E-Taxi Automated Pricing System

MASTER IN BUSINESS MANAGEMENT PROGRAM –
BUSINESS ANALYTICS
Final report – PRJ600
DYNAMIC PRICING MODEL OF E-TAXI APP
BY
FIRAS GUEZGUEZ
ACADEMIC SUPERVISOR
Pr. AMOR MESSAOUD
COMPANY SUPERVISOR
SADOK GHANNOUCHI
Tunis, 2015-2016

ii
APPROVAL
APPROVED BY
SUPERVISOR
Name Signature Date
COMPANY SUPERVISOR
Name Signature Date
ACADEMIC EVALUATOR
Name Signature Date

iii
DECLARATION
I certify that I am the author of this project and that any assistance I received in its preparation
is fully acknowledged and disclosed in this project. I have also cited any source from which I
used data, ideas, or words, either quoted or paraphrased. Further, this report meets all of the
rules of quotation and referencing in use at MSB, as well as adheres to the fraud policies listed
in the MSB honor code.
No portion of the work referred to in this study has been submitted in support of an application
for another degree or qualification to this or any other university or institution of learning.
Student Name Signature Date

iv
WORK TERM RELEASE FORM
I hereby state and verify by my signature that I have reviewed this internship report. I hereby
affirm that the report contains
no confidential data/information, and I authorize it to be released.
Confidential data/information, and I do not authorize it to be released.
COMPANY SUPERVISOR
Name Signature& Company seal Date

ABSTRACT
This paper provides the first comprehensive analysis of the E-Taxi automated pricing model.
Based on both anonymized in-depth interview and survey data, a ride scoring system has been
developed in order to estimate the fare to be charged to the users for each taxi ride.
One of the biggest challenges with launching a marketplace business is to grow the supply
and demand at the same pace, but the ingenious thing about E-Taxi’s model is that the
starting position is a win-win for the two actors: When a taxi services startup launches a
marketplace business, it starts with an over-supply of drivers who would be commuting to work
anyway. However, as more and more people start using E-Taxi’s platform, supply and demand
should stabilize and find an equilibrium with the right number of taxi drivers and passengers.
Therefore the approach setting fare is crucial in the development of E-Taxi service and
therefore the growth of the startup.
The first step to build an accurate approach fee estimator is to understand the reasons behind
the rejection of taxi drivers of some rides. Consequently, the major part of this research paper
will be focused driver side.
Keywords: Dynamic pricing model, rejection rate, approach fee, startup, premium, service
fee, ride fare

vi
ACKNOWLEDGEMENTS
The internship opportunity I had with E-Taxi was a great chance for learning and
professional development. Therefore, I consider myself as a very lucky individual as I was
provided with an opportunity to be a part of it. I would like first to express my deepest
gratitude to my friend Sadok Ghannouchi who invested his full efforts in making my journey in
E-Taxi successful. Furthermore, in spite of being extraordinarily busy with his duties he made
always sure that I did not miss anything in order to complete my mission.
I would like to express my deepest appreciation to my academic supervisor Pr. Amor
Ben Messaoud, who has the attitude and the substance of a genius: he continually and
convincingly conveyed a spirit of adventure to this research. Without his guidance and
persistent help this dissertation would not have been possible
I would like to thank Mrs. Soumaya Ben Dhaou for her careful and precious guidance
along the internship process which was extremely valuable for my work both theoretically
and practically.
A special thanks goes to my academic reader Mrs Ramla Jarrar, who through her
positive attitude and enthusiasm made me persist and progress in my internship. Moreover,
she has been a mentor for me.
Last but not least, I would like to thank my colleagues with whom I worked hand in
hand in E-Taxi. Furthermore, they provided expertise that greatly assisted the research.

7
Table of Contents
Approval .......................................................................................................................................................ii
Declaration.................................................................................................................................................. iii
Work Term Release Form...........................................................................................................................iv
Abstract........................................................................................................................................................ v
Acknowledgements ..................................................................................................................................vi
1 Executive Summary ........................................................................................................................ 11
2 Introduction...................................................................................................................................... 12
3 Company context .......................................................................................................................... 13
3.1 Description of the company ................................................................................................. 13
3.1.1 Business Model................................................................................................................. 13
3.1.2 Value proposition ............................................................................................................ 14
3.1.3 Team.................................................................................................................................. 15
3.2 Mission and Objectives........................................................................................................... 16
3.2.1 Mission .............................................................................................................................. 16
3.2.2 Vision................................................................................................................................. 16
3.2.3 Values............................................................................................................................... 17
3.2.4 Slogan............................................................................................................................... 17
3.3 Market Structure ..................................................................................................................... 17
3.3.1 SWOT Analysis ......................................................................................................................... 18
3.4 Industry structure...................................................................................................................... 20
3.4.1 Porter’s five forces........................................................................................................... 20
4 Internship Description ..................................................................................................................... 25
4.1 Internship Context .................................................................................................................. 25
4.2 General and specific objectives of the Internship........................................................... 25
4.3 Challenges and Obstacles................................................................................................... 26
4.4 Assigned Tasks and Responsibilities..................................................................................... 27
5 Literature Review............................................................................................................................. 29
5.1 Background .............................................................................................................................. 29
5.2. Business Models for startups................................................................................................... 29
5.3. Pricing for market place Business/ two sided markets ..................................................... 30
5.4. Case study: Pricing model of Uber...................................................................................... 31
5.5. Theoretical framework:.......................................................................................................... 32
6 Design/Methodology ..................................................................................................................... 33
6.1. In Depth structured interview................................................................................................ 34

8
6.2 Survey......................................................................................................................................... 35
6.2.1 Survey design ................................................................................................................... 36
6.2.2 Sampling .......................................................................................................................... 36
6.2.3 The survey administration .............................................................................................. 37
6.3 Data Preparation.................................................................................................................... 38
6.4 Multiple regression analysis.................................................................................................... 42
7 Results and finding.......................................................................................................................... 45
7.1 Findings of the in-depth interview....................................................................................... 45
7.2 Results and findings of the survey......................................................................................... 49
7.3 Estimating the regression and assessing the overall model fit........................................ 51
7.3.1 Statistical significance of the regression model....................................................... 52
7.3.2 Testing and interpreting the regression parameters: ............................................... 53
7.3.3. Model Assessment........................................................................................................... 54
7.4 Dynamic pricing model.......................................................................................................... 55
7.4.1 Regular approach fee hypothesis.............................................................................. 55
7.4.2 Rigid approach fee hypothesis ................................................................................... 56
7.4.3 Flexible approach fee hypothesis............................................................................... 57
8 Recommendations ......................................................................................................................... 58
9 CONCLUSIONS................................................................................................................................. 60
REFERENCES............................................................................................................................................... 61
Appendices:.............................................................................................................................................. 62
..................................................................................................................................................................... 62

9
List of figures
Figure 1: E-Taxi Organizational chart.................................................................................................... 16
Figure 2: Number of Habitants / Taxi per city (Le Monde) ............................................................... 18
Figure 3: SWOT Analysis Matrix ............................................................................................................... 19
Figure 4: Weekly Evolution of the Taxi Rejection rate........................................................................ 24
Figure 5: The Information given by Google maps ............................................................................. 40
Figure 6: Scatter diagram of the ratings attributed to the ride Rades Boumhel ......................... 41
Figure 7: Screenshot of the model ‘variable view on SPSS .............................................................. 44
Figure 8: Screenshot of multiple regression on SPSS .......................................................................... 44
Figure 9: Frequency bar chart of the answers to the first question of the in-depth interview. . 46
Figure 10: Frequency bar chart of the answers to the first question of the in-depth interview 47
Figure 11: Frequency bar chart of the answers to the third question of the in-depth interview.
..................................................................................................................................................................... 48
Figure 12 average attributed score to each taxi ride ...................................................................... 51
Figure 13 Real vs model curve............................................................................................................... 55
Figure 14 Regular fee hypothesis curve............................................................................................... 56
Figure 15 Rigid fee hypothesis curve .................................................................................................... 57
Figure 16 Flexible fee hypothesis curve................................................................................................ 57

10
List of tables:
Table 1: E-Taxi services fee table........................................................................................................... 14
Table 2: Features comparative table................................................................................................... 18
Table 3: Interview summary table ......................................................................................................... 35
Table 4: Summary of the independent variables .............................................................................. 39
Table 5: Summary of all variables.......................................................................................................... 42
Table 6: In-depth interview summary ................................................................................................... 48
Table 7: Average assigned score per ride .......................................................................................... 50
Table 8: Descriptive statistics of the average ratings........................................................................ 51
Table 9: Model Summary ........................................................................................................................ 53
Table 10:ANOVA....................................................................................................................................... 53
Table 11: Coefficients.............................................................................................................................. 54

11
1 EXECUTIVE SUMMARY
One of the most critical success factors for a start-up is setting a profitable pricing in
addition of being competitive. This pricing should be clear and transparent to avoid any
problem customer side. This task is even more critical for market place businesses. Indeed, the
big challenge for this type of startups is to grow supply and demand at the same place.
Consequently, failing in setting a price that satisfies both suppliers and customers will be
equivalent to signing a death wish.
E-Taxi is a one year old startup whose main activity is based on matching taxi drivers with the
passengers to overcome the taxi market inefficiencies. The company is now using a manual
pricing model where the operators are the ones responsible of setting the approach fee
charged to the customer. The problem with this method is that it does not work on a case by
case basis. In fact, the approach fee set by the company is two dinars for day rides and three
dinars for night rides this price does not take into consideration the ride ‘length, the traffic state
and the distance. This has a direct impact on the customer adoption rate since the price
charged will be too expensive especially for short rides. On the other hand, this pricing strategy
may face drivers’ rejection and so generate revenue shortfall. In fact, the fixed price strategy
may be undervalued compared to the characteristics of the rides. Therefore, it will lead to the
dissatisfaction of the drivers who will be rejecting the ride and as a result the user would not be
able to use the service.
This results and findings of this research paper recommend that the company starts using a
dynamic automated approach fee which will be dealing with the rides on a case by case
basis. This model will take into consideration several factors when assessing the monetary value
to be charged to the user. Indeed, the research showed that the factors ride’ length, traffic
state, distance between the driver and the user at the ordering time and the taxi ordering
time are responsible for 90% of the taxi rejection rate of rides. Consequently, including those
variables when estimating the approach will be satisfying both customers and drivers.
However, the top management of the company could use this pricing estimator as a base for
a larger and more exhaustive pricing model depending on the top management vision and
the brand image it wants to convey for E-Taxi. Indeed, it could incorporate other factors with
pre assigned weights in the model like the average rating attributed to both the user and the
driver, the frequency of use of the service. Those factors will serve as drivers to retain the
customers and drivers (Portail du transport, 2014).

12
2 INTRODUCTION
This research paper was conducted for the purpose of the MBM internship program of
MSB. The university second year master students are required to be placed in an operating
company for a 4 months internship. During the 2 months preceding the internship, the students
pass through a long process of searching the company to integrate. Despite the rich
professional network provided by the university, some students faced hard times finding an
internship placement opportunity.
One week before the internship placement due date, I still didn’t know where I was going to
spend the next 4 months and this was not due to a lack of opportunities. Indeed, during that
period, I was hesitant between joining an established big size company or joining a one year
old startup. This choice which seems obvious for many students left me uncertain for a while.
However, now when I assess my internship journey I can say that I do not regret my choice of
joining the startup.
My internship at E-Taxi offered me the opportunity to be surrounded by high profile employees
within an enriching work environment. It allowed me to apply the theoretical tools that I have
learned at MSB on real projects and assignments. The topic that I have been working on
enlarged my knowledge about the Business and Pricing models research domains. In fact, I
used a great number of theories and practical tools to develop my thesis. Understanding the
Tunisian Taxi market and the interactions between the major players on this market was the
initiation point to assess this project. Asking some Taxi Drivers and other stakeholders about their
vision and expectation of the taxi ride experience helped me a lot in framing the model which
is based mainly on the willingness to buy of the user and the adaptation level of the drivers to
technology in their day to day work.
The total ride fee paid by E-Taxi customers is composed of two main fees. The first one is the
ride fare which is displayed on the taximeter at the end of the ride plus a variable approach
fee. This research paper will be dealing with building a dynamic pricing model that estimates
the approach fee.

13
3 COMPANY CONTEXT
3.1 Description of the company
E-taxi is a startup founded in October 2014 by Sadok Ghannouchi. Its office is located
in Carthage Salambo Tunis. E-taxi employs 11 employees and has more than 50 taxi partners
in the region of Tunis. Sadok founded the company to overcome the inefficiency in the taxi
industry that exists in Tunisia. The start-up company uses smartphone technology and multiple
data-points to match taxi drivers and passengers. The application connects taxi drivers and
passengers allowing them to experience a fast, convenient and safe ride, at just a tap of
button. Users are able to reserve a taxi, as well as track the location of their reserved taxi as it
drives to the user’s location E-taxi is present through three different channels:
 The website www.etaxi.tn.
 The telephone 70 028 128
 The mobile app for Android, iOS and Windows Phone,
The startup was recently cited in article of Techcrunch, the leading magazine in
technological startups news. This article boosted the notoriety of the company nationwide and
internationally which pushed the team to think of expanding overseas.
3.1.1 Business Model
In efforts to cater to the growing instant gratification need, E-taxi provides an on-
demand taxi service app powered through the smartphone platform, web and phone. The
software company connects taxi drivers to passengers through the app seamlessly, making
the process user-friendly and eliminating blurry money transactions.
The principal revenue source of the startup is based on the monthly subscription fee of 50 TND.
This fee must be paid by the taxi drivers on a monthly basis to be part of the cab fleet and to
get access to the E-taxi customer base. On the other hand, the company takes a portion of
each ride it proposes to the drivers. This portion is paid by the driver and it follows the following
table:

14
Table 1: E-Taxi services fee table
The ride’ fare ( The price displayed on the
taximeter)
Fee to be paid to E-taxi
2500 – 10 000 0,500
>10 000 1 000
As showed in the table above, the ride’ fare which is displayed on the taximeter at the end of
the ride is the amount based on which the driver is going to pay the service fee to the
company. However, since the driver is the only person to know the real amount of the ride
some unethical practices may occur. Indeed, some drivers report reduced amounts to pay
less service fees.
E-taxi is also taking advantage of its large taxi fleet to use it as an advertising support. E-taxi
has more than 50 taxi partners in Tunis, each of whom is driving on average of nearly 300 Km
per day. Therefore this fleet represents a valuable asset for the company since it could be
exploited by advertising agencies as an advertising support. Those agencies need this support
due to its high mobility and so its high exposure rate. E-taxi proposes this service by renting a
bundle of taxi vehicles where the advertiser is going to stick the stickers of his contractor, On
the other hand E-taxi pays a proportion of the revenue to the owner of the Taxi.
3.1.2 Value proposition
Value proposition to the taxi drivers:
According to a study made by the ministry of transport the taxi drivers are running 40%
of their time empty, meaning that nearly half of the fuel and other maintenance costs are not
generating cash inflows. E-Taxi proposes to reduce this free time by proposing to the drivers
other alternative rides by connecting them to a platform allowing them to have access to a
larger customer base. The E-Taxi drivers are provided with a tablet with a pre-installed app
which will be used as a new work tool. Moreover, the customers proposed via the platform,
have verified profiles and known identities. Therefore, the payment and security issues faced
in normal times are less likely to occur.

15
Value proposition to the users:
Even though Tunis has one of the highest ratio of taxi per habitant worldwide (le
monde), finding a taxi nearby is still not guaranteed. E-Taxi allows its users to order a cab at
any time anywhere in Tunis at just a click of button. The user can order a taxi through three
different channels; the telephone, the web and the mobile app. In addition, E-Taxi allows its
customers to pre-order a taxi at any time during the day. Therefore, the users are guaranteed
to find a driver to pick them for important trips like for going to the airport or important
meetings.
E-Taxi is very selective when choosing its drivers. In addition the customers can check the
different drivers’ profiles with their attributed scores, car type and other users’ comments
before ordering a cab. Therefore, as opposed to traditional taxi services, the user is the one
who selects his driver.
Value proposition to the companies (B2B):
With the actual political and social instability of the country, providing a safe transport back
home for employees has became a necessity, especially for those who have employees who
finish work late night and cannot use public transportation. E-Taxi assures the scheduling,
dispatching and the transport of the employees In a safe way, at the minimum possible cost
and cash free.
3.1.3 Team
The E-Taxi team is composed of 11 employees. Mr. Sadok Ghannouchi the CEO, 3 are
in the technical department, 3 are in the operations, 1 is responsible of the marketing
department and the rest are interns divided in all the departments. However all the employees
switch from a department to another depending on the amount of work required.

16
The CEO Mr. Sadok set the strategy and the direction of the startup through weekly meetings
with all the team. He also the one responsible for the funding and partnerships. Therefore he
spends the majority of the time outside the office, the Technical department deals primarily
with the development of the app and the maintenance of the Web site. On the other hand
the operations plays the roles of middle man between Users and Taxi drivers. They basically
receive customer claims through phone calls and with the help of a platform which displays
the location of the taxis in real time they process the claim to the closest taxi driver. Finally the
marketing department is responsible for the community management and the B2B marketing
campaigns.
3.2 Mission and Objectives
3.2.1 Mission
“As a technology driven company we believe it is our mission and our duty to shape
the future of mobility in an efficient, safe and sustainable manner - with trendsetting
technologies, outstanding products and made-to-measure services”
3.2.2 Vision
“Pioneer in transportation services”
Figure 1: E-Taxi Organizational chart

17
3.2.3 Values
In the journey of E-Taxi towards its strategic vision and through seeking to achieve the
stated mission, the startup will refer to the following values system as the first cardinal values,
the source of inspiration of all stakeholders and the solid base around which projects and
policies are built constantly:
 Customer Services: Volume and profits achieved by E-Taxi through the high customer
retention rate is a real proof of the service level and satisfaction the company is
providing to the customers.
 Ethics and Treatment: the company’ reputation nearby customers and taxi drivers is a
competitive advantage and a strong proof of the sophisticated morals and high ethics
of all the staff.
 Technology and Effectiveness: The effectiveness of E-Taxi app performance is a direct
translation of a deep knowledge of the market and the extent of its reliance on
technology and results towards optimizing the ride experience.
 Satisfaction and Loyalty of employees: The Satisfaction and loyalty of E-Taxi staff is a
direct reflection of respect, empowerment and the ability to make highly important
decisions.
 Quality and Excellence: The accuracy and high level of the operations ensure the
safety and efficiency of the Taxi drivers when performing their job.
3.2.4 Slogan
“Fi Clike Ijike”:”your ride in a tap away” Throughout this slogan the company wants to
show the easiness and convenience of the experience.
3.3 Market Structure
The Taxi market in Tunisia has a huge potential. Indeed, According to the ministry of
transport there are 34 000 taxi cabs in Tunisia of which 17 000 are in Tunis region, this makes a
taxi cab for each 147 people. This market generates nearly 2.5 million TND per day. Therefore
the annual taxi activity represents 1% of the Tunisian GDP.

18
There are 6 taxi service companies operating in this market. The oldest one is Allo Taxis with
more than 15 years of existence in the market and a fleet of more than 200 taxis, followed by
Taxi-Sat the leader in the B2B segment. Then comes Tunis-Taxis which operates only in the B2B
market. Followed by the new comers E-taxi, Taxi 216 and Taxi Bibi. Unlike the first three
companies, those three startups are using new technologies in their service.
Table 2: Features comparative table
3.3.1 SWOT Analysis
This SWOT analysis of E-Taxi provides the competitive advantages the company has
over the competition. The analysis highlights the different growth opportunities and the
different critical success factors that the startup should master in order to succeed in its market.
Figure 2: Number of Habitants / Taxi per city (Le Monde)

19
This market analysis was conducted with the help of different actors in the startup ecosystem
with some personal observations. Those are some questions I asked to the actors to get a
clearer view of the market of the startup.
 What unique or lowest-cost resources can you draw upon that others can't?
 What could you improve?
 What good opportunities can you spot in this market?
 What obstacles do you face?
The following matrix illustrates the strengths, weaknesses, opportunities and threats of the
startup.
Figure 3: SWOT Analysis Matrix

20
3.4 Industry structure
Each region and more precisely each country has its own structure as different forces
or factors can play a role in shaping the competition. In order to understand the Tunisian
network transportation industry’s attractiveness and potential growth, Porter’s Five Forces
Analysis will be used in order to consider the Taxi service’ industry at the macro-level.
3.4.1 Porter’s five forces
Threats of new entrants:
Although E-Taxi has a technological edge over the competition, this does not prevent
new startups to enter the market and to have the same value proposition. E-Taxi does not have
patents to protect them from being copied. Therefore, the features offered in the app are not
protected and even though it would, the Tunisian law is not rigid in terms of intellectual
properties. As a result, E-Taxi currently does not have any protection from potential new
competitors as it has no proprietary elements that can prevent new entrants from competing
in the industry.
In terms of capital cost, E-taxi’s seed capital was 50 000 TND paid in full by the founder Mr.
Sadok Ghanouchi and ITILAQ; a Qatari investment fund. Prospective new entrants into the
industry can expect relatively low seed capital. Due to the low capital requirement to enter
the industry, E-taxi faces low protection against new entrants. Since E-Taxi does not require
membership for prospective customers and offers their app for free, there is virtually no cost to
switch services. Due to the lack of propriety elements, low initial capital requirement, and lack
of switching cost, E-Taxi faces high threat from potential entrants. These factors will likely limit
E-taxi’s profitability.
Bargaining power of suppliers:
For the type of service E-Taxi is offering, Taxi drivers are considered as the main supplier.
Since E-Taxi does not own any vehicles, their business model depends entirely on Taxi drivers
with their own vehicle. There is also no substitute for taxi drivers. In addition, Taxi drivers have
the option to pick and choose between E-taxi, rival services, or traditional taxi services.
Therefore, suppliers have the power to negotiate for a lower subscription fee. Lastly, drivers

21
face a low switching cost since they essentially pay a monthly 50 TND subscription fee plus a
Tablet at the beginning of their partnership. These factors give drivers immense bargaining
power. This is not to say E-Taxi is powerless; since there are nearly 17 000 Taxi drivers in Tunis and
E-Taxi has a better value proposition than the competition in terms of facilitating and optimizing
the drivers work, it has the power to set the desired terms and rates. Taking all factors into
account, suppliers have moderate power to impact E-taxi’s profits in the industry.
Bargaining power of customers:
Customers and consumers have amassed far more bargaining power today due to
instant access to information, especially with the rise of social medias including access to
reviews and feedback, Those feedback platforms like ‘Les Bons plans de Tunis’, ‘On a mange
pour vous’ and many others have gained a lot of notoriety in the last few years and are big
actors in shaping the behavior and consumption habits of Tunisians. In addition users have now
low switching costs via digital channels, price sensitivity, access to substitute products and
services with greater ease of use and convenience. E-Taxi knew how to take advantage of
this factor through its presence on social Medias. However those platform are double-edged
weapons since a dissatisfied customer has a higher reach and can influence many others and
so harm the company’s image. Therefore E-taxi has more responsibility towards its customers
by offering a better service.
E-Taxi offers a service that customers do not need on a daily basis. The majority of the
customers, use the service only in specific circumstances. Therefore, E-taxi customers have the
option to choose when to utilize the E-taxi app and when to go with a classic cab. Lastly, since
E-Taxi is a free app that only requires prospective users to register with them, the switching cost
for customers is quite low. When taking into consideration the low switching cost and
substitutes, it is clear that customers are more likely to be price sensitive. These factors give
buyers significant power to limit E-taxi’s potential profits.
Threats of Substitutes:
Since E-Taxi competes in the Network Transportation Industry, it has many substitutes
across the transportation industry. The closest substitute to E-taxi is Uber. Based on what
happened in several others countries Uber harmed taxi services companies. Especially when
we know that on average an E-Taxi cab costs more than Uber. Therefore an average customer
would go with an Uber cab for its lower costs and a higher service quality. On the other hand

22
Public transportation is considered as substitute that poses serious threat; in exchange for a
slower speed and a worse service public transit also offer substantially lower fare. Since E-taxi
poses virtually no switching cost, it faces very high threat of substitute from a wide array of
transportation methods especially if Uber penetrates the Tunisian market. As a result E-taxi’s
profitability is seriously impacted by the high threat of substitutes. However knowing the power
of Taxi unions and the poor service of public transportation E-Taxi would not normally face a
threat of substitute during the five upcoming years.
Rivalry among competitors:
There are many competitors in the Network Transportation Industry which E-taxi is
classified under. However the Taxi services market is divided into two sub-markets, the first is
the B2B and the second is the B2C. For the B2C market, Notable, the two biggest competitors
in the space are Taxi216 and Taxi-Bibi. The three companies use very similar business models
and suppliers in addition of the technological advantage they have over the traditional Taxi
companies. They are therefore competing not only for customers but also for suppliers.
Furthermore, the different companies target customers living in the same geographical
locations, they started all operating in Tunis. Although rivalry and competition is fierce in the
space, E-taxi has a competitive advantage compared to its two competitors, since it the only
actor offering a app allowing the customer to order a cab without picking the phone while for
the two other the user has to call the driver before ordering.
In the B2B segment E-taxi is facing a totally different competition. In this sub-market its main
competitors are Taxi-Sat, Tunis Taxi and Taxi Tunis. Those company have between 5 and 10
years of experience in the Taxi services. They are sharing all the B2B cake and dethroning them
is a real challenge. This difficulty is not attributable to the quality of service they are offering or
to the savings they are providing to their contractors but rather to their rich network of people
within the companies.

23
4. Internship Description
My internship at E-Taxi offered me the opportunity to be surrounded by high profile
employees within an enriching work environment. It allowed me to apply the theoretical tools
that I have learned at MSB on real projects and assignments. The topic that I have been
working on enlarged my knowledge about the Business and Pricing models research domains.
In fact, I used a great number of theories and practical tools to develop my thesis.
Understanding the Tunisian Taxi market and the interactions between the major players on this
market was the initiation point to assess this project. Asking some Taxi Drivers and other
stakeholders about their vision and expectation of the taxi ride experience helped me a lot in
framing the model which is based mainly on the willingness to buy of the user and the
adaptation level of the drivers to technology in their day to day work.
During the first two weeks I was shifting from the operations and IT offices to have a broad view
of the activity of the company. I looked at the different metrics and key performance
indicators to see the evolution of the startup after one year of activity and to have a clearer
idea about the growth opportunities. Moreover, during that period I was looking for the
business problem that I will cover during my internship experience at E-Taxi. After one week of
looking at the metrics, I found that the startup was facing a major problem that could harm its
image toward the users and therefore affect the customer adoption rate.
At that time period the company was relying only on the phone channel to receive the
customers’ claims, since the mobile and web apps were being developed by the IT
department. Each operator has to record any taxi order he performed on the database. At
the end of each week each operator has to make a summary of his results on an Excel sheet.
In this document, the operator has to list the number of taxi rides ordered, the number of rides
accepted and finished and the number of rides rejected or canceled, then they calculated
some ratios based on these metrics, these ratios serve as Key performance indicators of the
operations department. One of the ratios that caught my attention was the percentage of
rejected rides, for the purpose of this research paper I will call it the rejection rate. During that
particular week this rate was 21%, I thought that it was maybe particular to that week but even
when I looked to the data of the previous weeks, this rate didn’t reach a value lower than 19%,
I was shocked when I realized that nearly one customer out of five couldn’t find a taxi that
escort him to his desired destination.
This was the moment when I realized that understanding the cause of this problem and trying
to solve it would be an interesting research topic for my internship. Based on this problem I built
the research questions and ojectives.

24
Figure 4: Weekly Evolution of the Taxi Rejection rate

25
4 INTERNSHIP DESCRIPTION
4.1 Internship Context
During my internship in the Taxi startup, I was not assigned to particular department or
task but rather I was daily with the CEO working on several points. We spend most of the time
outside the office meeting with different actors of the ecosystem for different purposes.
However for most of the time spent in the office, I was taking care of building the dynamic
pricing model which required a lot of research and experiment work. This model is a critical
determinant for the success of the startup. Indeed the firm could have the best app on the
market however its success depends on major part on the price it is going to charge to its
customers. In the other side the start-up cannot charge low price since it will repulse and
discourage the drivers from joining the crew. That is why I tried to be the more exhaustive and
to not underestimate any criteria when making my research.
This dynamic premium estimator model will be a competitive advantage since it is automated,
fast and transparent and it is cheaper than the competition ceteris paribus. This premium
estimated will encourage taxi drivers to accept “bad” or “not profitable” taxi rides and
therefore it will reduce the number of dissatisfied users.
4.2 General and specific objectives of the Internship
The general objective of this internship is to explore the different tasks, duties and
responsibilities of a technological startup. Indeed, this internship offered me with deep
understanding of my future career plans. Throughout this experience, I changed the whole
plan I made for myself for the upcoming years. Observing, the dynamism and involvement of
the team inspired me and challenged all the stereotypes I had about startups ad
entrepreneurship experience.
This internship also is the first real confrontation to the professional world. Indeed during my past
professional experiences I used to work in big firms where I was a small part of a large process.
Therefore I couldn’t see the impact of my contribution to the final results and this demotivated
me. However working in a startup allowed me to see the impact of my contribution and had
a positive effect on my confidence and risk taking behavior. I become more innovative and I
take more initiatives.
The objectives of this internship are mainly applying what I learned during my two years of
experience at MSB into concrete business cases. More specifically trying to help the company

26
from the knowledge I acquired in the Business analytics subject field, especially when the
company I work in is a technological startup. Therefore it relies a lot on the data generated
from its activity. In addition this project will give me an understanding of what research is, how
to conduct research specific to a certain business problem, and how to report and
communicate the research. I will also Understand and apply basic qualitative and quantitative
research concepts, and statistical tools used in business research.
For the purpose of my internship I decided to answer the following research objective‘(1) To
identify the factors that influence the choice made by taxi drivers on whether accepting or
rejecting a given taxi ride and (2) identify the importance of each factor on that decision (3)
Build a ride’ dynamic approach fee based on these factors ’. These research objectives answer
to the following research questions ‘(1) Do taxi drivers accept all taxi rides? (2) What are the
factors that they take into consideration when making their decision about whether accepting
or rejecting a given ride? And (3) will an automated monetary contribution decrease the
rejection rate? ‘. Answering to these research questions will allow me to work on the dynamic
pricing model of the mobile app. My primarily task will be dealing with automating the
premium charged to the customer upstream. This fee will be estimated by an algorithm
developed by the CTO Mr. Nader Toukabri and it will base its computation on several
independent variables extracted from the research work done with the different stakeholders
of the market.
4.3 Challenges and Obstacles
I have faced many challenges throughout my experience at E-taxi where I was eager
to transform into learning opportunities. First, I had some problems about writing the first
intermediate report since to achieve this task, I needed the mission, vision and values. However
none of these statement existed. Therefore I proposed to Sadok to write them together. For this
purpose I needed to know how he perceives the company in mid-long terms. We met several
times and it wasn’t easy due to different other responsibilities he had. We agreed on the
mission, vision statement after 3 meetings. This task was in the benefit of the startup since each
company has to have these statements to set the company culture and objectives.
The second major obstacle was faced during the extraction of data of the times and location
of the taxi rides from the data base. For the purpose of my internship project I had to list the
most frequent taxi rides depending on their starting point, customer destination and time.
However For this task which is easy in appearance I had a real nightmare. The major problem
I faced was identifying the ride’ starting point and customer destination from the database.

27
The difficulty encountered was mainly due to the manual seizure of data by the operators. This
led to different entries for the same location. Therefore I had to go through all the locations
and identify the same entries with different writings. This task took me nearly 10 days. However
it was an occasion to remedy to this problem through pre-defined locations. Therefore each
location has only one entry regardless of the operator.
I faced some difficulties when conducting the survey with the taxi drivers. The purpose of this
survey was to rate different taxi rides extracted from the company’ database based on their
level of frequency. I took the 30 most repeated taxi rides and I broke them based on their
timing, Departure point, arrival point and the distance separating the user from the driver. At
the end I obtained a document listing these 30 observations with their unique characteristics.
Then I met with 25 taxi drivers to rate these observations. In this particular task I planned that a
single survey would take from 5 to 10 minutes to be completed and I planned all the meetings
with the drivers based on this assumption. However after the 3 first surveys I figured out that I
exceeded the planned time by 15 minutes, it was 5 minutes per respondent. This delay was
mainly due to the time I spent explaining the details and purpose of the survey since my
audience was not used to this kind of exercise. Therefore I had to extend the duration of this
exercise beyond what was planned.
4.4 Assigned Tasks and Responsibilities
Since the start of my internship, I have worked on three major projects, those three
projects were challenging and they allowed me to learn new things not precisely related to
my field of studies.
The first task I started working on was the pricing model which is also my internship project. I will
develop this part on the section 3 (project description).
The second important task was the Taxi drivers’ recruitment process. One of the biggest
challenges with launching a marketplace business is to grow the supply and demand at the
same pace, but the ingenious thing about E-Taxi’s model is that the starting position is a win-
win for the two actors : When a taxi services startup launches a marketplace business , it starts
with an over-supply of drivers — who would be commuting to work anyway — but as more
and more people start using E-Taxi’s platform, supply and demand should stabilize and find an
equilibrium with the “right” number of taxi drivers and passengers . Thereby recruiting ‘good’
taxi drivers at the beginning is a critical success factor to the viability of the company. In that
purpose, E-Taxi is very selective when it comes to recruiting taxi drivers and the process of
recruiting those drivers should be at the image of the startup vision.

28
When I started working on the recruitment of the taxi, I set an objective of maximizing the
signing rate. In that purpose, I designed all the steps that composes this process and assigned
the different E-taxi actors involved in each one. At the end, the route of the potential E-taxi
driver was clear from the starting point; visiting the office until the last step signing the contract.
We set an acceptance rate of 30% to be sure of the quality of service delivered to the end
user. We wanted to know all the details of the drivers and record them in a new dynamic
database. I built an evaluation form with predefined scale that allow the E-taxi operator
Riyadh Gharianni To assess the driver through a face to face interview based on known criteria
like his motivation, state of his car, level of French and easiness with technology. On the other
hand the Taxi driver has to fill a form of all his information. At the end of each month all the
applications are gathered and the team deliberate on which drivers we will recruit.

29
5 LITERATURE REVIEW
5.1 Background
To date, many companies have lacked an effective business model that could help
guide them in determining an appropriate market price regarding their services or products.
On the other hand, many firms have failed because they could not achieve profitability even
though they offered innovative value proposition. The need of transparent and automated
pricing models has increased with the Uberization of the modern economies, especially in the
transportation field, as it is done by Uber, Lyft, Blabla car and many others startups. Those young
companies are revolutionizing the transportation experience by proposing competitive prices
compared to traditional means of transport. Therewith, they even provide new payment
methods that are more secured and cash free. In this brief section, we’ll share a
comprehensive review of the literature that has been conducted on the business models,
pricing models and how is it applicable to transportation solutions. Moreover, this review has
an objective of factoring in all the key elements required to establish a sound pricing
approach.
5.2. Business Models for startups
The business model has been the interest topic of several business cases from Peter
Drucker in 1994 in his article “Theory of the Business”, published by the Harvard Business Review
to Andrea Ovans in her study “What is a Business Model” published in 2015 also in HBR. Drucker
was the first to introduce the concept of business model in 1994. However, he never mentioned
the term in his study. In fact, Drucker’s study about business model was a set of assumptions
about what a business will and will not do when he said “Assumptions about what a company
gets paid for” (Drucker, 1994), it was similar to what porter did when defining strategy. Drucker
was also cited in several other business researches covering the business model. For instance,
when Joan Magretta defines the business model terminology in 2002 in “Why business models
matter” as “Who is the customer?” And “What does the customer value?” It also answers the
fundamental questions every manager must ask: “How do we make money in this business?
“What is the underlying economic logic that explains how we can deliver value to customers
at an appropriate cost?” (Magretta, 2002). She answers Drucker questions. Moreover, even
though there is 8 years between the two business research papers. Both of the authors agree
on the fact that the business model concept appeared with the rise of the PCs. In fact they

30
highlight the fact that before the PC era business models were created by accident and
became clear only in the digitalization era (Drucker, 1994) (Ovans, 2015).
The 2000s have known high fascination for business models. The IBM Institute for Business Value’s
Biannual Global CEO Study has reported that senior executives across industries regard
developing innovative business models as a major priority. The business model definition has
not been challenged from the one given by Margaretta in 2002 in “why business models
matter” and its primary use was for operating companies. Until came Alex Osterwalder who
developed a new user-friendly template, which he called the business, model Canvas. This
template as opposed to the traditional one helps organizations conduct structured, tangible,
and strategic conversations around new businesses or existing ones. Therefore, it was the first
of its kind to target startups since they can use it as a tool to search for the right business model.
“The canvas’s main objective is to help companies move beyond product-centric thinking
and towards business model thinking.” (Osterwalder, 2013). He defined the business model as
‘the rationale of how an organization creates, delivers and captures value”. According to Alex
Osterwalder in his book ‘Business Model Generation’, The Business model Canvas is based on
nine building blocks, which are Customer segments, Value propositions, Channels, Customer
Relationships, Revenue Stream, Key Resources, Key Activities, Key Partnerships and Cost
Structure. In the last decade the entrepreneurial ecosystem has known the rise of a new
phenomenon named the lean startup, one of the pillars of this phenomenon is the Business
model Canvas (Ostenwalder & Pigneur, 2010). In fact, according Steve Blank in his research
“Why the lean startup changes everything “published in 2013 the lean startup is “a temporary
organization designed to search for a repeatable and scalable business model (Blank, 2013).
5.3. Pricing for market place Business/ two sided markets
Pricing is one pillar of the Business model Canvas. Indeed, it crucial for the viability of
the organization. The pricing depends on the value delivered, the cost occurred, the company
strategy and most importantly the ecosystem the company operates in. This report will be
covering two sided markets types of business. This term emerged in the early 2000s with the rise
of network economies and the democratization of the Internet. Two sided markets also called
market place are defined as markets with platforms serving two different user groups that exert
inter-group network effects on each other. Such inter-group network effects arise if on a given
platform the utility for each user on one-side changes ceteris paribus with the number of users
on the other side (Rochet & Tirole, 2004) since buyers and sellers need to be brought together
for markets to exist and gains from trade to be realized. The network effects between the two
user groups imply a pricing system on the platform that can differ from that obtained for
classical multi-product markets. According to Eisenmann et al., platform operators have to

31
face three major challenges. The most important one is that they have to find the right pricing
system for their platform (Eisenmann, Parker, & Van Alstyne, 2006). The market place business
has known a boom in the last years especially with of what experts call the Uberization of the
economy or the sharing economy. We have seen the rise of new types of companies based
on the sharing principle and taking advantage of the technological advance like Uber,
Airbnb, Lyft and many others. Unlike traditional companies, Those 2.0 Businesses have low
variable costs since their primarily role is to connect users and suppliers to fill a given need and
in return they take a percentage of the transaction. For instance, Airbnb takes 12% from the
host and 3% from the renter while Uber takes on average 15% from the driver. However, these
rates may vary from a city to another.
5.4. Case study: Pricing model of Uber
When reviewing the literature of the pricing models of companies, not much was
written on the transportation companies. The few studies conducted on that subject field were
covering mainly logistics companies. Therefore, I chose to dedicate this part as a case study
of the Uber pricing model due to its similarity with E-Taxi.
In early 2016 Uber was valued to more 60 billion Dollars, It was the first company in our history
to reach that valuation in less than 7 years. One of the strengths of this market place business
is its dynamic pricing model. Even though, several studies have been made on that subject,
there are still many misperceptions about how the model works.
Uber is an American online transportation network company that allows consumers
with smartphones to submit a trip request which is then routed to Uber drivers who use their
own cars.
Uber revenue streams are generated from the percentage it takes from the driver when the
ride is completed, this percentage ranges between 12 and 20 percent depending on the city.
Uber has several strategic business units. This research will be covering only the UberX offering
since it is the closest one to the taxi market.
Back in 2012, The Uber team in Boston noticed that the drivers were logging off the system at
1am to go home. This resulted in a high amount of unfulfilled requests especially for the
customers who requested the service late night. Consequently, this leaded to high percentage
of dissatisfied customers. To respond to this phenomenon, the Boston team had the idea of
building a dynamic pricing model which offers higher prices for drivers to stay work late night
and is used only when demand exceeds supply.
The Boston experiment has shown that both the demand and supply for the Uber marketplace
are elastic. Indeed, as ride prices increases, supply increases all things being equal. On the
other hand, the demand side has confirmed price elasticity in two different areas. First, when

32
prices go up the open to order ratios go down. On the other hand, price decreases result in
immediate demand increase.
Using the supply and demand curves to illustrate the Uber dynamic price system which plays
the role of a regulator. In fact, when demand exceeds supply, the model increases the price
level in order to reach the equilibrium. If the price regulation is not introduced, the marketplace
business will face what economists call economic shortage. As a result, Uber unfulfilled rate will
increase and most customers would be left without a ride.
Fundamentally, most critics of Uber’s pricing model fail to recognize that Uber is a true
marketplace. The majority of leading Internet marketplace companies use dynamic pricing as
a solution when confronted with a scarcity of supply.
5.5. Theoretical framework:
The comparison between Uber and E-Taxi holds in several business aspects. Both are
market places offering transportation services. Moreover, the two companies have faced high
ride’ unfulfilled rate. However, the main difference between the American ride sharing giant
and the Tunisian taxi startup is that the first has the freedom of setting its own prices while the
second is operating in a regulated market where the prices are set by transportation regulatory
agency, yet the Tunisian startup could charge the customer a premium for benefiting from the
service. Like the Uber case, this premium will play the role of regulator to set the total ride fare
at the equilibrium which will decrease the ride rejection rate.
In order to answer to this business objective we have to understand the reasons that drive the
drivers to reject a given ride. AS a result an exploratory qualitative study has been made. To
explore the reasons behind the high ride rejection rate.

33
6 DESIGN/METHODOLOGY
When tackling this project, I had to break it down into several sub-parts and each part
had its own methodology requirement depending on the stage to which knowledge about
the research topic has advanced.
I faced some difficulties relating to decisions regarding the purpose of the study, whether it is
exploratory or descriptive, research strategy whether to opt for experiments, interviews or
surveys. The complexity of the project pushed me to divide the projects into inter-related sub-
parts. This decision led to a longer duration requirement for the project. However, it
guaranteed more accurate results and a minimum level of confidence.
The primary purpose of this research is to generate more knowledge about the Tunisian Taxi
market and to illustrate the different forces that drive the behavior of taxi drivers when making
their decision. Therefore I had to conduct a basic research to understand better the
phenomena of interest and to build the pricing model based on the research findings. This
model comes to bring some transparency and to remedy to problems faced by customers
with other taxi companies where they had to pay two to three times what they were supposed
to.
When tackling this project, I had a brief idea about the taxi market, however it was not enough
to build a pricing model or even to make informed decisions to successfully deal with the
problem. When I finished making my internship proposal and defining the research objectives
which are ‘(1) To identify the factors that influence the choice made by taxi drivers on whether
accepting or rejecting a given taxi ride and (2) identify the importance of each factor on that
decision and (3) Build a ride’ dynamic approach fee based on these factors. I asked myself
how I could start answering a market research question without even knowing the market.
Therefore I decided to conduct an exploratory qualitative research since not much was known
about the situation at hand.

34
6.1. In Depth structured interview
I interviewed twelve taxi drivers and two E-Taxi operators. I made sure that the drivers
came from different background and live in different areas to be sure that the outcomes will
be as exhaustive as possible. The purpose of this in-depth interview was to get an answer to
the research questions discussed in the part 5.2. I wanted mainly to know the factors driving
the taxi drivers whether they accept or reject a given taxi ride. I chose the taxi drivers as
respondents since they are considered as the professionals in this research subject. In addition
the factors driving their decision was the subject of the study. Therefore they were in the best
position to give me the needed information and their response would give me a deep insight
about the subject matter. On the other hand I chose to interview two phone operators working
since they are dealing on a daily basis with the customers and taxi drivers. In addition they
have a broad view on the big picture. Therefore they might be noticing some factors that the
taxi drivers will neglect or hide when answering.
I decided to opt for structured interview rather than unstructured because I knew what was
exactly the information considered relevant to the research. Therefore I built a list of
predetermined set of questions to be asked personally to the respondents.
The Interview was divided in three big parts. I started first the face to face interview by
explaining the purpose of the research and ensured the respondents about the anonymity
and confidentiality of their response and that all the outcomes were aiming to improve the
experience for the drivers and users. I highlighted the fact that I needed unbiased answers, it
was done through a joke to be sure that the respondents were at ease and relaxed and to
avoid that they do not come out with their true opinions but provide information that they think
is what I expect of them.
At the beginning of the interview I asked the respondents ‘Do you accept all the taxi rides? ’, I
then pursued by asking ‘what are the reasons that prevent you from accepting a ride?’ then I
asked them ‘Will an automated monetary contribution decrease your rejection rate?’. I was
making sure to write down each of their answer. In addition I restated and rephrased each
response I judged important to the research, this was done to make sure that I got the ideas
of the respondent as he intends to represent them. I concluded the interview by thanking the
respondents and by valorizing their contribution to the research.
After reaching the required number of respondents I gathered all the notes and started coding
the responses for each of the three questions. For the first and the third question the task was
more or less easy since they required a ‘yes’ or ‘no’ answers. Therefore, a dichotomous scale

35
was used and a nominal scale was used to the response. Concerning the coding of the
answers, I coded the ‘No’ by a ‘0’ and the ‘Yes’ by ‘1’. On the other hand, the second question
was an open question where respondents were required to list the factors that were preventing
them from accepting a given ride. For that purpose I constructed a category system that
allows all of the data to be categorized systematically. I made sure that the categories are
internally homogeneous and externally heterogeneous. I ended up by having 4 distinct
factors. Therefore I designed a simple coded frequency table to summarize all the gathered
data. The most frequent factor mentioned by the respondents was ‘The state of the traffic at
the time of the order’ and was coded ‘1’, followed by ‘the distance between the ride starting
point and ending point’ coded ‘2’, then came ‘the distance between the user and the taxi
driver at the ordering time’ which was coded ‘3’ and the least mentioned factor was ‘the taxi
ordering time’ coded ‘4’.
Table 3: Interview summary table
Question Coded answer Frequency
1: Do you accept all the
taxi rides?
0: Yes 14
1: No 0
2: what are the reasons
that prevent you from
accepting a ride
1: The state of the traffic at
the time of the order
14
2: the distance between
the ride starting point and
ending point
13
3: the distance between
the user and the taxi
driver at the ordering time
10
4: the taxi ordering time 9
3: Will an automated
monetary contribution
decrease your rejection
rate?
0 : No 0
1 : Yes 14
6.2 Survey
In order to answer to the second research objective: (2) identify the importance of
each factor on that decision’. I decided to conduct a descriptive cross sectional quantitative
research. I chose to opt for this research design since I wanted delineating the importance of

36
the factors extracted from the in-depth interview and causing the problem. In addition the
research objectives and the outcome data were requiring such a design to be used as input
in the following research activity. I used the data generated from the in-depth interview
discussed in 7.1 as an input for this research.
6.2.1 Survey design
The purpose of this survey was to assess the perception of taxi drivers of given rides
based on the factors extracted from the in-depth interview. The outcomes of this questionnaire
aim to estimate the importance of each factor when taxi drivers make the decision of
accepting or rejecting a given ride. Therefore a survey of listed distinct rides was built for this
purpose.
The operations department data base was used to extract the observations. I opted for a
sample size of 30 observations since it is the minimum sample size required by the central limit
theorem. In addition, to be sure of having a sufficient number of observations to build an
accurate model. On the other hand each component of the sample would be assessed by
the respondents in the questionnaire. Therefore it would be too time consuming to build a
survey of more than 30 questions. Especially when we know that our respondents are taxi
drivers who cannot afford to spend a lot of time answering a survey.
Concerning the factors representing each ride, only three factors per ride were mentioned.
This choice made after consulting my academic advisor Mr. Amor Ben Messaoud and Mr.
Borhene Kalboussi who has over 25 years’ experience in taxi service market. This decision was
based on the fact that the factor ‘State of the traffic’ was captured by two other factors
combined which are ‘the ride’ starting and ending points’ and ‘the ride ordering time’.
Therefore, it is decided to remove this variable to maintain a low level of complexity of the
survey.
6.2.2 Sampling
I used the cluster sampling technique when sampling the population. I opted for this
method because first of all it is a probability sampling technique and therefore it is
generalizable to the population. On the other hand choosing another probability more
representative sampling technique would give me less exhaustive results. Therefore I had a
tradeoff between representativeness and exhaustiveness. I chose the second alternative since

37
I needed a model that takes into considerations the different possible taxi rides rather than a
model representative of the population with similar taxi rides. Especially when we know that
80% of the rides recorded in the database of E-Taxi are within the same geographical cluster.
At the end of this step I designed a survey containing 30 taxi rides based on the three criteria
obtained from the first part of the research.
After obtaining the 30 typical rides to be rated, I needed to select the taxi drivers who are
going to rate them. However, I couldn’t afford to ask all the E-Taxi drivers because of their non-
availability. Therefore, I decided to ask 25 taxi drivers. The goal of having such an important
number is to obtain results reflecting the true opinion of the drivers. In this particular task I had
a dilemma between choosing a smaller sample size which could lead to biased results through
outliers. On the other hand, choosing a larger sample size would consume a lot more time and
resources. As a result, it would slow down the process of the research in addition to machining
it more complex. I opted for the simple random sampling technique since it was the most
efficient sampling technique for my research requirements. Indeed, this sampling method
allowed me to obtain generalizable results with the less amount of resources since I didn’t have
to pre-select the taxi drivers to ask. Consequently, I made sure to have always copies of my
survey to be ready when a taxi showed up in the company’s office.
6.2.3 The survey administration
After finishing the survey design and the number of respondents required was known. I
set an objective of interviewing five respondents per day. However, I had the choice of either
administering the questionnaires personally or sending them to be filled by the drivers in their
tablets. I opted for the first alternative since I preferred to guide the respondents when they
are filling the survey and clarify any doubt that the respondent might have on any question. In
addition, As opposed to mailed questionnaires, the personally administered surveys guarantee
a higher response rate and more reliable answers.
At the beginning, I thought that I didn’t have to move from the office since many drivers come
to the headquarters for different purposes. However, after two days, only three drivers came
to the office and it was bellow my forecasts. Therefore, I decided to go to my respondents to
catchup the time lost during the two first days. I knew that every day E-taxi drivers gathered in
front of Tunisia Mall to escort the employees of ZARA, Massimo Dutti and Pull and Bear. Indeed,
during that period E-Taxi signed a two months trial contract with Tunisie Textile, the franchisor
of all the cited brands. During these two months E-Taxi is committed to escort 40 employees
home at 9 pm from Tunisia Mall. Therefore, every day starting from 20:30, nearly ten Taxi cabs

38
gathered in front of the mall waiting for the employees to finish. I decided to go directly there
and to question them. In this specific task, I collaborated with Mr. Borhene Kalboussi who is
responsible of dispatching the employees into the taxi cabs in such a way that minimized the
number of taxis, the number of kilometers traveled and therefore the total cost. Consequently,
I knew the taxi who was going to leave first. Based on this information, I determined the number
and the order of drivers to be surveyed.
The typical respondent took on average 12 minutes to fill entirely the survey. Those 12 minutes
were divided in two parts. The first part was dedicated to explaining the purpose of the survey
and the research topic. I also made sure to motivate the respondent by emphasizing on the
importance of his answer on the service improvement and finally I explained the different
variables he had to look at when rating a given ride. This part took on average Seven minutes.
The second part was mainly dedicated to the filling of the survey and took on average 5
minutes. During this task, I made sure that I was the one responsible of writing down the answers
to prevent random and biased responses.
6.3 Data Preparation
After reaching the required number of respondents, I started preparing the data for
the analysis. Consequently, I set up a categorization scheme before starting entering the data.
The first step I passed through in data preparation was the data coding. Indeed, I coded the
taxi rides based on their order in the survey, meaning the first taxi ride mentioned in the survey
was coded ‘1’ and the second was ‘2’ so on and so forth until ride number 30. This was done
to facilitate the data entry process through ensuring traceability and avoiding confusion
between the taxi rides and finally to make the data ready to be used as input for the regression
analysis. On the other hand I did not have to code all the factors since they were of a common
scale and the regression model required numerical variables (interval or ratio scale) as input.
The following table summarizes the independent variables including their name, unit of
measure and the measurement scale:

39
Table 4: Summary of the independent variables
Variables Unit of measure Scale
Variable 1: ‘The state of the traffic
at the time of the order’,
Min/Km Ratio
Variable 2: ‘the distance between
the ride starting point and ending
point’
Km Ratio
Variable 3 ‘the distance between
the user and the taxi driver at the
ordering time’
Km Ratio
Variable 4 ‘the taxi ordering time’ hh:mm:ss Interval
The extracted 4 factors had different sources. In fact, the variable ‘distance between the ride
starting and ending points’ was extracted directly from Google maps. Indeed, the E-Taxi
database allowed me to have only the ride’ starting and ending points addresses, the time of
the ride and ‘the distance between the driver and the user’. Therefore I had to fill the cases
‘departure point’ and ‘arrival point’ on Google maps at the exact ordering time to know the
distance of the ride.

40
Figure 5: The Information given by Google maps
In addition Google maps allowed me to know the estimated ride duration based on the
Itinerary and the ordering time since the traffic varies depending on the time and region.
Therefore, I used the preceding information to code the variable ‘state of the traffic‘. I
measured this variable through the number of minutes spent per kilometers since using the
time spent per the ride as a measure would not give me information about the traffic.
Equation 1: Variable 2 formula
Variable 1 = Number of minutes estimated per ride
Variable 2
To ensure the accuracy of the model I decided to put the average rating attributed by the
taxi drivers for each ride instead of using the actual numbers used by each respondent.
Therefore, I obtained a spreadsheet composed by 30 lines referring to the taxi rides and 4
columns referring to the factors plus a column referring to the average rate attributed by the
drivers to each observation.
After being sure that my raw data was coded, I started entering it into SPSS Data Editor. This
process was done manually. As a result, I obtained a spreadsheet composed by 30 lines and
5 filled columns. After keying in all the data, I chose randomly 5 lines to check the coding
accuracy. In this process a followed a systematic sampling procedure. That is, every 5th
observation was checked. Fortunately, No typing error was found.
When I finished entering the data. I started the data editing process. First of all, I made sure
that there was not blank responses in the data base, which was the case. I then checked the
presence of outliers. To achieve this particular task, I opened a new SPSS Data Editor and

41
entered all the respondents’ answers for each observation. As a result, I obtained a
spreadsheet with all the answers and on which I could detect outliers. I chose to plot the ratings
of each taxi ride using scatter plots since my raw data is from an interval scale. This process
lasted a long time since I had to iterate it thirty times. In addition, I had to dig carefully into the
graphs to detect outliers. After finishing the thirty iterations I found the presence of only one
outlier. This outlier was a rating attributed to the ride Rades-Boumhel at 11pm (see the scatter
diagram below), the respondent number 26 gave the score of 8 compared to 3 as highest
given score for the other responses. After investigating the driver’s file, I found out that it was
not an error in the data entry. Indeed, the respondent is living in Mornag and is used to go
back home at 22pm Therefore. The ride Rades – Boumhel would be a perfect ride for him due
to its proximity to his home. Consequently, I decided to keep this response as it is.
Figure 6: Scatter diagram of the ratings attributed to the ride Rades Boumhel
The data transformation was the final step in the data preparation before it was ready for
analysis. So far, the model was based on four variables which are summarized in the table
number 3. Those four variables are all of ratio scale except the factor ‘taxi ordering time’ which
captures the time at which the ride is ordered. This variable is meaningless by itself it could
bring more complexity to the model. Consequently, I decided to create a new variable based
on the timing but would bring less complexity to the model (see the difference in significance
from the coefficients table in Results and findings). I chose to opt for a categorical variable
named ‘day or night’. This variables takes 0 when the timing is between 5 am and 9 pm and
takes 1 when the time is between 9pm and 5 am.

42
The other parameter that was meaningless to the model by its own is ‘the distance between
the user and the taxi driver at the ordering time’. In fact, the distance separating the user from
the closest driver has no importance if we take it as an absolute value. However, if it was
indexed to the total ride distance it would be more significant. Indeed, a taxi driver is more
willing to drive 3 Km empty to take a 10 Km ride than driving 2 Km empty to take a 3 Km ride.
Therefore, I created a new variable of a ratio scaled which I called ‘p’ like proportion. Where
p= Variable3 / Variable 2
Table 5: Summary of all variables
Variables Unit of measure Scale
Variable 1: ‘The state of the
traffic at the time of the order’,
Min/Km Ratio
Variable 2: ‘the distance
between the ride starting point
and ending point’
Km Ratio
Variable 3 ‘the distance
between the user and the taxi
driver at the ordering time’
Km Ratio
Variable 4: ‘the taxi ordering
time’
hh:mm:ss Interval
Variable 5: ‘Day or Night’
0: Day
1: Night
Nominal (Dummy)
Variable 6: ‘p’ Proportion Ratio
6.4 Multiple regression analysis
The regression model was used since our dependent variable was measured on a
continuous scale. In addition, the research objectives aim to assess the explanatory effect of
the independent variables extracted from the in-depth interview in the part 7.1 on our target
variable, the rating of the taxi rides. In this research case, a multiple regression analysis was
required. Indeed, the analysis required four independent variables. In a first step, the fact that
the rating of the taxi rides is bounded is ignored and propose model had the following
equation:
Equation 2: Multiple regression equation form
𝐘 = 𝛃𝟎 + 𝛃𝟏 ∗ 𝐗𝟏 + 𝛃𝟐 ∗ 𝐗𝟐 + 𝛃𝟑 ∗ 𝐗𝟑 + 𝛃𝟒 ∗ 𝐗𝟒 + 𝛆

43
 Y: The target value / the dependent variable ( the rating of the ride )
 β0: The model intercept.
 βn: The unstandardized regression coefficient.
 X1: The distance between the ride’ starting point and ending point in Km
(Distance.ride.KM).
 X2: The state of the traffic at the time of the order (Number.Min.per.KM.).
 X3: Day or Night
 X4: The proportion of the distance between the driver and user at the ordering time
from the total distance.
 ε: the residuals.
Hypothesis formulation:
As stated in the theoretical framework part, we want to test the predicting power of
the independent variables on our target variable ‘the score of the ride’. As this research is
based on a hypothetico-deductive method a null hypothesis must be set to be rejected in
order to support an alternate hypothesis. For our research the null and alternate hypothesis are
as follow:
Equation 3: Null hypothesis equation
𝐇𝟎 ∶ 𝛃𝟏 = 𝛃𝟐 = 𝛃𝟑 = 𝛃𝟒 = 𝟎
 H0 means that none of the independent variables is responsible of the variation of
the target variable
Equation 4: Alternate hypothesis equation
H1: at least one βi ≠ 0 where i lies within [1, 4]
 H1 means that at least one of the independent variables is responsible of the
variation of the dependent variable.
SPSS:
After formulating the hypothesis, the null hypothesis is presumed true until statistical
evidence. Therefore I used SPSS as a statistical tool in order to support the alternate hypothesis.
At this stage of the research, I had a ready SPSS data base with all needed variables to be
analyzed. Indeed, I entered the variables into the variable view screen of the software with
their required type and measure the scale. The variables were named as follow:
 The state of the traffic at the time of the order Number.Min.per.KM.

44
 The distance between the ride starting and ending points in Km
Distance.ride.KM
 Day or Night DayorNight.
 The proportion of the distance between the driver and user at the ordering time
from the total distance P
 The distance between the user and the taxi driver at the ordering time
Distance.Driver.user.
 The taxi ordering time Time.
 The average rating of the ride Avgrate
Note that the variable names in the SPSS data set are given between parentheses (see Figure
7). Figure 8 shows the SPSS regression dialog box.
Figure 7: Screenshot of the model ‘variable view on SPSS
The next step conducted on SPSS was the data analysis part. A multiple linear regression was
calculated to predict the ‘Avgrate’ based on ‘Number.Min.per.KM.’, ‘Distance.ride.KM’, ‘P’
and ‘DayorNight’.
Figure 8: Screenshot of multiple regression on SPSS

45
7 RESULTS AND FINDING
The present part presents the findings of all the research activities on which the
Recommendations and Options will be grounded. The reader will therefore find in the following
section an accurate and in-depth description of the findings resulting from the in-depth
interview, the survey and the multiple regression analysis. In the attempt of guaranteeing the
readability and respecting the maximum allowed number of words allocated to this, only the
most significant findings of the thorough “in-field” research are included.
The research activities have been conducted to achieve the research objectives: To identify
the factors that influence the choice made by taxi drivers on whether accepting or rejecting
a given taxi ride and (2) identify the importance of each factor on that decision and (3) Build
a ride’ dynamic approach fee based on these factors ’. Therefore. This part will be determinant
in knowing whether those objectives have been met or not. On the other hand, if the
outcomes of this study are conclusive, it will allow the startup to integrate the automated
dynamic pricing estimator in the next update of the app. Therefore it will have a competitive
edge over the competition.
This study is composed of three parts. Each part represents a research activity. The first part is
an exploratory quantitative research and was based on an in-depth interview made with the
different market stakeholders. The outcomes of this exploratory study served as inputs for the
second research activity. This second part was a descriptive cross sectional quantitative
research which is basically composed by a survey administered directly to the taxi drivers and
was aiming to respond to the second research objective mentioned above. The final research
part is a quantitative descriptive study which is based on the outcomes of the two preceding
researches. This research activity aims to test the predicting power of the independent
variables extracted from the in-depth interview on our target variable ‘the score of the ride
which in its turn was obtained from the survey. For this purpose, a multiple regression model
was built.
7.1 Findings of the in-depth interview
The structured in-depth interview was decomposed in three questions and was
conducted to answer to the first research question. The first question of the face to face
confrontation with the experts aims to measure if the taxi drivers accept all the requested rides.
This question was of a dichotomous scale and so required a yes no answer.

46
The following bar chart summarizes the answers of the 14 respondents to the first interview
question.
Figure 9: Frequency bar chart of the answers to the first question of the in-depth interview.
The figure above summarizes the answers to the first question of the interview which states: ‘Do
you accept all the taxi rides? ’.
According to the bar chart, all the respondents answered no to the question. We can
conclude from the findings that all the drivers don’t accept all the rides.
The second questions aims to capture the factors driving the taxi drivers on whether they
accept or reject a given taxi ride. The question asked was ‘what are the different reasons that
prevent you from accepting a ride? .This question didn’t require any predetermined answer.
Therefore, I obtained four distinct answers from the respondents. Some answers captured the
same rejection reason but were formulated differently. Consequently, they were classified
under the same factor. In order to facilitate the analysis part the factors were coded from 1 to
4 based on their frequency level. ‘The state of the traffic at the time of the order’ was coded
‘1’, followed by ‘the distance between the ride starting point and ending point’ which was
coded ‘2’, then came ‘the distance between the user and the taxi driver at the ordering time’
which was coded ‘3’ and the least mentioned factor was ‘the taxi ordering time’ coded ‘4’.
14
0
0
2
4
6
8
10
12
14
16
0 1

47
The following bar chart summarizes the answers of the 14 respondents to the second interview
questions;
Figure 10: Frequency bar chart of the answers to the first question of the in-depth interview
The figure above summarizes the answers to the second question of the interview. This bar
chart includes all the factors cited by the respondents. The fact that the least cited factor was
cited by more than 64% of the respondents strengthens the research findings since it shows
that the respondents are unanimous on the factors preventing them from accepting a given
ride.
The third question aims to measure the acceptance of the drivers towards a monetary fee as
an incentive to accept rides. The following question was asked: Will an automated monetary
contribution decrease your rejection rate?’. As the first, this question was of a dichotomous
scale and consequently required a yes or no answer.
0
2
4
6
8
10
12
14
16
1 2 3 4

48
The following bar chart summarizes the answers of the 14 respondents to the third interview
questions.
Figure 11: Frequency bar chart of the answers to the third question of the in-depth interview.
The figure above summarizes the answers to the third question of the interview. This bar chart
represents the frequency of the respondents who answered to each question. As the chart
suggests, all the drivers agree on the fact that a monetary approach fee will decrease their
ride’ rejection rate. The following table summarizes the findings of the in-depth interview.
Table 6: In-depth interview summary
Question Coded answer Frequency
Percentage of
respondents
‘Do you accept all the
taxi rides?
0: Yes 14 100%
1: No 0 0%
What are the different
reasons that prevent
you from accepting a
ride?
1: The state of the traffic at the time
of the order
14
100%
2: the distance between the ride
starting point and ending point
13
92.8 %
3: the distance between the user
and the taxi driver at the ordering
time
10
71.4%
4: the taxi ordering time 9 64.3%
Will an automated
monetary contribution
decrease your
rejection rate?
0 : No 0 0%
1 : Yes 14
100%
0
14
0
2
4
6
8
10
12
14
16
0 1

49
7.2 Results and findings of the survey
The purpose of this survey was to assess the perception of taxi drivers of given rides
based on the factors extracted from the in-depth interview. The outcomes of this questionnaire
aim to estimate the importance of each factor when taxi drivers make the decision of
accepting or rejecting a given ride.
Twenty five taxi drivers responded to the thirty questions survey. Based on the three factors
explained in the methodology section. The individual response sheets are in the appendices
section.
Table 7 shows the mean and standard deviation of the scores of each ride. They are assigned
by twenty five taxi drivers. Extreme scores of each ride are located and eliminated from the
data set using the basic approach based on quartiles. A score is considered as an extreme
value if it is greater than Q3 +1.5 IQR or smaller than Q1- 1.5 IQR, where Q3 and IQR are the
third quartile and interquartile range, respectively.

50
Table 7: Average assigned score per ride
Ride starting and ending
points
Time of
the
ride
Distance
driver-
user
Standard
Deviation
of the
scores
Average
assigned
score
Lac 1 – Mohamed V 7:30 3 1,48 5,57
La Marsa – Place Pasteur 7:35 4 1,52 6,90
La Goulette – Place 14
Janvier
10:00 5 1,13 7,29
Menzah 6 – Lac 1 19:00 3 1,01 5,95
Lac 2- La Marsa 17:30 2 1,63 5,19
Mohamed V – L’aouina 17:30 5 1,34 3,33
Lac 1 – L’aouina 18:00 6 0,39 1,10
ElManar – Manouba 7:30 2 1,12 5,71
Gammarth – Centre ville 00:00 1 0,26 9,86
Le Kram – La Marsa 22:00 3 1,08 7,57
Cite Enasser – Cite
Elkhadhra
23:00 1 0,78 7,72
Carthage – Kheir eddine
Pacha
7:00 2 1 8,00
Raoued - Gammarth 18:00 3 0,89 7,62
Bardo – Mohamed V 7:30 3 1,39 4,18
La Marsa – Place Pasteur 7:40 4 1,76 6,42
Megrine– Place 14 Janvier 10:00 5 1,12 3,40
Montplaisir – Lac 1 19:00 3 0,87 5,90
Lac 2- La Marsa 17:30 6 1,07 4,80
Mohamed V – Lac 1 17:30 5 0,98 3,75
Lac 1 – Rades 18:00 6 0,85 6,79
ElManar – Manouba 7:30 6 0,76 3,23
Lac1 – Lac 2 2:00 1 1,15 6,78
L’aouina– La Marsa 22:00 3 1,12 7,90
Cite Enasser – Cite
Elkhadhra
10:00 4 0,86 4,00
Carthage – Kheireddine
Pacha
7:00 10 0,79 5,20
Rades – Boumhel 23:00 3 0,41 9,20
Gammarth – Cite Elkhadhra 16:00 1 0,39 9,10
Mannouba – Raoued 23:00 5 0,91 3,20
Khaznadar– Mannouba 1:00 5 1,12 4,10
Megrine – Ben Arous 21:10 2 0,41 9,12

51
The following bar chart summarizes the average score attributed to each ride.
Figure 12 average attributed score to each taxi ride
The following table shows some descriptive statistics performed on SPSS of the average score
assigned to the taxi rides.
Table 8: Descriptive statistics of the average ratings
N Range Minimum Maximum Mean Std. Deviation
Avgrate 30 8,76 1,10 9,86 5,9628 2,16871
Valid N (listwise) 30
From the table above we can conclude that the average ride’ score attributed was 6 over
10 which means that globally the taxi drivers found the rides extracted from the database as
quite satisfying. However, when we take a look at the standard deviation which is equal to
2.16871 and which measures the dispersion of the observations from the mean, we can infer
that our sample is heterogeneous since the ratings of the respondents varied a lot from an
observation to another relatively to the mean. Indeed, when we dig deeper into the table
we can remark that the minimum attributed score was 1.10 versus 9.86 as maximum with rang
equals to 8.76.
7.3 Estimating the regression and assessing the overall model fit
The second research objective aims to identify the importance of the factors
extracted from the in-depth interview on the average rating assigned to the taxi rides. As a
result, we obtained the following equation:
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Attributedscore
Ride' code

52
Y= β0 + β1*X1 + β2*X2 + β3*X3 + β4*X4 + ε
 Y: the rating of the ride (Avgrate)
 β0: The intercept.
 βn: The unstandardized regression coefficient.
 X1: The distance between the ride’ starting point and ending point in Km
(Distance.ride.KM).
 X2: The state of the traffic at the time of the order (Number.Min.per.KM.).
 X3: The ride ordered by Day or by Night (DayorNight) where Day = 0 and night = 1
 X4: The proportion of the distance between the driver and user at the ordering time
from the total distance (p).
 ε: the residuals.
Table 11 shows the estimates of model parameters. It is decided to use a confirmatory
specification of the regression model as the number of independent variables is not large. That
is, we specified and controlled the set of predictors. For more details about the specification
approaches, see hair et al. (2010).
7.3.1 Statistical significance of the regression model
Global significance of the model:
𝐇𝟎: 𝛃𝟏 = 𝛃𝟐 = 𝛃𝟑 = 𝛃𝟒 = 𝟎
H1: at least one βi ≠ 0 where i lies within [0, 4]
The observed value of the test statistic is 51.760 and the corresponding p-value is equal
to 0.000. At the 1% significance level, the data provides enough evidence to conclude that
the model is globally significant. That is, at least one of the independent variables can be
used to predict the ride rate (Y).
 We reject H0 and accept the alternate hypothesis.
Significance of the independent variables:
Test statistic: β i^ - 0 where i lies within [0,4]
Se (βi ^)
 According to the table 11: β0, β1, β2, β3 and β4 are significant. The significance
levels are respectively 1%, 1%, 5%, 1%, and 1%. Therefore, the rating attributed to
the ride is explained by at least one of the variables.

53
7.3.2 Testing and interpreting the regression parameters:
A multiple linear regression was calculated to predict the rating of the ride based on
The state of the traffic at the time of the order, The distance between the ride starting point
and ending point, was the ride ordered by Day or by Night and the proportion of the distance
between the driver and user at the ordering time from the total distance. A significant
regression equation was found (F (4, 25) = 51,760, p < .000), with an R2 of ,892. Therefore, 89.2%
of the rating of the ride is explained by the regression model and the ride’ predicted rating is
equal to:
Y =7.688 +0.096(Distance.ride.KM) – 0.886(Number.Min.per.KM) + 1.248(DayorNight) - 3,928(P)
where:
 Day = 0 and Night = 1.
 Distance is measured in KM.
 P is measured in Min/km.
Table 9: Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 ,945a
,892 ,875 ,76669
a. Predictors: (Constant), p, DayorNight, Distance.ride.KM,
Number.Min.per.KM
b. Dependent Variable: Avgrate
Table 10:ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 121,700 4 30,425 51,760 ,000b
Residual 14,695 25 ,588
Total 136,395 29
a. Dependent Variable: Avgrate
b. Predictors: (Constant), p, DayorNight, Distance.ride.KM, Number.Min.per.KM

54
Based on the regression model, the ride’s rating increased 0.096 score points for each Km of
distance and decreased by 0.886 for any additional min/Km spent on the road. On the other
hand, Night weighed 1.248 score points more than day. Finally, for any positive marginal point
change in the proportion of the distance between the driver and user at the ordering time
from the total ride distance will generate a 3.928 decrease in the ride’ score. We can infer
from the standardized coefficients in tables 10 that the ride’s rating is mostly affected by the
distance between the driver and user ‘p’, followed by the charging fee whether it is night fee
or normal fee, then comes the length of the itinerary and finally the state of the traffic.
7.3.3. Model Assessment
The obtained model is well fitting regression model since the predicted values are close
to the estimated ones. Indeed, despite the fact that the model is based on four predictors, the
difference between the R² and the adjusted R² is small (< 2%)
The following curves illustrates the difference between the observed values and the estimated
ones.
Table 11: Coefficients
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. 95,0% Confidence Interval
for B
B Std. Error Beta Lower Bound Upper Bound
1
(Constant) 7,688 ,845 9,101 ,000 5,949 9,428
Distance.ride.KM ,096 ,031 ,257 3,093 ,005 ,032 ,160
Number.Min.per.KM -,886 ,422 -,198 -2,100 ,046 -1,756 -,017
DayorNight 1,248 ,359 ,268 3,479 ,002 ,509 1,987
p -3,928 ,693 -,554 -5,670 ,000 -5,355 -2,501
a. Dependent Variable: Avgrate

55
Figure 13 Real vs model curve
7.4 Dynamic pricing model
As discussed in previous sections, the total fee paid by the customer is composed by
approach fee charged to the customer will be mainly based on the rating of the ride. The
lower is the ride’s rating the higher will be the fee. Yet, the setting of this fee is a strategic
decision since the price has a big impact on the positioning of a company and on the
perception of the customers towards the brand. Consequently, setting high prices could have
a negative impact on the demand (customers) and setting low prices could have negative
impact on the supply side (taxi drivers).
I met several times with the CEO and some of the staff members to discuss this strategic
decision and to agree on the minimum and maximum fee to be charged. We agreed on
setting a minimum approach fee of 0,500 TND when the score of the ride is equal to 10 and a
maximum of 5, 000 TND when the ride’s rating is equal to 1. On the other hand, we agreed
that this premium would be always lower than the price charged by a regular taximeter. Based
on these assumptions I built three hypothesis of the fee regarding the score.
7.4.1 Regular approach fee hypothesis
The first assumption states that there is a liner relationship between the rating attributed
to the ride and the premium to be charged to the customer. Knowing that the maximum
amount to be paid is 5, 000 TND and with a minimum of 0,500 TND. Therefore, for each
additional marginal score point the amount decreases by 0,500 TND.
0
2
4
6
8
10
1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
AVERAGEATTRIBUTEDSCORE
RIDE' CODE
Real Model

E-Taxi Automated Pricing System

E-Taxi Automated Pricing System

Recommended

Recommended

More Related Content

Similar to E-Taxi Automated Pricing System

Similar to E-Taxi Automated Pricing System (20)

E-Taxi Automated Pricing System