing policy based on time, where prices change depending on the
day on which the reservation is processed. Abrate et al. (2012) reached the conclusion that
more than 90% of the prices change with time depending, mostly, on the type of client
(business or leisure) and the star rating.
• Hoteliers are using different strategies to improve their revenue. A comparative analysis
revealed significant differences in average prices due to the segmentation of the market as a
function of the commercialisation channel (Tso & Law, 2005).
• Customers make decision to purchase flight tickets and hotel rooms based on various factors
such as the quality of the information (Wong, 2005), the time/date, past experiences (Kim &
Kim, 2004), and the frequency (Magnini & Karande, 2011). But the factor that influences the
hotel choice most is the price (Lockyer, 2005; Tanford et al., 2012; Tso & Law, 2005).

6. ENTER 2015 Research Track Slide Number 6
Related Work
It is important for hoteliers to know the level of demand and the prices offered by
competitors to define dynamic pricing policies.
• Official Statistics regularly calculate hotel pricing indices aggregated by time and geography.
• Marketing Decision Support Systems (MDSS) were developed based in IT providing
information to react to competitors’ and market changes quickly.
• In 1998, Wöber (1998) presented TourMIS, an online MDSS tool for tourism and
accommodation management.
• In 2009, Walchhofer et al. (2009b) introduced SEMAMO, a market surveillance system
combining semi-supervised techniques with semantic models.
• In 2011, CICtourGUNE created the Dynamic Pricing Monitor (Gerrikagoitia, Alzua et
al, 2011) as a technology and science infraestructure with contributions to industry
(Alzua et al, 2013) (Ibarguren et al, 2014), official statistics and public bodies (Roman et
al, 2013).
• Currently, BeonPrice has released a system called ’Analyzer’ which allows a company to
monitor competitors’ price changes in real time
Additionally to the historic and real-time information, making predictions is an essential tool in
the tourism domain (Song and Li, 2008) (Haensel and Koole, 2011)

7. ENTER 2015 Research Track Slide Number 7
Methodology / Procedure
3.1 Data collection methodology
• A data scrapping bot customised for booking.com has been used to collect the data
• The bot collects prices offered for a single overnight stay in a ’Double or Twin room’
for Bilbao.
• Hotel room prices change in real-time so information must be gathered periodically.
• The sampling method followed to collect the prices is based on that of Abrate et al.
(2012) collecting fares for hotel room reservations 1, 2, 4, 7, 15, 22, 30, 45, 60, and 90
days in advance of the date of the overnight stay (i.e. the target date). Extending this
approach, the bot used collected prices 1-28, 45, 60, and 90 days.

8. ENTER 2015 Research Track Slide Number 8
Methodology / Procedure
3.2 Data
• The collected variables are: hotel name, collection date (i.e. the date when the
price is collected form the website), target date (i.e. the date of the overnight stay),
price, and hotel’s star rating.
• There are 316.391 observations for 2013, 9.151 (0,2%) of which have the price
missing. There were 32 hotels in Bilbao operating on booking.com during 2013,
distributed as follows :
• 1-star: 3 hotels
• 2-star: 7 hotels
• 3-star: 6 hotels
• 4-star:12 hotels
• 5-star: 4 hotels
• Target dates go from 01/01/2013 to 31/12/2013
• The name ’lag-day’ is used here to refer to the difference in days between the
collection date and the target date.

9. ENTER 2015 Research Track Slide Number 9
Methodology
3.3 Temporal relationships in the data
1. [target date]. Prices offered by the same hotel corresponding to the same target date
all refer to the same product. However, prices corresponding to the same target date
but offered by different hotels are prices for competing products.
2. [collection date]. At any one instant in time, only prices with the same collection
date are simultaneously available, both for the clients to book a room and for the
hoteliers to increase or decrease the price.
3. The difference in days between the two dates.
The analysis of the data has been carried out with these temporal relationships as a key
factor.

10. ENTER 2015 Research Track Slide Number 10
3.4 Data visualisation
• row: complete series of
prices of a hotel for a target
date
• column: collection date
• block: rows corresponding to
the same target date but
different hotels
Methodology

11. ENTER 2015 Research Track Slide Number 11
• Price changes (increase or decrease)
are visualised with respect to the
previous price in the series
• The three temporal relationships
correspond to rows, columns and
step-wise diagonals in the
visualisation.
3.4. Data visualisation
Methodology

12. ENTER 2015 Research Track Slide Number 12
Results
The objective of the analysis is to explore the three temporal relationships in order to
explain hotel price dynamics.
4.1 Number of price changes per series. How often the price is changed for a target
date.
4.2 Magnitude of individual changes. How much is the increase or decrease in
prices? When does it happen?
4.3 Aggregate change in price series. Is the final price increased or decreased after
the changes?
4.4 Duration of price changes within a series. How long remains a price change?
When does it happen?
4.5 Relative location of price changes. How many changes occur by collection date,
target date and lag day? The importance of the days changes are performed, the day
the product is “consumed” and the period before the product is “lapsed”.

13. ENTER 2015 Research Track Slide Number 13
Results
4.1 Number of price changes per series - How often the price is changed for a target date.
• 75% of the price series have 5 or less price changes and 19 is the maximum number
of price changes in a single series
• Mean number of changes by category: 3-star (4.3), 4-star (3.8), 5-star (2.8), 2-star (2.1),
and 1-star (0.9).
• 39% of the price series have more increases than decreases, 32% have an equal
number of price increases and decreases, and only 29% have a greater number of price
decreases.
• Price series of hotels in categories 4- and 5-star usually have more price increases than
decreases (about 51% in both cases).

14. ENTER 2015 Research Track Slide Number 14
• The magnitude of the price increases is slightly
greater than that of price decreases.
• The mean price increase is 13.27 and the mean
price decrease is 12.09, both in Euro
• Change magnitudes for 1-star hotels are greater
than those for 2-, 3-, and 4-star hotels.
• 5-star price change magnitudes are larger than
those for all other categories
• Price changes are of larger magnitude for
weekends (i.e. Friday and Saturday).
4.2 Magnitude of individual changes. How much is the increase or decrease in prices?
When does it happen?
Results

15. ENTER 2015 Research Track Slide Number 15
• If the sum total is positive (’Up’), then the
final price is higher than the starting price in
the series.
• As the magnitude of the aggregate change
increases in absolute value, it is more likely
to be a positive aggregate change than a
negative one.
• In 2013 most of the aggregate changes were
positive
4.3 Aggregate change in price series. Is the final price (sum total of price changes per
series) increased or decreased after the changes?
Results

16. ENTER 2015 Research Track Slide Number 16
• Categories 3-, 4-, and 5-star follow the
pattern of more changes price up.
• Categories 1- and 2- star behave different.
• The smallest aggregate change happen on
Sundays and the largest on Wednesdays,
followed by Saturdays.
Results
Distribution of aggregate change by category.

17. ENTER 2015 Research Track Slide Number 17
Results
4.4 Duration of price changes within a series. How long remains a price change?
When does it happen?
• This section studies the duration of price changes within a series, that is, for each price
change the number of days that elapse before the next price change occurs within the
same price series.
• Generally, price decreases last longer than price increases.
• By category, 1-star price changes last significantly longer that price changes in other
categories. This is to be expected as category 1-star has very few price changes and,
therefore, they must last longer.
• For 2- and 5-star, price decreases last noticeably longer than price increases, especially for 2-
star.
• By month, price changes in December last longer than in other months and price increases
last longer than decreases in August, September, and October.
• By day of the week, price increases last longer than decreases on Friday and Saturday and
the opposite is true for the other weekdays.

18. ENTER 2015 Research Track Slide Number 18
Results
4.5 Relative location of price changes. How many changes occur by collection date, target date and
lag day? The activity in observed days, in the days the product is “consumed” and the period before
the product is “lapsed”
• The count of the number of price changes with regard to the three temporal relationships target
date, collection date and lag-day.
• Vertical and diagonal counts have peaks for price increases and decreases at certain collection
dates and lag-days, whereas the horizontal counts are rather more uniform for all target dates.
• This means that price changes are not uniformly distributed across dates, they occur more at
some collection dates and lag-days. This suggests that prices are changed due to underlying
causes or patterns.
vertical horizontal diagonal
collection date target date lag days

19. ENTER 2015 Research Track Slide Number 19
Results
Examples of vertical and diagonal change patterns
• Vertical pattern: Green and red
vertical lines
• Diagonal pattern: Green diagonal
line

20. ENTER 2015 Research Track Slide Number 20
Conclusion and future work
• The analysis has uncovered the dynamic pricing patterns that hotels in Bilbao
follow during 2013.
• It has shown that Bilbao’s dynamic pricing strategy on booking.com is to make
contiguous changes vertically and diagonally, that is, to change the prices for
different products on the same collection date or on the same lag-day. These have
been named vertical and diagonal dynamic pricing patterns.
• Vertical patterns include more price decreases than increases whereas all diagonal
patterns of a significant length are price increases.
• Only hotels of 3-star or greater category present long diagonal patterns, but vertical
patterns can be found in all categories.

21. ENTER 2015 Research Track Slide Number 21
Conclusion and future work
• The working hypothesis is that:
• diagonal patterns correspond to regular hotel dynamic pricing
policies and are, mostly, independent of other hotels’ actions
• vertical patterns are the direct result of external factors (factors
other than hotel policy such as an increase/decrease in the
competition’s prices, demand, availability...).
• Future work will focus on researching the causes of vertical patterns and
developing a prediction model, researching the causes of changes that are
not part of vertical or diagonal patterns, and analysing the meaning of
missing prices and whether they are correlated with room availability.