Indocabs Report

1. Michael Mitroff IndoCabs Report.pdf
Analysis of Cancellations at a Cab
Portal Company
MICHAEL MITROFF
WISCONSIN SCHOOL OF BUSINESS
OCTOBER 2021

2. Executive Summary
When first looking at the data given to us, we realized that the data needed to be processed and cleaned up
before it was analyzed. To do this we deleted data that contained errors and data that had duplicates. The
erroneous data contained dates that were before 2013, many of these trips contained dates in the 1970s. We
then created more variables to get a better understanding of the data we were looking at. These variables
included "fromdate", "Dayoftheweek", "Duration(hours)" "Duration(days)", and “BookingWindow". In
total the data provided showed 2972 trips. To better understand these trips, we looked at information on
trip duration and booking window, including variables on the mean, standard deviation, and range. We
used the interpretation of this data to then look at the problem of cancellations at IndoCabs. The total
cancellations at IndoCabs are 263 which is 9% of all the bookings created. After analyzing our data on
travel type, we realized that point to point travel has the highest cancellation rate of 10% more than double,
long distance or hourly rental travel. This led us to then look at the cancellation by type of booking. Here
we discovered that mobile and online booking have a 13% cancellation rate, over 4% of the base
cancellation rate. We decided to analyze the proportion of bookings cancelled by day of the week. Here we
noticed some days have higher cancellation rates, but these were random and not correlated in any manner.
Finally, we analyzed the relationship between booking windows, cancellation and trip timing. We found
noticeable increases in cancellation rates from 5 to 8pm. Also discovering that most bookings have a
booking window and are cancelled in under 12 hours.
Introduction
IndoCabs is currently experiencing a problem with company induced cancellations that are stemming from
an unknown source. In order the combat this issue we have been given data on cancelled and noncancelled
trips taken by IndoCabs clients. We have processed and analyzed this data to look for correlations in trip
cancellations.
Analysis
A Look at Trip Durations
To begin to understand the problems currently at IndoCabs we need to start by looking at data on trip
duration and booking window. Below I have compiled the mean, standard deviation and range of our
clients’ trip duration and booking window

3. Summary Statistics of Trip Duration and Booking Window
Trip
Duration
(Hours)
Booking
Window
(Days)
Mean 4.14 1.95
Standard
Deviation 10.84 5.01
Range 253.26 79.45
I choose these three measurements of data to show you because of how they interact with one another. The
mean is the average of the data set, the average time a trip takes for our clients is approximately 4 hours,
while the standard deviation is 68%. So, 68% of all of our clients trips are in the range of 0 to 15 hours,
while we have a total range of 253 hours of trip time. We can take this information about our measurements
and apply it to booking window data. Booking window is the time from when a client books a cab to when
their trip starts. The average booking window is approximately 2 days. While the standard deviation of
booking window is 5 days. Meaning that 68% of all of clients book their cabs from 0 to 7 days in advance,
while the range of booking windows are approximately 79 days. These condensed booking window time
and trip duration show us that while people may have really long trips or plan for them well in advance
most people are taking trips under a day’s time and plan for them only a week in advance at most.
The Magnitude of the Cancellation Problem at IndoCabs
From our data processing we find that 263 of a total of 2972 total bookings are cancelled. Meaning that 9
percent of all bookings created are cancelled before the trip starts.
An important part of our data is the type of travel the client is booking. We have three types of travel
including long distance, point to point and hourly rental travel. From our data we notice that that point to
point is the most popular type of travel, hourly rentals and long distance follow respectfully. In fact, point
to point travel make up 79% of travel. We notice that the number of cancellations is higher in point to point
travel than in the other two types, which makes sense since it is the most popular. But the proportion of
cancellations in point to point is more than twice the other two. Point to point travel experiences a 10%
cancellation rate while long distance and hourly travel both only experience 4% cancellation rate. I did
expect there to be a pattern by travel type. People who are booking long distance trips are going to be paying
more than the other travel types and are going to make sure their trips are not cancelled. Also from our data
we know that the average duration of long distance trips is on average 35 hours, compared to the
approximately 2 and 6 hours for point to point and hourly rental respectfully. This could suggest that the
shorter the trip the higher the likelihood of it being cancelled
Number of Bookings
Number of
Cancellations
Proportion
Cancelled
Long Distance 130 5 0.04
Point to Point 2344 239 0.10
Hourly Rental 498 19 0.04
The number of bookings made online versus on the mobile app are 1,207 and 136 respectfully. The number
of bookings made online is much higher than those made on the mobile app. While together these make up
1,343 of total bookings or 45% of total bookings. What is interesting about these types of bookings are that

4. their cancellation rates are both at 13%, 4% over the average cancellation rate. These two booking types
bring up big concerns for IndoCabs. While IndoCabs does offer other types of booking like text, e-mail,
phone call, or webchats, the mobile and online options will continue to grow as most people prefer to use
their phones and the internet due to the convenience it brings. Shutting down two methods of booking which
contribute up to 45% of all bookings is not a good option for IndoCabs. IndoCabs should focus on fixing
the problems that contribute to the high proportion of mobile and online bookings
This is not the pattern I was expecting to see from the percentage of bookings cancelled by weekday. I
would have expected the percentages to be much higher on weekends when people are in a higher need
for IndoCabs services. I find it most interesting that Thursday is above the average cancellation rate while
Saturday is much lower that the average.
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Percentage
Day of the Week
Percentage of Bookings Cancelled on each
Weekday

5. The Relationship between Booking Windows, Cancellations, and Trip Timing
The chart above is a scatterplot of the number of hourly bookings and cancellations. The R-squared for
this data is .3622. This suggests that the data is not very well correlated. We notice this as well in the
graph as after 100 bookings the data becomes much more scattered and uncorrelated.
R² = 0.3622
0
5
10
15
20
25
30
35
40
0 50 100 150 200 250 300
Number
of
Cancellations
Number of Bookings
Scatterplot of the Number of Bookings and
Cancellations
0
50
100
150
200
250
300
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Number
of
Bookings
Hour of the Day
Number of Bookings by Hour of Trip Start

6. Of all of these charts the one that is the most informative is the final chart on the percentage of car
cancellations by hour. Specifically, the chart shows us that the highest percentage of car cancellations
occur at night between the 17th
and 20th
hour of the day or 5pm to 8pm. This finding is also supported by
the other chart which shows the number of cancellations, of which most seem to be occurring between the
17th
and 20th
hour of the day. IndoCabs should investigate what is happening during the night which
causes their system to cancel trips. By narrowing down the time of the day in which most cancellations
occur they will have an easier time discovering why cancellations are occurring.
0
5
10
15
20
25
30
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
Number
of
Cancellations
Hour of the Day
Number of Car Cancelations by Hour of Trip Start
0
0.05
0.1
0.15
0.2
0.25
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Percentage
of
Cancellations
Hour of the Day
Percentage of Car Cancellations by Hour

7. The two histograms above show the proportion of bookings by booking window. The histogram with
booking windows of .25 day bins is a more useful chart than the one with only 1-day bins. This chart
shows us that 57% of all bookings have a booking window of a half day or less, which a 1-day bin could
not tell us. This tells us that most people are planning their trips within of half day of taking them.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1 2 3 4 5 6 7 More
Proportion
of
bookings
Booking window by 1-day
Length of Booking Windows (1-day bins)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Proportion
of
Bookings
Booking Window by .25-day
Length of Booking Windows (.25-day bins)

8. 0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1 2 3 4 5 6 7 More
Percentage
of
Cancelled
Bookings
Booking Window by 1-day Bins
Proportion of Cancelled Bookings (1-day bins)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
1 2 3 4 5 6 7 More
Percentage
of
Bookings
Booking Window by 1-day Bins
Proportion of Non-cancelled Bookings (1-day
bins)

9. The four histograms above show the proportion of bookings that were either cancelled or not cancelled in
1 and .25-day bins. The .25-day bins give us much more information on the probability of a booking
window being cancelled. We can see that most bookings are cancelled within a half booking window.
Which tells us that the .25-day bins are much more effective in communicating that the shorter the
booking window the more likely a trip will be cancelled.
Recommendations and Conclusion
By analyzing the data provided to us we have found a lot of useful correlations that could point us in the
direction of solving the trip cancellation problem that IndoCabs is currently facing. First off, we
discovered that IndoCabs is experiencing a 9% cancellation rate. Although the cancellation rate is 9% we
discovered several correlations that have a higher cancellation rate. For instance, point to point travel type
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.25
0.5
0.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
3.25
3.5
3.75
4
4.25
4.5
4.75
5
5.25
5.5
5.75
6
6.25
6.5
6.75
7
Proportion
of
Cancelled
Bookings
Booking Window by .25-day Bins
Proportion of Cancelled Bookings (.25-day bins)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.25
0.5
0.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
3.25
3.5
3.75
4
4.25
4.5
4.75
5
5.25
5.5
5.75
6
6.25
6.5
6.75
7
Proportion
of
Non-cancelled
Bookings
Booking window by.25 day bins
Proportion of Non-cancelled Bookings (.25-day bins)

10. has more than double the cancellation rate compared to the other two travel types with a cancellation rate
of 10%. It is important to figure out why this is the case and how to prevent it. Those who are using point
to point travel contribute to a significant majority of the travel IndoCabs provides. So it is important for
IndoCabs to keep this majority happy to continue to remain profitable. We notice a higher cancellation
rate in the type of bookings. Where mobile and online booking both have a cancellation rate of 13%. I
recommend IndoCabs look deeper into what is causing these booking types to have such a high
cancellation rate. These booking types make up over 45% of bookings, which is contributing a significant
amount to the trip cancellation rate. Mobile and online booking will most likely rise in popularity so
fixing this problem sooner will be important to the future of the company. We found out a lot about the
timing of trip cancellations, as many cancellations occur from 5-8pm along with occurring mostly within
12 hours of creating the trip. IndoCabs should investigate what causes cancellations to occur at these
times. It could be due to the high amount of people requesting cabs, something that their system could be
poorly equipped to handle. Implementing incentives to not cancel your trip are very important. IndoCabs
could use cancellation fees to prevent people and drivers from cancelling their trip. Also, IndoCabs could
create a ranking system for their clients to prevent them from cancelling their trips very often.
Elevator Charts
Looking at all the charts we have compiled I would use the percentage of car cancellations by hour,
proportion of cancelled bookings by .25-day bins and the length of booking windows by .25-days in an
elevator pitch. Starting off I would use the length of booking window by .25-day graph. This shows us
initially that most bookings have a booking window of 12 hours or under. So, when we look for why
cancellations occur, we can refine our search to this period. This graph also goes along with the proportion
of cancelled bookings by .25-day bins. In that graph as well, we notice that most cancellations have a
booking window of 12 hours or less. This correlates itself with the previous graph and forces us to look at
the shorter range of booking windows to solve our cancellation problem. Finally, I would use the car
cancellations by hour graph. The graph has a very striking and obvious spike in the range of 5pm to 8pm.
This will catch anyone’s attention and highlights the need for IndoCabs to look at what is happening during
this time to cause a rise in car cancellations.
Notes on Data Preparation
There were many steps taken to clean and remove errors from the data provided to us. We made a new
spreadsheet with only the processed data titled “ProcessedData” to make sure we still had our original data.
In our processed data sheet, we first removed duplicate data by using the remove duplicates function in
excel. Next, we had to remove any errors in our data. By sorting our data in terms of date we discovered
that the data contained dates that were from before 2013. After doing this we created several new variables
to understand the data better. These include, "fromdate", "Dayoftheweek", "Duration(hours)"
"Duration(days)", and “BookingWindow". By creating these variables, we were better able to interpret and
analyze the data provided. In terms of data collections, steps could be taken to fix these problems. There
may be problems in the collection of data causing dates mismatched with other information making them
older than 2013. Also, there may be some bad code causing data to be duplicated. I suggest IndoCabs
investigate the cause of this bad data, by looking into their data collection software.