The document analyzes cancellations at IndoCabs, a cab service company in India. The author processed a dataset of 1,956 bookings from IndoCabs to remove duplicates and errors. Key findings include: the average trip duration was 4.47 hours, the average booking window was 2.08 days, and 8.79% of bookings were cancelled. Point-to-point travel had the highest cancellation rate. Mobile bookings saw a higher cancellation rate than online bookings. Most cancellations occurred with booking windows of 1 day or less. The author recommends IndoCabs offer discounts for earlier bookings and revamp their mobile booking and phone services to reduce cancellations.

1. Analysis of Cancellations at a
Cab Portal Company
MAXIMILIAN DRESCHER
WISCONSIN SCHOOL OF BUSINESS
OCTOBER 2017

2. 1
Executive Summary
We undertook an investigation of cancelations at IndoCabs, a cab service company
located in India. We analyzed a specific set of IndoCabs data and manipulated this data
in various ways in order to try and learn more about IndoCabs and its cancelation
problems. The first steps we undertook in order to properly analyze our data was to
process our data. We processed our data by removing duplicates, removing erroneous
data records, and by removing erroneous booking dates. We further processed our data
by creating additional data variables such as “Booking Window”, “Duration,” “Weekday”,
and “Hour” in order to get a deeper and more complete understanding of our IndoCabs
data. My specific data set consisted of 1,956 bookings and I used this set to then
summarize trip duration and booking window. Within my specific data set, the average
trip duration (in hours) was 4.47 hours, with a maximum of 184.00 hours and a minimum
of 0.12 hours. Furthermore, the average length of the booking windows (in days) was
2.08 days, with a maximum of 32.23 days and a minimum of 0.00 days. We then used all
of this information to turn to the main problem at IndoCabs: cancellations. As stated
before, the total number of bookings in my specific data set was 1,956 and out of those
172 were canceled, culminating in a 8.79% of all bookings cancelled. The cancelation
patterns by travel type revealed that point to point travel or travel type 2 had the highest
percentage of cancelations compared to number of bookings under each travel type. We
also looked at cancelations by booking channel and found that 12.74% of online
bookings and 21.05% of mobile bookings are ultimately cancelled, meaning that a
greater percentage of users cancel mobile bookings. In my sample, the cancellation
patterns by trip start day were highest on Monday, Thursday, and Sunday, which is not
very revealing. By creating histograms, we looked further at how booking windows affect
cancellations, as this could be a key area for strategic improvement. We found that the
vast majority of bookings have a window of between 0 and 1 days at 76.48% and more
specifically we found that 34.71% of bookings have a window of 0 to 0.25 days and
23.52% have a window of 0.25 to 0.5 days. This highlights that most our specific data set
has a booking window of one day or less. Furthermore, the percentage of bookings that
are cancelled with windows between 0 and 1 days is 79.65%, conversely, the
percentage of bookings that are not cancelled with windows between 0 and 1 days is
76.18%. The percentage of bookings that are cancelled with windows between 0 and
0.25 days is 55.23% and the percentage of bookings that are not cancelled within the
same window is 32.74%. Analyzation of the percentage of bookings that are canceled
within windows of 1 day or less shows that the majority of trips are canceled when they
have a booking window of 1 day or less, meaning that IndoCabs could reduce overall
cancellations by encouraging earlier booking. Through probability analysis I also found
that the probability that a trip is cancelled or made via telephone is 22.03%. Additionally,
the probability that a trip is cancelled and made via telephone is 1.76%. Lastly, if 1 out of

3. 2
4 customers upgrades to a deluxe account and a cancellation occurs, the probability that
the customer holds a deluxe account is 14.29%. Overall, IndoCabs clearly has a
cancellation problem and the best way to go about fixing this issue is to revamp their
phone service, since 21.05% of mobile bookings are cancelled, and encourage
customers to book early by perhaps offering a discount to those that book trips 2 or more
days in advance, since the percentage of bookings that are cancelled with windows
between 0 and 1 days is 79.65%.
Analysis
A Look at Trip Durations
Out of all the summary statistics we produced in order to effectively analyze trip durations
and booking windows, I will present three measures – average, median, range – that most
effectively communicate both of the trip duration and booking window variables. First, I
will present the average because it gives a good snapshot of the overall data. However,
due to the potential flaw of averages with the possibility of large or small outliers I will
include the median as well, which will help us measure the central tendency, and range
to help contextualize the average. The central tendency, a central value for a probability
distribution, is often measured by the median and for our specific data the median is 1.34
hours for trip duration and 0.42 days for booking windows.
Summary Statistics of Trip Duration and Booking Window
Measure Trip Duration (hours) Booking Window (days)
Average 4.47 2.08
Median 1.34 0.42
Range 183.88 32.23
The Magnitude of the Cancellation Problem at IndoCabs
After we looked at a specific set of IndoCabs data while focusing on cab cancellations we
found that out of 1956 bookings, 172 of them were cancelled. That means that 8.79% of
our bookings ended up being cancelled.
In addition to examining total cancellations at IndoCabs, we also examined cancellation
patterns by travel type. For travel type 1, which was long distance, there were 93 total
records and only 2 cancellations, so only 2.15% of long distance travel trips were
cancelled. For travel type 2, which was point to point, there were 1536 total records and
146 cancellations, meaning 9.5% of point to point travels were cancelled. For travel type

4. 3
3, or hourly rental, there were 327 total bookings and 24 cancellations, so 7.34% of hourly
rental trips were cancelled. I was expecting either point to point or hourly rental travel
types to be the most cancelled trips and they were. I was not expecting the same from
long distance because more planning was probably put into the trip by the customer as
they need to travel far. The travel type that had the most bookings was point to point and
consequently it also had the most cancelations and by percentage as well. What we also
learned through our data analysis was that the average trip duration (in hours) for each
travel type was 44.10 hours for long distance, 1.5 hours for point to point, and 6.17 for
hourly rental. This suggests that the shorter the trip is, the more likely that it will be
cancelled.
Cancellations by Travel Type
Travel Type Number of Bookings Number of Cancellations Percent
Cancelled
Long Distance (1) 93 2 2.15%
Point to Point (2) 1536 146 9.51%
Hourly Rental (3) 327 24 7.34%
As part of our data analysis, we used pivot tables and the online and mobile booking
variables to examine how cancellations might differ by booking channel. We found that
785 of our bookings occurred via online booking and 100 of those were ultimately
cancelled, so 12.74% of online bookings were cancelled. When looking at the mobile
booking channel, 95 bookings were made and 20 were cancelled, so 21.05% of mobile
bookings were ultimately cancelled within our data set. From this data one can conclude
that the mobile booking channel is very problematic as not many people create bookings
with this channel and from those that do, over 1 out of 5 cancel. Therefore, I would
suggest revamping the mobile booking channel and perhaps also creating an app to help
streamline service and gauge more interest from mobile users.

5. 4
The chart above is a chart showing the percentage of cancelled trips by the weekday.
The pattern is not what I was expecting as Thursday and Monday seem to be the days
with the highest percentage of cancelled bookings. I was expecting to see more
cancellations on the weekends when people are more likely to be traveling with IndoCabs.
The Relationship between Booking Windows, Cancellations, and Trip Timing
Above is a scatterplot used to measure the relationship between the hourly number of
bookings and cancellations. The R-squared value of the scatterplot is 0.3721. This
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Percentage of Bookings Cancelled on each
Weekday
R² = 0.3721
-5
0
5
10
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40
0 20 40 60 80 100 120 140 160
NumberofCancellations
Number of Bookings
Scatterplot of Bookings and Cancellations by Hour
Day of the Week
Percentage

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suggests that the correlation between the hourly number of bookings and cancellations
is stronger than it is weak. The correlation between these two variables is positive but not
very strong.
0
20
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160
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
NumberofBookings
Hour of Day
Number of Bookings by Hour of Trip Start
0
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35
40
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
NumberofCancellations
Hour of Day
Number of Car Cancellations by Hour of Trip Start

7. 6
Out of these three charts, the one I found to be most informative is the percentage of
cancellations by hour. This line chart shows that there is a large percentage of
cancellations between hour 17 and 18. IndoCabs wants to solve their cab cancellation
problem, therefore, I think the chart that highlights the percentage of cancellations by
hour can help them narrow down their pain points and areas for improvement. The
patterns in the charts are similar in the fact that the number of bookings, number of car
cancellations, and percentage of car cancellations all have a peak in their charts from
around 17-18 hours.
0
5
10
15
20
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30
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
PercentageofCancellations
Hour of Day
Percentage of Cancellations by Hour
0
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1600
1 2 3 4 5 6 7 More
NumberofBookings
Booking Window by 1-days
Length of Booking Window (1-day bins)

8. 7
Out of the following histograms that show the length of booking window, I think the most
revealing is the graph with 0.25-day bins. This is because it gives the IndoCab
executives a more specific look on the length of booking windows. The overwhelming
majority of booking windows are one day long or less, therefore, the 0.25-day bins allow
for a more detailed look and highlight that the majority of bookings are actually 0.25
days long or less.
0
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800
0.25
0.5
0.75
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1.25
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1.75
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2.75
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3.75
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4.25
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4.75
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6.25
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More
NumberofBookings
Booking Window by 0.25-days
Length of Booking Window (0.25-day bins)
0
50
100
150
1 2 3 4 5 6 7 More
NumberofBookings
Booking Window by 1-days
Percentage of Cancelled Bookings (1-day bins)

9. 8
0
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1 2 3 4 5 6 7 More
NumberofBookings
Booking Window by 1-days
Percentage of Non-Cancelled Bookings (1-day bins)
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0.25
0.5
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More
NumberofBookings
Booking Window by 0.25-days
Percentage of Cancelled Bookings (0.25-day bins)
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0.25
0.5
0.75
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More
NumberofBookings
Booking Window by 0.25-days
Percentage of Non-Cancelled Bookings (0.25-day bins)

10. 9
After reviewing these four histograms that show the percentage of cancelled and non-
cancelled bookings by either 1-day or 0.25-day bins, booking windows appear to impact
cancellations. The shorter the booking window the more likely it is to be cancelled. The
pair of histograms that are most helpful in communicating these insights are the 0.25-
day bins, as they give a more detailed and complete look at booking windows and
cancellations.
Elevator Charts
From all the charts that I have produced, I would choose the percentage of
cancellations by hour, length of booking window (0.25-day bins), and percentage of
cancelled bookings (0.25-day bins) to share in an elevator pitch. First, I would choose
the percentage of cancellations by hour because the line graph shows that there is a
very large percentage of cancellations between hour 17 and 18. IndoCabs wants to
solve their cab cancellation problem, therefore, I think the chart that highlights the
percentage of cancellations by hour can help them narrow down their pain points and
areas for improvement. Next, I would choose to show the length of booking window
(0.25-day bins) histogram. This is because it gives the IndoCab executives a more
specific look on the length of booking windows. The overwhelming majority of booking
windows are one day long or less, therefore, the 0.25-day bins allow for a more detailed
look and highlight that the majority of bookings are actually 0.25 days long or less.
Lastly, I would show the percentage of cancelled bookings (0.25-day bins). This is
because booking windows appear to impact cancellations and the 0.25-day bins offer a
detailed look at shorter booking windows resulting in more cancellations.
Notes on Data Preparation
The data preparation process we undertook consisted of various steps in order to clean
the data and make sure it was free of errors. Some of the errors we fixed consisted of
dealing with duplicates, fixing dates, eliminating negative booking windows and more.
Since IndoCabs have stated that their record keeping system can generate errors, we
first started by removing duplicate observations. We removed duplicates by using the
Excel function to do so after creating a new “ProcessedData” worksheet from our
“OriginalData: worksheet. Next, we removed erroneous date records by deleting all the
entries that were recorded before Jan 1, 2013 using the filter functions. We also created
some new variables such as “from_date”, “DayofWeek”, “Hour”, “Duration”, and
“Booking Window” to help further organize our processed data. Overall, we removed
duplicates, fixed dates, removed negative booking windows, and prepared some new
variables in order to complete the data cleaning process. The original data was of low
quality as there were many duplicates and erroneous data records in my specific data
set. In order to properly analyze data and make the necessary changes to one’s
company, one must first have quality data. Therefore, I suggest that IndoCabs reinvest
in their data team and fix their record keeping system so it no longer generates errors.

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