Dynamic Pricing for Hotel Revenue Management

MASTER IN BUSINESS MANAGEMENT PROGRAM
FINAL REPORT – PRJ600
Dynamic Pricing for Hotel Revenue Management
BY
Alaeddine Ferjani
ACADEMIC SUPERVISOR
Dr. Amira Meliane
COMPANY SUPERVISOR
Mr. Karim Arif
Tunis, 2018-2019

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Abstract
This paper illustrates a comprehensive analysis of Le Corail Suites Hotel dynamic pricing
model. Based on both reservation information and competitors’ occupancy data, a revenue
management tool has been developed in order to estimate the daily occupancy rate.
One of the biggest challenges for hotels is pricing since they are not only required to set prices
for current dates, but they must also quote rates for up-coming dates and communicate them
to the different distribution channels. We develop a predictive model of dynamic pricing using
a multiple regression algorithm. The estimated statistical model presents accurate predictions
of the actual daily occupancy rates of our hotel. Le Corail Suites Hotel occupancy rate co-
move strongly with its competitors’ occupancy rates and we reveal that a price based on
forecasted occupancy rates can significantly increase the revenues.
Keywords: Dynamic pricing, occupancy rate forecasting, revenue optimization, forecasting
for hotels, revenue management systems, pricing strategies, hospitality industry.

Acknowledgements
I am extremely grateful to the General Manager of Le Corail Suites Hotel Mr. Karim Arif
for providing the reservation and occupancy data that made this research possible. Moreover,
in spite of being heavily busy with his duties he made always sure that we were being on the
right track. I am also grateful to the sales director, Mr. Mehdi Ben Thabet, for providing
guidance and giving me access to the insights provided by OTAs.
I would also like to express my deepest appreciation to Dr. Amira Meliane, my academic
supervisor, and Dr. Salma Fourati, my academic reader, who made me persist and progress in
my internship.
A special thanks goes to the HR assistant, Mr. Walid Baccouche, and to my colleagues in the
finance department with whom I worked closely.

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Table of Contents
Approval ...................................................................................................................................................iii
Declaration.............................................................................................................................................. iv
Work Term Release .................................................................................................................................. v
Abstract.................................................................................................................................................... vi
Acknowledgements...............................................................................................................................vii
List of Tables............................................................................................................................................10
List of Figures...........................................................................................................................................11
List of Equations......................................................................................................................................12
1. EXECUTIVE SUMMARY.....................................................................................................................13
2. INTRODUCTION ...............................................................................................................................14
3. COMPANY CONTEXT......................................................................................................................15
3.1. Description of the company ...............................................................................................15
3.2. Mission and Objectives.........................................................................................................16
3.3. Industry structure ...................................................................................................................16
3.3.1. SWOT Analysis ................................................................................................................16
3.4. Market Structure ....................................................................................................................17
3.4.1. Porter’s Five Forcers Analysis........................................................................................17
4. INTERNSHIP DESCRIPTION...............................................................................................................19
4.1. Internship Context.................................................................................................................19
4.2. General and specific objectives of the Internship...........................................................20
4.3. Challenges and Obstacles..................................................................................................20
4.4. Assigned Tasks and Responsibilities ....................................................................................20
5. LITERATURE REVIEW.........................................................................................................................22
6. METHODOLOGY .............................................................................................................................26
6.1. CRISP-DM Framework ...........................................................................................................26
6.1.1. Business Understanding ....................................................................................................26
6.1.2. Data Preparation ..........................................................................................................26
6.1.3. Data Understanding.....................................................................................................27
6.1.4. Data Visualization .........................................................................................................31
6.1.5. Modeling Phase.............................................................................................................33
7. RESULTS AND FINDINGS..................................................................................................................36
7.1. Statistical Model 1 .................................................................................................................36
7.1.1. Regression results...............................................................................................................36
7.1.2. Statistical significance of the model..........................................................................37

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7.1.3. Testing and interpreting the regression parameters................................................38
7.1.4. Model Assessment.........................................................................................................39
7.2. Statistical Model 2 .................................................................................................................40
7.2.1. Regression results...............................................................................................................42
7.2.2. Statistical significance of the model..........................................................................43
7.2.3. Testing and interpreting the regression parameters................................................44
7.2.4. Model Assessment.........................................................................................................44
7.3. Dynamic Pricing Model........................................................................................................46
8. RECOMMENDATIONS.....................................................................................................................49
9. CONCLUSIONS................................................................................................................................51
REFERENCES............................................................................................................................................52

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List of Tables
Table 1: Room Types.................................................................................................................................. 15
Table 2: Hotels in the local market ............................................................................................................. 16
Table 3: SWOT Matrix ................................................................................................................................ 17
Table 4: Features used in this study............................................................................................................ 29
Table 5: Data sources used in this study ..................................................................................................... 30
Table 6: Model 1 Summary......................................................................................................................... 37
Table 7: Model 2 Summary......................................................................................................................... 43
Table 8: Rates deployed per type of room .................................................................................................. 47

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List of Figures
Figure 1: Correlation between different features........................................................................................ 31
Figure 2: Occupancy rate dynamics ............................................................................................................ 32
Figure 3: Occupancy rate cycles for Corail versus its competitors ................................................................ 33
Figure 4: Screenshot of the features’ description........................................................................................ 34
Figure 5: Screenshot of the features’ types................................................................................................. 35
Figure 6: Screenshot of train/test split of Model 1...................................................................................... 35
Figure 7: Residuals plot of the Model 1....................................................................................................... 39
Figure 8: Screenshot of the evaluation metrics ........................................................................................... 40
Figure 9: Correlation between the different features of Model 2 ................................................................ 41
Figure 10: Residuals plot of the Model 2..................................................................................................... 45
Figure 11: Screenshot of the evaluation metrics ......................................................................................... 45
Figure 12: Comparison between the evaluation metrics ............................................................................. 46

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List of Equations
Equation 1: Multiple regression equation form........................................................................................... 33
Equation 2: Null hypothesis equation......................................................................................................... 34
Equation 3: Alternate hypothesis equation................................................................................................. 34
Equation 4: Multiple regression equation of Model 1 ................................................................................. 38
Equation 5: Multiple regression equation of Model 2 ................................................................................. 44

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1. EXECUTIVE SUMMARY
One of the most critical success factors for a lodging is setting up a profitable pricing
strategy. This pricing should be the same across all the distribution channels to avoid any
problem between them. Indeed, the big challenge for hotels is to sell their perishable room
inventory at highest possible rates. Consequently, failing in setting prices that are aligned with
competition will lead to a poor financial performance.
Le Corail Suites Hotel is one of four luxury hotels operating since 2013 in a particular region
called ‘Les Berges du Lac’ located in Tunis. The company is facing a big problem when it
comes to setting the rates at which rooms should be booked on a given day. Indeed, currently
they are being determined randomly by the General Manager after taking a look at historical
data including competitors’ prices and their occupancy rates (i.e. the number of booked
rooms). The problem with this method is that it cannot be done on a daily basis and sometimes
factors like events could be left behind. On the other hand, fixed pricing strategies will leave
no room for rate bargaining or negotiation. In the long run, this will lead to huge loss of profit.
The results and findings of this study recommend that the hotel starts using a dynamic
automated pricing approach that helps to take the right pricing decisions while foreseeing
future market demands. The model will help the General Manager of Le Corail Suites Hotel to
find rates that maximize the hotel’s revenue toward the changing market demands. Based on
our study, the decision-making tool that we have created will help him make informed and
accurate decisions at the right time to optimize profitability. This makes him able to sell his
perishable rooms inventory at highest possible rates. Indeed, the study showed that the factors
day of the week, special events including Ramadan and local competitors’ occupancy rates
can explain 74% of the variance of an important performance indicator named occupancy
rate. Consequently, including those features when estimating the price will lead to an optimal
revenue management.
However, the top management of Le Corail Suites Hotel could personalize this pricing predictor
depending on their pricing strategy and their vision. Indeed, it could take in other key
performance factors like the average daily rate. Those factors will perform as drivers to
maintain healthy balances (Ivanov, 2014).

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2. INTRODUCTION
Revenue management is a fundamental science of controlling a finite amount of
inventory to maximize profit, by dynamically managing the price and the quantity offered.
Recently, this phenomenon is being adopted in the tourism industry, at least for luxury hotels
(Ivanov, 2014). Like flight tickets, hotel rooms profitability has evolved recently by the adoption
of dynamic pricing (Saleh, Atiya, & Habib, 2013).
Hotel pricing is a challenging field since beside putting various rates for different room
categories (standard rooms, superior rooms, executive suites, etc.) and customer categories
(corporate guests, tourists) the hotel manager must be available to regularly update a large
array of future rates since there is a possibility that guests book their rooms well in advance of
their arrival date (Cho, Lee, Rust, & Yu, 2018).
We analyze large datasets of daily records of reservations, rates and occupancy of a lavish
hotel based in a major city located in the capital of Tunisia. We formulate and build a revenue
management tool that sets rates to maximize the expected profits.
Our main finding is that our algorithm delivers accurate predictions of Le Corail Suites Hotel
daily occupancy rates. This will reduce the uncertainty of the upcoming demand and help
the top management to take the appropriate decisions and maintain healthy balances.
In this paper we propose a new dynamic pricing approach. It is based on several factors like
events, competitors’ prices and occupancy rates, and day of the week. The details on how
we have identified those factors are described in the section 6.
The rest of the paper is arranged as follows. Section 3 and 4 present a detailed description of
the context. Section 5 illustrates a review of the literature. The results and findings are
demonstrated in section 7. Finally, we discussed the recommendations in section 8.

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3. COMPANY CONTEXT
3.1. Description of the company
Located in the heart of the business district les Berges du Lac II, Le Corail Suites Hotel is
one of the luxurious hotels in the capital, which opened its doors during the month of October
2013. It contains 134 Rooms and Suites that satisfies the needs for a wide scope of nearby and
foreign customers.
Table 1: Room Types
Code Description
Number of
rooms
% of
rooms Rack Rate
CC Standard Room 22 16% TND 600
CP Prestige Room 20 15% TND 620
SC Superior Room 21 16% TND 640
SJ Junior Suite 23 17% TND 740
SE Executive Suite 45 34% TND 780
SP Premium Suite 1 1% TND 950
SR Royal Suite 2 1% TND 1,600
Total 134 100%
Among these 7 room types, the standard room is always best-seller. If an overbooking happens
for CC rooms, guests are gratuitously upgraded to the next tier ‘Superior Room’.
Le Corail Suites Hotel is one of four luxury hotels operating in a tightly defined local area that is
recognized by Online Travel Agencies (OTAs) and other travel agents.
Although clients can book at other luxury hotels in different parts of this city, the areas of these
other lavish lodgings are adequately a long way from this specific alluring territory that they
are not viewed as applicable substitutes for clients who wish to remain in this particular region
of the capital.
The company that owns this hotel possesses two other resort hotels, one of them is located in
Hammamet and the other one in Djerba. Their respective names are Le Corail Appart Hotel
and Iberostar Mehari Djerba.
Fortunately, I had the chance to work as a revenue management intern collaborating with
the financial and sales department of Le Corail Suites Hotel.

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3.2. Mission and Objectives
The mission of Le Corail Suites Hotel is to put Tunisian hospitality services on the highest
level of quality to meet the guests' expectations. Le Corail's team is aiming to make the hotel
a spot for experiences, business success, pleasant meetings and gala ceremonies.
3.3. Industry structure
The Hospitality Industry like any other industry is competitive, innovative and is being
cleared by the rush of modernization in operations and outlook. The key practices for arranging
the development and for increase in the return on investment are being used adequately.
There have been severe changes in the Hospitality structure because of the changing global
trends especially associated with travel, business life, social and different technological trends.
The flow of progress in the ownership for key hospitality companies has added complex
variables to this generally fragmented Industry.
This industry is extremely complex as hotels are implementing severe strategies like franchising,
strategic alliances and management contracts to improve their growth and gain in terms of
market share.
Table 2: Hotels in the local market
Property Star
Chained
Brand
Capacity
in rooms
Rating
Booking.com
Le Corail Suites Hotel - Lac 4+ No 134 8.0
Concorde – Lac 5 Yes 129 7.6
Concorde Hotel Paris - Lac 4 Yes 70 7.8
Novotel - Tunis 4 Yes 126 7.8
Hotel Belvedere - Tunis 4 No 69 8.9
Hotel ibis - Tunis 3 Yes 152 7.6
In our study, we are going to disregard the number of stars of Le Corail’s competitors as we
assume that the guests’ overall demand for hotel rooms is more sensitive to the customers
reviews (i.e. booking.com rating) and if the demand goes up for any hotel in Tunis, it will drive
up the occupancy rate for all of them.
3.3.1. SWOT Analysis
The following matrix illustrates the strengths, weaknesses, opportunities and threats of
Le Corail Suites Hotel.

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Table 3: SWOT Matrix
Strengths Weaknesses
- A strong online reputation (8.0 out of 10
on booking.com).
- Le Corail Suites Hotel is not a member of
a chained brand.
- First business hotel in the region of les
Berges du Lac 2.
- High fixed costs compared to substitutes
like Airbnb apartments.
- Certified by Cristal international
standards (Quality Check).
- Less entertainment facilities.
- No rooms with lake-view.
Opportunities Threats
- Business district surrounded by
international companies and Hospitals.
- *The hotel is neighboring the American
and the Canadian embassies.
- Competition from international brands
like Movenpick.
- *The hotel is neighboring the American
and the Canadian embassies.
- Les Berges du Lac 2 is considered a
fast-growing city.
- Two new international branded hotels
located in the same street are opening
their doors soon.
- Vast employment pool. - Unstable economic and political
environment.
*This specific point is mentioned as a threat and opportunity at the same time, it is considered
as an opportunity since embassies generally organize their meetings at Le Corail Suites Hotel
and foreign visa applicants will also book their rooms directly at our hotel. In the other hand, it
can be a threat because petitions could happen in the area where the embassies are
located, this could threaten the security of our hotel. For the sake of example, we can mention
the petition that happened on September 14th, 2012 when protesters climbed the walls into
the US Embassy in Tunis, blowing up the cars and destroying the entrance building.
3.4. Market Structure
3.4.1. Porter’s Five Forcers Analysis
As per Porter (2008), mindfulness of the five competitive forces could help a business to
understand the field and position itself in a substantially more lucrative spot that is more secure
from attacks.
In such manner, Porter’s five forces approach plans to uncover the bargaining powers of the
suppliers and clients, rivalry between current organizations, threats of substitute products and
conceivable new entries into the business.

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When all the opportunities and threats made by each industrial power turns out to be clear, it
will be possible for lodging managers to produce and lead hostile and protective systems to
position their hotels properly (Tavitiyaman, Qu& Zhang, 2011, p. 648).
Threat of new entrants
The first important variable to have a competitive advantage is the entry of new
businesses to the industry and the threats posed by them.
Despite the fact that investing money in hotels is not something that will lead to short term
return on investment and it necessitates a very big capital, Le Corail Suites Hotel is currently
being threatened by new competitors.
During the last year, Movenpick Hotel Lac Tunis, a managed hotel by Movenpick Hotels &
Resorts, opened its doors in approximately the same geographic area of our hotel. Even
though, Movenpick is not considered as a direct competitor of Le Corail Suites Hotel, we
believe that, somehow, it influences Corail’s occupancy rate as it has a certain impact on the
overall demand by its relatively large capacity of 189 rooms.
This year, another new hotel named Avani Suites opened his doors in the same district of our
Suites Hotel. These two hotels do have the same segment of customers of our hotel, both of
them are targeting business people that are searching for an accommodation in the Lake of
Tunis region. According to our General Manager, Novotel Lac will also open its doors in the
same street where Le Corail Suites Hotel is located, this will happen within the next year.
To conclude, Movenpick Hotel du Lac opened its doors during 2018, Avani Les Berges Du Lac
Tunis Suites started operating during the first trimester of 2019, Novotel Lac is expected to open
its doors during April 2020. This means that within three years, three new lodgings entered the
market of business tourism in the area of les Berges du Lac. Consequently, we can say that our
hotel is facing is high threat from potential entrants.
Bargaining power of suppliers
Compared to other industries, hotels are not significantly subject to the bargaining
power of their suppliers and encounter low levels of tension on their competitiveness from this
force. For a sustainable business strategy over the long run Le Corail Suites Hotel will have to
maintain a permanent cost advantage over potential similar businesses in higher strategic
groups, say in the five-star hotels, as well as further differentiating itself within its own strategic
group. Indeed, most of the products or services consumed by hotels are generally available

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at more than one supplier. This enables the hotels to have a high bargaining power over their
suppliers.
Bargaining power of customers
Certain buyer groups force a bargaining power as a consequence of their
concentration or bulk booking. These groups would include domestic or international travel
agencies, tour operators and large customers, like convention organizers or corporations. This
factor is more sensitive for the lower tier hotels which depend more on travel groups than the
independent leisure or business traveler. Overall, we can say that B2B customers have a high
bargaining power over business hotels as we believe that even individual travelers will book
their rooms through OTAs, which are considered as powerful customers.
Threat of substitute products or services
Even though, there are remarkable differences between the quality of services offered
by hotels and Airbnb apartments, guests that are price elastic could find high-end apartments
in the same region available for booking at lower rates compared to hotels’ prices. In fact, we
believe that the profitability of Le Corail is barely impacted by this low threat of substitutes.
Rivalry among existing competitors
In the Lake of Tunis region, Le Corail Suites Hotel has four major competitors, two of
them entered the market during the six previous months, Concorde Hotel Paris, Concorde
Hotel les Berges du Lac, Movenpick Hotel du Lac and Avani Suites Hotel.
Even though rivalry between these businesses is insane, as they have practically the same level
of quality, they still collaborate and share their Average Daily Rates (ADR) to maximize their
profitability and maintain healthy balances especially during the off-season periods.
Consequently, we can say that the threat of rivalry among existing competitors is considered
moderate.
4. INTERNSHIP DESCRIPTION
4.1. Internship Context
Le Corail Suites Hotel is currently facing a big problem when it comes to determining
the price at which the room should be booked on a given day. Now, it is being determined
randomly by the General Manager after taking a look at the competitors’ prices and their
occupancy rates (i.e. the number of booked rooms).

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For a person that did not work in the business tourism field, getting the daily competitors’
occupancy rate seems impossible and not feasible but we realized that this task is legal, and
it is done by almost all the hotels to help their selves collaborate and maximize their revenues.
Again, it is important to know that currently the General Manager needs a decision-making
tool to help him determine the daily room rates.
4.2. General and specific objectives of the Internship
Starting from the present situation and taking into consideration the different factors
that can impact the price at which the hotel room should be booked such as: seasonality or
events, hotel rooms occupancy rate, competitors’ prices and occupancy rates, we intend to
conceive a model that allows Le Corail Suites Hotel to set a dynamic pricing algorithm. That
one should maximize the hotel sales revenue based on the fixed costs, seasonality or events,
forecasted demand (i.e. occupancy rate) and competitors’ prices. Briefly, the study objectives
of this research are ‘(1) to find the variables that have direct impact on the price at which the
rooms are being sold and (2) build a dynamic pricing model based on these features.
4.3. Challenges and Obstacles
While it is essential to take a look at the effect of historical strategies and data, looking
forward is similarly vital as in the business tourism industry, any event that happens in the macro
environment could have a very dangerous impact on the hotel’s turnover. Forecasting the
price in a very uncertain environment is considered as a tremendously challenging task given
the weight of the unexpected events that can happen. Indeed, there are a lot of factors
influencing the best available rate (BAR) of any hotel situated in Tunis, most of them are
unmanageable.
4.4. Assigned Tasks and Responsibilities
As a first step, we were asked to extract the data from the hotel information system
‘Top Hotel’ and define the key performance indicators (KPIs) to assess the current room price
of Le Corail Suites Hotel. These KPIs are also essential in determining the data mining goal which
is predicting the price that will be shown in the different online travel agencies (i.e.
booking.com or Expedia or Hotels.com).
Also, to enhance the data that we are using for our study, we conducted competitive analysis
to determine competitors’ prices and ensure increased reservation rates that will affect
automatically the profitability of the hotel.

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Our ultimate objective is to set up a model that will forecast future demand and pricing trends.
As a part of competitive analysis, we were also asked to weekly scrap the data of Tunisian
hotels from booking.com and compare their rates with our hotel. This helped to make better
decisions regarding our price at least for the short term.
Also, based on the data provided by the National Office of Tunisian Tourism (NOTT), we were
asked to determine the demographics of Le Corail’s customers as compared to the guests
who entered the region of Tunis-Carthage. The numbers provided by the NOTT helped as well
in finding the market share of our hotel from the total number of arrivals and night stays in the
same region. In addition, these insights showed the different nationalities of our guests. This
information was used to determine the international holidays that will, a priori, impact
negatively our occupancy rate.

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5. LITERATURE REVIEW
The business tourism industry is one in which accurate forecasting of occupancy rates
is crucial. Far from the businesses that produce and stock tangible products, the lodging
industry produces immaterial services that are produced and consumed at the same time.
The need to adjust resources to consumed rooms is fundamental to both effective activity and
guest satisfaction (Warren, 2017).
The fundamental idea of revenue management is to expand incomes through demand-
based variable pricing in light of a forecast of demand for any future date (Ivanov, 2014).
Revenue management is best in exchanges which include variable demand and generally
fixed, very short-lived inventories (Ivanov, 2014).
Also known as yield management, revenue management is a fundamental tool for matching
supply and demand by segmenting the market into various sections of customers based on
their expectations and allocating capacity to those different segments in a manner that boosts
a specific company’s revenues. It is also defined as the application of pricing strategies to
assign the right capacity to the right customer at the right price at the right time. This makes
revenue management one of the components of marketing management where it has an
essential role in demand creation. The theory of this field also benefited well from research in
operation and pricing (Ivanov, 2014).
Created by the airline industry, the revenue management has enlarged to its present state as
a typical business practice in a wide scope of fields. Not only used by airlines, hotels and
restaurants also benefit from this instrument after its deregulation procedure during the 1970s.
While yield management can be deployed by many industries, its principles may differ from
one to another. For example, successful revenue management strategies for airlines, are not
always the best solutions for a restaurant or a hotel (Ivanov, 2014).
Several fundamentals and assumptions determine Hotel revenue management applicability
in the business tourism industry:
▪ Product perishability
Hotel’s product is basically a service which cannot be stocked for later utilization that is why
excessive capacity will not remain available for use during high demand periods, creation and
consumption of the inn services happen at the same time with the active cooperation of the
guest. Each room that has not been utilized for a specific period (overnight) is a wastage of

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money that will not be retaken forever as customers would not pay for already passed periods
(Ivanov, 2014).
▪ Limited Capacity
In the short term, the physical capacity of a hotel is fixed (i.e. the number of rooms cannot be
changed). However, hotel managers can easily decrease the capacity by flagging some
rooms as unavailable during low demand periods to reduce operating costs. The capacity of
alternate services in a hotel, that are also generating a lot of revenues, can be easily increased
or decreased in the short run. In the long term, the capacity of every hotel is variable (Ivanov,
2014).
▪ High fixed costs and low variable costs
It is well known that hotels are one of the businesses having a very high fixed costs, these costs
do not vary according to the number of customers in a lodging. For the sake of example, we
can mention depreciation, wages for administrative personnel and part of the utilities expense
(Ivanov, 2014).
The ability to precisely forecast the number of consumed rooms for a random night is a critical
component in augmenting guest satisfaction and profitability in a hotel. As noted above, the
generation and utilization of the experience are simultaneous and may not be stocked, and
the opportunity perishes each night (Ivanov, 2014).
Further authors like Saleh, Atiya and Habib (2013) considered that revenue management tools
are essential when it comes to taking structural decisions, price decisions and quantity
decisions in the hotel industry.
Warren (2017) checked on the issues of forecasting unconstrained room demand and the
difficulties with different traditional forecasting techniques.
While there has been noteworthy research into revenue management as a field in the lodging
industry (Mohammed, Guillet, Schuckert, & Law, 2015) there has been little research done on
forecasting occupancy in lodgings specifically.
Schwartz and Cohen (2004) called attention to the subjectivity of forecasting using a
simulation approach in a survey of revenue management professionals.
Seven distinctive forecasting models were analyzed by Zhang, Li, Pan and Zhang (2018). These
models had different degrees of estimating accuracy. Zhang, Li, Pan and Zhang (2018)

24
additionally tested the precision of aggregated and disaggregated forecasting techniques
and found that the disaggregated forecasting outflanked the different aggregated
approaches.
Zheng, Bloom, Wang, and Schrier (2012) found in the estimating of RevPAR, simple moving
average and single exponential smoothing approaches beat ARIMA and artificial neural
systems. A review of recent literature claims that it is extremely challenging to find a ‘best’
forecasting method (Zhang, Li, Pan, & Zhang, 2018).
Ivanov (2014) defined the various metrics that can mesure the performance of a given hotel
which can be interpreted in four relevant metrics; the most used one in forecasting is the
Occupancy Rate (OR) then we have the Average Daily Rate (ADR), the multiplication of these
two key performance indicators (KPIs) gives birth to the Revenue per available room (RevPAR)
and the last one is the Gross Operating Profit per available room (GOPPAR).
As we noted above the most important variable to predict the price of a given hotel is the
demand which can also be called occupancy rate, the feature showing the ability of a
lodging to generate revenues in a given period of time. Ivanov (2014) introduced six variables
that can explain the variation of the OR. The first one is ‘Day of week’, in our case it is generally
higher during the weekdays and lower during weekends as we have a business hotel. The
second one is ‘Period of the year’ which alludes to the seasonality or events for our case. The
third one is ‘Market Segments’ which cannot be measured in our situation as the system does
not keep track of it. The fourth one is ‘Special events’ which can either increase or decrease
the demand for business hotels. On the one hand, events like tradeshows or conferences could
significantly drive up the occupancy rate of a business hotel. On the other hand, national or
religious holidays could have a significant negative impact on the occupancy rate.
Also, factors like competitors’ actions or activities of a given hotel could influence its OR.
Other researchers like Cho, Lee, Rust, & Yu (2018) found out that factors like the average BAR
of competing hotels, the number of cancellations, and the number of group reservations do
have an impact on the occupancy rate. Consequently, this influences the BAR of lodgings.
Additionally, Warren (2017) detected that the market demand for hotels depends on features
like length of stay, early booking and daily-pickup rate.

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6. METHODOLOGY
The project used a quantitative methodology based on the approach advocated by
(Zhang, Li, Pan, & Zhang, 2018). The research was based on the CRISP-DM process and aimed
to set an automated algorithm that updates the BAR of Le Corail Suites Hotel on a daily basis.
In this study, we followed the CRISP-DM Framework with some modifications. As noted in the
book written by Larose & Larose (2015), we have also followed their recommendation and
preferred cleaning the data before starting the exploration phase.
6.1. CRISP-DM Framework
6.1.1. Business Understanding
As stated by the framework, the first step consists in understanding the business.
As an assessment of the current situation, we can say that currently the BAR level is being set
arbitrarily based on internal historical data only.
Our research objectives as a revenue manager are ‘(1) to find the variables that have direct
impact on the price at which the rooms are being sold and (2) build a dynamic pricing model
based on these features’.
To elaborate a project plan, we chose to collect the data from ‘Top Hotel’, the hotel’s
information system, prepare it and understand it in order to reach the third step which is the
modeling phase.
6.1.2. Data Preparation
We needed to perform some modifications on the data available that is why we
created three dummy variables.
The first dummy is the feature called ‘is_weekend’, we simply managed to add one column in
our dataset and place the value ‘1’ if the corresponding day of our record is Saturday or
Sunday. Otherwise we just put ‘0’ instead of ‘1’ for the remaining weekdays.
The second dummy is the variable called ‘is_ramadan’, we searched for the dates of the
Muslims religious month ‘Ramadan’ for the period starting from 2016 to the end of 2018, we
coded them as ‘1’ and ‘0’ for the rest of the days.

27
Before defining the last dummy variable, we tried to figure out what are the demographics of
le Corail Suites Hotel’s customers. We extracted the insights from ‘Top Hotel’ to figure out that
57% of the arrivals are Libyans, next to them we find the customers coming from France who
represents 7% of the arrivals to our hotel.
As we are considering a business hotel in our analysis and based on these insights, we exported
the list of holidays in Tunisia, Libya and France. As we noted above, these holidays should have
an impact on the occupancy of our hotel as they are considered non-business days.
Here comes the definition of our last dummy variable named ‘is_event’, where we simply
managed to mention ‘1’ if the record corresponds to a holiday either in Tunisia, Libya or
France. Otherwise, we simply mention ‘0’. This variable does not only include public or national
holidays but also the religious holidays like ‘Aid’ or ‘Christmas’.
Then, we took a step ahead to start preparing competitors’ data which is mainly composed
of the occupancy rates of the competitors and their average daily rate.
For the local competitors, Hotel Paris and Hotel Concorde which are situated in Les Berges du
Lac region, we chose to create only one variable that can explain the variation of their
occupancy rates. This new attribute is the mean and it is called ‘OR Compset_Zone’. As these
two hotels are located in the same region and have similar fluctuations of their occupancy
rates, we believe that it will be useless to include both of them in our model, so we had just
kept the average.
Unfortunately, the data available for both Hotel Belvedere and Hotel Ibis is for the period
ranging from 01/01/2018 to 31/03/2019. That is why, a priori, we decided to drop the columns
corresponding to these two hotels.
6.1.3. Data Understanding
The second step consists in data understanding where we were asked to export the
hotel reservation data and the different Key performance Indicators (KPIs) from Top Hotel. We
did not need to check the data quality as it is extracted directly from the system and already
verified by the reservation department.
Data
Le Corail Suites Hotel is one of four luxury hotels operating in a well-defined local area
called Les Berges du Lac. In spite of the fact that clients can book at other lodgings in different

28
parts of the city of Tunis, the areas of these other hotels are adequately a long way from this
specific alluring territory that they are not viewed as applicable substitutes for guests who wish
to remain in this particular zone of the city.
Table 1 records some synopsis data about the six lodgings: all are 7.5 or higher rated hotels,
according to booking.com, that are classified as upscale or luxury class.
Our model uses all relevant data including daily occupancy rates which is quite important for
the algorithm that we are planning to build.

29
Table 4: Features used in this study
Number Variable Description
1 OR Corail Le Corail Suites Hotel occupancy rate
2 OR Concorde Concorde les Berges du Lac occupancy rate
3 OR Paris Concorde Hotel Paris occupancy rate
4 OR Novotel Novotel occupancy rate
5 OR Belvedere Hotel Belvedere occupancy rate
6 OR Ibis Hotel ibis occupancy rate
7 ADR Corail Le Corail Suites Hotel average daily rate
8 ADR Concorde Concorde Lac average daily rate
9 ADR Paris Concorde Hotel Paris average daily rate
10 ADR Novotel Novotel average daily rate
11 ADR Belvedere Hotel Belvedere average daily rate
12 ADR Ibis Hotel ibis average daily rate
13 OR Compset_Zone Average of 2 and 3
14 ADR Compset_Zone Average of 8 and 9
15 OR Compset Average of 4, 5 and 6
16 ADR Compset Average of 10, 11 and 12
17 is_weekend Dummy variable (weekend=1, weekday=0)
18 is_ramadan Dummy variable (Ramadan=1, not Ramadan=0)
19 is_event Dummy variable (holiday=1, not holiday=0)
Data sources
The clients of Le Corail Suites Hotel are both business/government clients who
essentially remain in the hotel on weekdays and leisure guests who typically remain on
weekends. Since corporate clients and government clients are refunded for their travel costs,
we can expect them to be less price elastic than tourists. Then again, numerous government
organizations and corporations that frequently do business in this city have negotiated
discounted rates with our hotel. These amounts are generally a fixed percentage, often 20 to
25%, off the current price that is name the best available rate (BAR). In addition, our hotel pays
an important commission, ranging from 15 to 25% for rooms booked via OTAs such as
booking.com.

30
Table 5: Data sources used in this study
Data
The first
day of
occupancy
The last
day of
occupancy
Observations Description
Night-Watchman 01/01/2016 31/03/2019 1185
Competitors' price and
occupancy
Reservation raw 01/01/2016 31/03/2019 44284 Reservations detail information
Data Range 01/01/2016 31/03/2019 39 months
This allowed us to start the data exploration phase during which we chose to use a heatmap
module in Python to plot every variable available in our dataset and see if there are any
correlations (figure1).
Correlation Analysis
Our main purpose from the data exploration phase is to find the features correlated
with the occupancy rate of Le Corail Suites Hotel (OR Corail).

31
Figure 1: Correlation between different features
At first glance, we can see that the two variables ‘OR Novotel’ and ‘OR Compset’ have a
strong positive relationship with our dependent variable ‘OR Corail’.
Also, the three features ‘is_weekend’, ‘is_ramadan’ and ‘is_event’ have a significant negative
relationship with our class attribute.
6.1.4. Data Visualization
Figure 2 is a scatter plot that shows a linear relationship between the occupancy rate
of Le Corail Suites Hotel and the two variables ‘OR Compset_Zone’ and ‘OR Novotel’.
This summarizes the strong positive relationship that we can see in the correlation matrix
(Figure1).

32
Figure 2: Occupancy rate dynamics
Figure 3 plots the time series of occupancy rates for four hotels in this market for the month of
March 2019 using the Night-Watchman data. Two of them as mentioned above are
summarized in ‘OR Compset_Zone’

33
Figure 3: Occupancy rate cycles for Corail versus its competitors
6.1.5. Modeling Phase
Since our dependent variable ‘OR Corail’ was measured on a continuous scale and
we do have more than one independent feature, we chose to apply a multiple regression
model to reach our data mining objective.
Multiple regression analysis
A regression model was created to evaluate the explanatory effect of independent
features exported from our dataset on our class attribute, Corail’s occupancy rate. In this study,
a multiple regression analysis was needed. Indeed, the analysis required five independent
features.
Equation 1: Multiple regression equation form
𝒚 = 𝜷 𝟎 + 𝜷 𝟏 𝒙 𝟏 + 𝜷 𝟐 𝒙 𝟐 + 𝜷 𝟑 𝒙 𝟑 + 𝜷 𝟒 𝒙 𝟒 + 𝜷 𝟓 𝒙 𝟓 + 𝜺
▪ 𝒚: The class attribute / the dependent variable (the occupancy rate of Corail).
▪ 𝜷 𝟎: The intercept.
▪ 𝜷𝒊: The regression coefficient.
▪ 𝒙 𝟏: The occupancy rate of Novotel
▪ 𝒙 𝟐: The average occupancy rate of Hotel Concorde and Hotel Paris.
▪ 𝒙 𝟑: Weekend or weekday.
▪ 𝒙 𝟒: Ramadan or not Ramadan.

34
▪ 𝒙 𝟓: Holiday or not holiday.
▪ 𝜺: The residuals
Hypothesis formulation
As stated above in the theoretical framework CRISP-DM, we want to test how powerful
are the independent variables in predicting our class attribute. As this study is based on a
hypothetical-deductive approach, a null hypothesis must be set to be rejected to support an
alternate hypothesis. Below, we have noted the null and alternate hypothesis.
Equation 2: Null hypothesis equation
𝑯 𝟎: 𝜷 𝟏 = 𝜷 𝟐 = 𝜷 𝟑 = 𝜷 𝟒 = 𝜷 𝟓
𝑯 𝟎 assumes that none of the independent features has an impact on the variation of the class
attribute.
Equation 3: Alternate hypothesis equation
𝑯 𝟏: 𝒂𝒕 𝒍𝒆𝒂𝒔𝒕 𝒐𝒏𝒆 𝜷𝒊 ≠ 𝟎 𝒘𝒉𝒆𝒓𝒆 𝒊 𝒍𝒊𝒆𝒔 𝒘𝒊𝒕𝒉𝒊𝒏 [𝟏, 𝟓]
𝑯 𝟏 assumes that at least one independent feature has an impact on the variation of the class
attribute.
Python
After formulating the hypothesis, 𝑯 𝟎 is considered true until statistical evidence. That is
why, we used Python as a statistical tool to support our alternate hypothesis.
At this phase, we had a ready csv file with all needed features to be analyzed. Indeed, after
dropping the unnecessary columns we wanted to describe the remaining features which are
showed in the figure 4.
Figure 4: Screenshot of the features’ description

35
As noted above the variables are defined in the table 2 and the figure 5 shows their types.
Note that ‘int64’ means integer and ‘float64’ represents a rational number.
Figure 5: Screenshot of the features’ types
For evaluation purposes we divided our dataset into training and test-set. The training set
contains 80% of the records available (948 days), the rest will be kept in the test-set. The
ordinary least squares regression results and the evaluation phase of CRISP-DM will be shown
in the next section.
Figure 6: Screenshot of train/test split of Model 1

36
7. RESULTS AND FINDINGS
The present section presents the outcomes of the conducted study on which the
recommendations will be mentioned briefly.
We would like to mention again that our research objectives are ‘(1) to find the variables that
have direct impact on the price at which the rooms are being sold and (2) build a dynamic
pricing model based on these features’. That is why this part is crucial when it comes to
identifying the features needed to reach the final step which is building our dynamic pricing
algorithm.
This research is composed of two parts. Each part will include a different statistical model. We
tried to build two different models with two different datasets in order to compare their
accuracy and choose the one with the highest performance.
7.1. Statistical Model 1
7.1.1. Regression results
The first research objective aims to find the variables that have direct impact on the
best available rate (BAR), the price at which the rooms are being sold. As noted above the
first variable that has an important weight in determining the BAR is the occupancy rate. Below,
in table 5, are shown the results of our first multiple regression model.
𝒚 = 𝜷 𝟎 + 𝜷 𝟏 𝒙 𝟏 + 𝜷 𝟐 𝒙 𝟐 + 𝜷 𝟑 𝒙 𝟑 + 𝜷 𝟒 𝒙 𝟒 + 𝜷 𝟓 𝒙 𝟓 + 𝜺
▪ 𝒙 𝟏: The occupancy rate of Novotel
▪ 𝒙 𝟐: The average occupancy rate of Hotel Concorde and Hotel Paris.
▪ 𝒙 𝟑: Weekend or weekday.
▪ 𝒙 𝟒: Ramadan or not Ramadan.
▪ 𝒙 𝟓: Holiday or not holiday.

37
Table 6: Model 1 Summary
7.1.2. Statistical significance of the model
Global significance of the model
𝑯 𝟎: 𝜷 𝟏 = 𝜷 𝟐 = 𝜷 𝟑 = 𝜷 𝟒 = 𝜷 𝟓
𝑯 𝟏: 𝒂𝒕 𝒍𝒆𝒂𝒔𝒕 𝒐𝒏𝒆 𝜷𝒊 ≠ 𝟎 𝒘𝒉𝒆𝒓𝒆 𝒊 𝒍𝒊𝒆𝒔 𝒘𝒊𝒕𝒉𝒊𝒏 [𝟏, 𝟓]
The observed value of the F-statistic is 299 with a corresponding p-value equal to 1.33e-
191 which can be interpreted as 0.00.
At a significance level of 1%, our data shows enough evidence to say that the model is globally
significant. This means that at least one independent feature has the power to predict the
occupancy rate of Le Corail Suites Hotel (𝒚).
 We reject 𝑯 𝟎 and accept the alternate hypothesis.

38
Significance of the independent features
Test statistic:
𝜷𝒊 − 𝟎
SE 𝜷𝒊
where i lies within [1,5]
According to table 5: at the 1% significance level 𝜷 𝟎, 𝜷 𝟏, 𝜷 𝟐, 𝜷 𝟑, 𝜷 𝟒 and 𝜷 𝟓 are significant.
Therefore, the variance of the occupancy rate of Le Corail Suites Hotel (𝒚) can be explained
by at least one of the features.
7.1.3. Testing and interpreting the regression parameters
A multiple linear regression was calculated to predict the occupancy rate of Le Corail
based on the occupancy rate of Novotel, the average occupancy rate of Hotel Concorde
and Hotel Paris, is the corresponding day Weekend or weekday, was it during the month of
Ramadan or not and if it is Holiday or not holiday. A significant linear regression model was
found with an F-Statistic value of 299 (p-value ~ 0.00) and an R-squared equals to 0.613. This
means that 61.3% of the occupancy rate of Le Corail is explained by our linear regression
model and the predicted occupancy, as indicated in table 5, is equal to:
Equation 4: Multiple regression equation of Model 1
𝑶𝑹 𝑪𝒐𝒓𝒂𝒊𝒍 = 𝟎. 𝟒𝟎𝟖𝟎 + 𝟎. 𝟏𝟕𝟑𝟔 (𝑶𝑹 𝑵𝒐𝒗𝒐𝒕𝒆𝒍) + 𝟎. 𝟑𝟑𝟏𝟖 (𝑶𝑹 𝑪𝒐𝒎𝒑𝒔𝒆𝒕 𝒁𝒐𝒏𝒆) − 𝟎. 𝟎𝟓𝟑 (𝒊𝒔 𝒘𝒆𝒆𝒌𝒆𝒏𝒅)
− 𝟎. 𝟏𝟕𝟏𝟑(𝒊𝒔 𝒓𝒂𝒎𝒂𝒅𝒂𝒏) − 𝟎. 𝟎𝟓𝟖𝟗 (𝒊𝒔 𝒆𝒗𝒆𝒏𝒕)
Where:
▪ All occupancy rates are measured in percentage
▪ is_weekend: Weekend = 1 or weekday = 0
▪ is_ramadan: Ramadan=1 or not Ramadan=0
▪ is_event: Holiday = 1 or not holiday = 0
Based on this regression model, Le Corail Suites Hotel occupancy rate increases by 17.36% for
every increase by 1 unit in the occupancy rate of Novotel. It also increases by 33.18% for every
increase by 1 unit of the average occupancy rate of both Hotels, Concorde and Paris. In
addition, Saturdays and Sundays will penalize the occupancy rate of Le Corail by 5.3%, and a
holiday will penalize it by 5.89%. Also, each day during the month of Ramadan will decrease
the occupancy rate of Le Corail by 17.13%.

39
7.1.4. Model Assessment
The obtained multiple regression is a well-fitting statistical model since the predicted
values are close to the actual ones. Indeed, even though our model is based on five predictors,
the difference between the R-squared and the adjusted R-squared is too small (2%).
The following figure illustrates how well our model can generalize, the results interpreted during
the training phase, to out-of-sample data through analyzing the variance of the error.
Figure 7: Residuals plot of the Model 1
In the two-dimensional figure above, we can see that the uniform distribution of the residuals
against the target is fairly random. This indicates that our linear model is performing well.
The histogram shows that the error is normally distributed around zero, this means that we have
a well-fitted statistical model.
To be more precise in our study, we chose also to measure some evaluation metrics on our test
set as shown below in figure 8.

40
Figure 8: Screenshot of the evaluation metrics
The Mean Absolute Error (MAE) treats all errors equally and thereby it is not sensitive to outliers
(Larose & Larose, 2015) but it doesn’t punish larger errors. Thus, we measured the Mean
Squared Error (MSE). Lastly, we measured the Root Mean Squared Error (RMSE) as it is more
popular than MSE and it is easily interpretable in the ‘y’ units.
7.2. Statistical Model 2
As noted above, in this second model, we wanted to try a different dataset that
includes the same data as the first model but only for the period going from 01/01/2018 to
31/01/2019.
We made this choice because we have seen that the market quoted many changes since
2016. For the sake of example, another giant competitor entered the market during 2018 which
is ‘Movenpick Hotel du Lac’. This influenced dramatically the market share of lodgings in the
region of les Berges du Lac.

41
Figure 9: Correlation between the different features of Model 2
When we conducted correlation analysis for this second model, we have detected a problem
of multicollinearity between the two explanatory variables ‘OR Compset’ and ‘OR
Compset_Zone’ (the correlation coefficient is equal to 0.95). As mentioned previously, the ‘OR
Compset’ corresponds to the average occupancy rate of three hotels in the region of Tunis
(Hotel Belvedere, Novotel Tunis and Ibis Hotel) and the variable ‘OR Compset_Zone’ presents
the average occupancy rate of two hotels located in the region of les Berges du lac (Hotel
Concorde and Hotel Paris).
Even though we know that correlation does not imply causation, we noted in the literature
review section that according to (Ivanov, 2014) competitors’ actions and occupancy rates
have a significant impact on our hotel’s key performance indicators. Based on this idea, we
decided to drop the feature ‘OR Compset’ from our model as we believe that the average
occupancy rate of the two hotels situated in les Berges du Lac ‘OR Compset_Zone’ has a
larger impact on our hotel’s occupancy rate than the impact of the three other hotels situated

42
in the region of Tunis. Again, this is due to the nearness factor. As stated by Cho, Lee, Rust, &
Yu (2018) closer hotels or lodgings located in the same area tend to have a larger impact on
each others performance than the impact of other farthest hotels.
7.2.1. Regression results
As noted above, from this second model, our fundamental objective is to compare the
results with the first model to decide whether to keep the first one or the second one. Below,
in table 5, are shown the results of our second multiple regression model.
𝒚 = 𝜷 𝟎 + 𝜷 𝟏 𝒙 𝟏 + 𝜷 𝟐 𝒙 𝟐 + 𝜷 𝟑 𝒙 𝟑 + 𝜷 𝟒 𝒙 𝟒 + 𝜺
▪ 𝒙 𝟏: The average occupancy rate of Hotel Concorde and Hotel Paris.
▪ 𝒙 𝟐: Weekend or weekday.
▪ 𝒙 𝟑: Ramadan or not Ramadan.
▪ 𝒙 𝟒: Holiday or not holiday.

43
Table 7: Model 2 Summary
7.2.2. Statistical significance of the model
Global significance of the model
𝑯 𝟎: 𝜷 𝟏 = 𝜷 𝟐 = 𝜷 𝟑 = 𝜷 𝟒
𝑯 𝟏: 𝒂𝒕 𝒍𝒆𝒂𝒔𝒕 𝒐𝒏𝒆 𝜷𝒊 ≠ 𝟎 𝒘𝒉𝒆𝒓𝒆 𝒊 𝒍𝒊𝒆𝒔 𝒘𝒊𝒕𝒉𝒊𝒏 [𝟏, 𝟒]
The observed value of the F-statistic is 223.3 with a corresponding p-value equal to 5.11e-96
which can be interpreted as 0.00.
At a significance level of 1%, our data shows enough evidence to say that the model is globally
significant. This means that at least one independent feature has the power to predict the
occupancy rate of Le Corail Suites Hotel (𝒚).
 We reject 𝑯 𝟎 and accept the alternate hypothesis.

44
Significance of the independent features
According to table 6: at the 1% significance level 𝜷 𝟎, 𝜷 𝟏, 𝜷 𝟐, 𝜷 𝟑 and 𝜷 𝟒 are significant.
Therefore, the variance of the occupancy rate of Le Corail Suites Hotel (𝒚) can be explained
by at least one of the features.
7.2.3. Testing and interpreting the regression parameters
A multiple linear regression was calculated to predict the occupancy rate of Le Corail
based on the average occupancy rate of Hotel Concorde and Hotel Paris, is the
corresponding day Weekend or weekday, was it during the month of Ramadan or not and if
it is Holiday or not holiday. A significant linear regression model was found with an F-Statistic
value of 223.3 (p-value ~ 0.00) and an R-squared equals to 0.713. This means that 71.3% of the
occupancy rate of Le Corail is explained by our linear regression model and the predicted
occupancy, as indicated in table 6, is equal to:
Equation 5: Multiple regression equation of Model 2
𝑶𝑹 𝑪𝒐𝒓𝒂𝒊𝒍 = 𝟎. 𝟑𝟑𝟑𝟏 + 𝟎. 𝟔𝟏𝟗𝟑 (𝑶𝑹 𝑪𝒐𝒎𝒑𝒔𝒆𝒕 𝒁𝒐𝒏𝒆) − 𝟎. 𝟎𝟒𝟕𝟔 (𝒊𝒔 𝒘𝒆𝒆𝒌𝒆𝒏𝒅) − 𝟎. 𝟏𝟏𝟒𝟑(𝒊𝒔 𝒓𝒂𝒎𝒂𝒅𝒂𝒏)
− 𝟎. 𝟎𝟓𝟑𝟑 (𝒊𝒔 𝒆𝒗𝒆𝒏𝒕)
Where:
▪ All occupancy rates are measured in percentage
▪ is_weekend: Weekend = 1 or weekday = 0
▪ is_ramadan: Ramadan=1 or not Ramadan=0
▪ is_event: Holiday = 1 or not holiday = 0
Based on this second regression model, Le Corail Suites Hotel occupancy rate increases by
61.93% for every increase by 1 unit in the average occupancy rate of both Hotels, Concorde
and Paris. Contrarywise, Saturdays and Sundays will penalize the occupancy rate of Le Corail
by 4.76%, and a holiday will penalize it by 5.33%. Also, each day during the month of Ramadan
will decrease the occupancy rate of Le Corail by 11.43%.
7.2.4. Model Assessment
The obtained multiple regression is a well-fitting statistical model since the predicted
values are close to the actual ones. Indeed, even though our model is based on five predictors,
the difference between the R-squared and the adjusted R-squared is too small (3%).
The following figure illustrates how well our model can generalize, the results interpreted during
the training phase, to out-of-sample data through analyzing the variance of the error.

45
Figure 10: Residuals plot of the Model 2
In the two-dimensional figure of this second model, we can see also that the uniform
distribution of the residuals against the target is fairly random. This indicates that our linear
model is performing well. The histogram shows that the error is normally distributed around zero,
this means that we have a well-fitted statistical model. Also, the validation R-squared value is
larger than the training R-squared, this indicates that this model has the ability to generalize to
out-of-sample data.
For benchmarking purposes, we chose also to measure some evaluation metrics on our test-
set as shown below in figure 8.
Figure 11: Screenshot of the evaluation metrics

46
As shown below the two models are very close in terms of error. The second model has a slightly
better MAE and RMSE. Despite the fact that the evaluation metrics showed that the second
model is better than the first one, in the next section we will choose to continue working with
Model 1 as we believe that it is trained with more data and it has a better ability to generalize
future information.
Figure 12: Comparison between the evaluation metrics of both models
7.3. Dynamic Pricing Model
As mentioned previously, we are aiming ‘(1) to find the variables that have direct
impact on the price at which the rooms are being sold and (2) build a dynamic pricing model
based on these features. Up to now, we have found the variable that influences the price
directly which make us capable to build our pricing model.
Before describing our dynamic pricing algorithm, we wanted to find out the difference
between a fixed pricing strategy and a dynamic one. Fixed pricing in the hospitality industry
means that the hotel maintains a constant price for its perishable inventory leaving no room
for rate bargaining or negotiation. Dynamic pricing is based on a stochastic demand
approach. This is called a time-based pricing technique which allows us to set room rates as

47
per the time, use an automatic algorithm that increases and decreases depending on
demand and supply. Indeed, this can potentially increase the revenue.
A dynamic pricing algorithm must be set and followed in order to take the right pricing
decisions while foreseeing future market demands. In the previous sections, we have already
built the foundations of our dynamic algorithm.
As our General Manager struggles every day to find rates that maximize the hotel’s revenue
toward the changing market demands, our goal is to build a decision-making tool to help him
make informed and accurate decisions at the right time to optimize profitability. This makes
him able to sell his perishable rooms inventory at highest possible rates.
From now on, room rates will be changed frequently based on special events (including
Ramadan), day of the week and last year’s same day average occupancy rate of
competitors in the same region. By adjusting room prices in response to demand and supply
changes, we are aiming to achieve a balance between overpricing and underpricing. This
will help us identify the optimal rate and assign the right price at the right customer at the right
time. To be more precise, we will explain how the dynamic pricing algorithm works. Before
proceeding, it is important to know that Le Corail Suites Hotel communicates the rates that it
will use all year long to the NOTT, the rates are split into six categories and summarized in the
table below.
Table 8: Rates deployed per type of room
Code Description Rack Rate BAR 1 BAR 2 BAR 3 BAR 4 BAR 5
CC Standard Room 600 520 480 450 420 390
CP Prestige Room 620 540 500 470 440 410
SC Superior Room 640 560 520 490 460 430
SJ Junior Suite 740 590 550 520 490 460
SE Executive Suite 780 620 580 550 520 490
SP Premium Suite 950 850 810 780 750 720
SR Royal Suite 1600 1200 1200 1200 1200 1200
*Rates are per room per day in Tunisian dinars.
For a first step, the General Manager can fix thresholds, and set a bunch of conditions. Hence,
for a given daily forecasted occupancy rate, the algorithm automatically assigns a specific
rate level considering the threshold fixed a priori by the General Manager or the revenue
manager. For the sake of example, let us suppose that the thresholds are the following: less
than 60%, between 60% and 70%, between 70% and 80%, between 80% and 90%, between
90% and 95% and lastly more than 95% for the respective rate levels BAR 5, BAR 4, BAR 3, BAR
2, BAR 1 and rack rate. In fact, for a forecasted occupancy rate of 59%, the rate will be

48
automatically set to BAR 5. Contrarywise, if the predicted occupancy for a given day is 95%,
the rack rate will be selected. As noted above, this dynamic pricing algorithm can be
customized using any conditions desired by the top management.

49
8. RECOMMENDATIONS
The study objectives of this project are ‘(1) to find the variables that have direct impact
on the price at which the rooms are being sold and (2) build a dynamic pricing model based
on these features. Therefore, after finding the variables that influence the Best Available Rate
(BAR) at which the rooms are being sold, verifying the relationships and building the dynamic
pricing model we have a couple of recommendations that we would like to propose to Le
Corail Suites Hotel management.
Maintain organized records of key data
Data is key in order to fructify the revenue management efforts, but it has to be reliable.
Top Hotel, the information system of Le Corail Suites Hotel, collects a lot of inappropriate data.
Indeed, we recommend to the management to focus on the most essential information, in
other words, focus on quality over quantity and on how the data recorded will be used.
Not exclusively will it be simpler to store and process the information, yet it will be quicker, and
it will prompt increasingly important insights (i.e. daily pickup rate and competitors’ prices).
These insights will drive the whole analytics to the next level.
Improve data quality
It is important to mention that up to now, Le Corail Suites Hotel is getting the
competitors’ average daily rates and occupancy rates through a phone call realized by the
Night-Watchman. Even though, we believe that competitors will not deliver false information
to our Night-Watchman because they also need the OR and ADR to establish their operational
objectives, this leaves a space for the reader to say that the reliability of the data given by the
competitors could be questionable. In fact, we have found a way to make the data
exchanged between hotels transparent. According to a study conducted by Zhang, Li, Pan,
& Zhang (2018), hotels in the USA use an integrated software in their information systems called
STR market data. This software shares their daily occupancy rates between eachother to make
them able to forecast their KPIs accurately.
Chained hotels like Movenpick Hotel du Lac or Four Season Gammarth are obliged to buy STR
and integrate it in their systems for benchmarking purposes but we believe that STR report
would not be efficient if other hotels in the same area will not buy it and integrate it. Indeed,
even though some international lodgings located in Tunis can share their occpancies through
STR, more than a half of the information will be missing as they will not able to benchmark their
KPIs with other hotels located in the same area. For this reason, the National Office of Tunisian

50
Tourism or the ministry can intervene by creating a tool like STR reports and make it available
to all tunisian hotels in order to improve the performance of the tourism industry as a whole.
Ensure rate parity across different distribution channels
While the management of le Corail Suites Hotel is trying hard to maintain the same
room rates across all online distribution channels through a solution called ‘SiteMinder’, we
believe that there still room for improvement. Even though, SiteMinder helps the management
to allocate inventories and set the same prices across different OTAs, wholesalers like Expedia
are shaving their own margins to sell at prices lower than competitors and the hotel’s website.
It is important to note that Le Corail Suites Hotel needs OTAs to increase reach so shying them
away does not seem to be the best solution. Measures like putting in place strong contracts
with wholesaler OTAs or partnering directly with more channels so that they do not need to
buy from wholesalers can help Le Corail’s management to ensure rate parity and avoid
problems or tension between, at least, different online distribution channels. Other measures
can be taken also, like investing in a tool called ‘Parity Insight’ that helps monitoring and
enforcing online rate parity and allows to uncover discrepancies across different OTAs.
Offer direct booking incentives
As noted in the company description section, Le Corail Suites Hotel sells its perishable
rooms inventory through different channels such as Online Travel Agencies (OTAs) like
Booking.com and Expedia. These OTAs have important commissions (20 to 23%) that are
eating into the hotel’s profit margins. It is important to mention that these online distribution
channels cannot be abandoned but direct bookings are more desirable when it comes to
guest reservations.
Direct bookings help stimulate guests’ loyalty. Le Corail Suites Hotel’s management can
improve the total number of direct bookings by giving-away incentives that add real value to
customers. These can incorporate anything, from price reductions, food and beverage
discounts, lower rates for long stays and so forth. The objective is to create traffic to the hotel’s
website and avoid the customers from comparison sites where there is a possibility that the
client will choose a competitor. To be more specific, there is a module named ‘Triptease’
which can be integrated into the reservation engine of Le Corail Suites Hotel website. This small
software guarantees that website visitors will benefit from the lowest rates available and
creates instantaneous pop-ups with exclusive discount. Indeed, ‘Triptease’ pushes customers
to take actions immediately.

51
9. CONCLUSIONS
The four-month internship at Le Corail Suites Hotel allowed us to acquire a lot of
knowledge, both on technical (Python and Excel) and non-technical (critical thinking, time
management etc.) sides.
This study was conducted to help the top management of Le Corail Suites Hotel know the
factors that affect their room rates. A demand variable called occupancy rate was predicted
using a multiple regression model based on five features to help in setting the dynamic pricing
algorithm. A framework called CRISP-DM was followed with some minor modifications. Indeed,
the analysis went into five major phases: business understanding, data preparation, data
understanding, modeling and assessment using different evaluation metrics.
As a matter of fact, the data collection and the data preparation phases took a lot of time.
To be more precise, the night-watchman records were not well organized in the CSV files that
is why we tried to mention some improvements regarding data collection in section 8.
Whereas for the data understanding and data visualization parts, included in the methodology
section, allowed us to interpret some valuable relationships. In fact, the charts showed clear
relations between the variables studied.
In the modeling phase, we needed to build two different models to have a clear rationale
through the assessment metrics that we have used during the evaluation phase.
The application of the proposed model on our hotel provided a remarkably good
approximation to the hotel’s real occupancy rates. This led to a successful and large
improvement of the profit emitted by the room division. Contrarywise, the improvement is
sensitive to statistical prediction errors, such as errors due to wrong occupancy rate
forecasting.

52
REFERENCES
Cho, S., Lee, G., Rust, J., & Yu, M. (2018). Optimal Dynamic Hotel Pricing. Georgetown
University.
Ivanov, S. H. (2014). Hotel Revenue Management: From Theory to Practice. Zangador.
Larose, D., & Larose, C. (2015). Data mining and predictive analytics.
Mohammed, I., Guillet, B. D., Schuckert, M., & Law, R. (2015). An Empirical Investigation of
Corporate Identity Communication on Hong Kong Hotels’ Websites. Journal of
Hospitality Marketing & Management.
Saleh, M., Atiya, A., & Habib, H. (2013). Dynamic pricing for hotel revenue management using
price multipliers. Journal of Revenue & Pricing Management.
Schwartz, Z., & Cohen, E. (2004). Hotel Revenue-management Forecasting: Evidence of Expert-
judgment Bias.
Warren, R. N. (2017). Occupancy forecasting methods and the use of expert judgement in
hotel revenue management. Ames, Iowa, USA.
Zhang, M., Li, J., Pan, B., & Zhang, G. (2018). Weekly Hotel Occupancy Forecasting of a Tourism
Destination. Sustainability.
Zheng, T., Bloom, B. A., Wang, X., & Schrier, T. (2012). How do Less Advanced Forecasting
Methods Perform on Weekly RevPAR in Different Forecasting Horizons Following the
Recession? Tourism Analysis.

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