Significance Characteristics of Customers in Online Food Delivery Management System

This article explores the significance characteristics of customers in online food delivery management system through histograms and analysis of large datasets. It covers various reasons for order cancellation such as address error, courier issue, courier request, duplicate job, end customer satisfaction, job expired, late courier, long time wait at do, long time wait at PU, no courier available, package damaged, package incorrect, package not ready, PU closed, wrong transport type and other reasons. The article also discusses the impact of good and bad customers on the business and how zip codes can be used to identify them.

Significance characteristics of customers
Introduction
The creation of online food ordering giving various good things to the customers who
don't have time to order the food directly and who don't need to travel and order the food by
going to the restaurant directly. The online food delivery system also offers the things like offers,
discounts and free delivery, etc. to fulfill the customer’s satisfaction. They also offers the other
things like order line without any cash (cash on delivery), customers receives the delivery within
the few minutes of time. If this these things are not properly maintained it leads to cancelling of
an order by the customer (Kieling et al., 2011). In this topic we are going to see about the
customer’s response in the online food delivery management system and the significance
characteristics of customers, who orders the food through online from the Receipo Spain
restaurant.
Literature review
‘Ayazlar and Artuğer’, Carried a research on the characteristics of customers. It is
identified using different ways according to the number of occurrences by the customers. The
good and bad customer’s specification is also identified based on the characteristics of
customers. According to the author, the analysis of the large datasets involves many
complications. The present method was not adequate for the efficient analysis of the datasets
(Ayazlar and Artuğer, 2015). It facilitates the better efficient analysis reports. Also, it provides
the better support for the decision making process.
Methodology
For finding the significance characteristics of customers there are some methods,
researches and techniques are used. Using the given receipo Spain data various things are
identified in order to find the characteristics of customers. The given the data set for the Receipo
Spain is too large and contains nearly fifteen thousand records. So, we need to use different
technologies like R code generation, histogram generation for finding the various characteristics.
The other methodologies are also used for finding the rich and poor customers and also for
finding good and bad customers.

Significance characteristics of customers
The significance of the customer’s characteristics also represented in terms of histograms.
They are:
 address error,
 courier issue,
 courier request,
 duplicate job,
 end customer satisfaction,
 job expired,
 late courier,
 long time wait at do,
 long time wait at PU,
 no courier available,
 package damaged,
 package incorrect,
 package not ready,
 PU closed,
 wrong transport type,
 And other reasons.
Address error:
The address error may happen by means of human errors like the customer misspelled
their addresses or the retailer who takes the order can type the addresses wrongly or the voice of
the customer is not clear through the communication media at the time there is a possibility of
writing the address may incorrect at that point everything will be wrong and causes this kind of
errors (Inman, Winer and Ferraro, 2009). There are thirty orders are canceled due to address
error. It is an error or fault happens during the orders placed by the customer through the
communications media like telephone or internet. It happens because of the minute error
happened during the placement of the order by the retailer.so these kinds of error lead to address
error. There is a possibility of canceling the orders happens due to these kinds of errors.

Figure 1 address error
The above histogram represents the address error occurs during the delivery of orders for the
specific order.
Courier issue:

The courier issue is one of the reasons for cancellation of orders. The possibility of these
errors are very high and hence cancellation behind these types of error also high (Motulsky,
2014). It is happened because of the huge amount of orders. For example, there are 14365 orders
are initiated for the Receipo Spain client in the given data set. At that time most of the orders are
needs to temporarily stopped, for other orders to complete. At that moment the courier issue and
its related issues have occurred. This issue also occurs during the careless mistake done by the
delivery agent like it is not delivered properly to client, most of the time the delivery agent forget
to deliver the product in the proper zip code due to a large number of orders placed by the
particular delivery agent and these are the reasons which lead to courier issue.
Figure 2 courier issue
The above histogram represents the courier issue in the given data.
Courier request:

Courier request is one of the reasons for cancellation of orders. For example, the
address request given by the customer is too long or it is a remote area. Hence the process is
declined by the client by giving different reasons. At that time customers mentality is giving bad
reviews about the client (Pejović et al., 2014). And also this kind of issues happens because of
the request given the customer has already canceled many orders previously at these cases client
giving different reasons and canceling the particular order. Because almost all the time that
particular client cancels the order and those things are already noted in the database and those
kinds of customers are treated as bad customers from the retailer’s point of view. At these cases,
the courier request will not be accepted by the client. So these kinds of orders also treated as
courier request. The impact of the bad customers is not that much even though the total number
of time wasted for the particular category is keep on increasing. And also some of the customers
are giving some silly reasons and hence they are also added to the category of bad customers.
The action taken against them is not high but in terms, small actions like their third orders are not
taken. Because the analyzers already checked two times and hence they are obeying and
cooperated well with the shop.
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Zipcodes vs Cancelled/Finished rate
cancelled finished
Zipcodes
no.of orders
The above histogram explains about relationship between zip code vs. cancelled and
finished rate for people of different area and their orders. Where the total number of cancelled
and finished orders happened by courier request.

Figure 3 courier request
The above diagram represents the courier request issues in the customer characteristics.
Duplicate job:
The duplicate job is another reason for the cancellation of orders. It may happen when the
delivery agent uses the third person for delivering the product or agent gives his work to some
other person due to his unavailability. At that time the particular delivery person’s job is not a
valid one, he may be working as the part-time worker and his experience in this field also very
less so he can’t fulfill the customer needs. Hence these kinds of orders are canceled. And also the
orders canceled for these kinds of reasons are very high (Zhu, 2017). Customers can easily be
identified as the difference between these people by analyzing their behavior and the way they
approach. This may cause cancellation of an order.

Figure 4 duplicate job
The above histogram represents the duplicate and various transport types used for
duplicate jobs. Totally there are forty seven duplicate job entries found in the data. Among them
twenty six orders are used through bikes. Hence majority of duplicate jobs are done through
bikes.
End customer satisfaction:
End customer satisfaction is another reason because of their lack of experience and lack
of maturity. The delivery agents are the one who acts as an interface between the customer and
retailer. So they have huge responsibilities while delivering an order. Even though the
customer’s behavior is not good they have to accept the behavior of the customer for achieving

the end customer satisfaction (G. Black, H. Vincent and J. Skinner, 2014). It is also called as the
end customer cancellation. The representation of rich customer and the poor customer can be
calculated with the help of zip codes in the data. The zip code da in the data are analyzed using
the software and then it is processed and needs to enter the postal zip codes then it will give the
histogram or graph in a pictorial representation that gives a clear idea about the customers status
that is who are all rich consumers and who are all poor consumers exactly using the zip codes. It
is calculated by means of the majority of the consumers in the given area. The number of users
living in the first range is very high when compared to the second range. So the customers who
are all repeatedly bought and given the good review about the restaurant from both the ranges are
called as good customers and customers who did not give the proper address and not responds
when the time of delivery and who simply orders and those orders are separated and the persons
who ordered such a product are treated as bad customers (Correia et al., 2018).
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zipcode vs end customer cancellation
zipcodes
no. of cancellation
The above histogram explains the cancellation orders of end customer. The number of
cancellations occurred in the zip codes. There are two zip codes which has more end customer
cancellations.

Figure 5 end customer satisfaction
The above histogram represents the end customer cancellation in the form of graphical
representation.

Job expired:
Most of the delivery agent’s jobs expired already even though they are working
and they are used by the retailer for delivery because their experience in this field is high. These
kinds of reasons have happened in very fewer cases, for example, the delivery agent failed to
deliver the orders because their job is expired. It also happens when the task was given for the
particular agent valid for only order but due to huge of orders the orders may increase at that
time the agent failed to deliver the order for various reasons like the long distance, customer not
available etc. It is also one of the reasons for cancellation. It happens in worst case scenarios
(Kouda, 2017). It mainly happens when the maximum numbers hours spent for only one order
due to long waiting time in delivery of the order, at this cases other orders get the delay or can’t
able to deliver at the given time.
Figure 6 job expired

The above histogram represents the graphical representation of job expired for the delivery
agents.
Late courier:
This is one of the reasons for canceling and it happens when there is a delay in the
delivery of the products. Hence customers waiting time also increases which leads to
cancellation. The major reasons for late courier are the agent who delivers the orders may handle
one or more orders. So the time taken to the previous order may also high. Or the chance of late
courier happens because of the reasons like huge orders, wrong transport type used, issues in the
transport type, long waiting time taken for delivery to previous customers etc. for example the
delivery agent takes one or more orders may cause late in the courier (Correia et al., 2018).
Figure 7 late courier
The above histogram represents the streets those are the reason for late courier.

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cancelled orders for late courier
zipcodes
orders
Long time wait at delivery of order:
There is some certain average of waiting time are maintained during the delivery of the
product. If that average waiting time exceeds then the delivery agents also have the rights to
abort the order (Sun, 2014). The customers are not available at the time of delivery, change in the
delivery address once after the order confirmation, not paying or cash not Hence the cancellation
occurs for the mentioned reasons. According to the retailer, the delivery of food products are
completely considered as the business and also the food products need to deliver within the time
unless it will get wasted. So the distance was the main parameter in this case.

Figure 8 long waiting at DO
The above graph represents the places which takes long waiting time at the delivery of orders.
Long time wait at pick up:
There are many reasons behind this reason like customers absence during delivery or
customer are not in their residence. Hence these kinds of faults can happen. For example, the
customer who places the order may absent when the delivery arrives. At that time customer
requests to the agent to wait until the customer arrives. This is one of the reasons for a long time
waits at pick up. There are other reasons for a long time wait during pick up our customers out of
an area, the card given by the customer is not working for further transactions. And in some

other cases the customer themselves waiting for the delivery agent to come and deliver their
orders. These are all the reasons behind a long time waiting at pick up.
Figure 9 long waiting time at PU

Figure 10 waiting time representation
The Figure 11 represents waiting time during the pickup and figure 11 represents the waiting in
for the particular customer. The thick line represents the average waiting time for every
customer.

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zipcode vs long time wait
zipcodes
long time wait
The above histogram represents long time wait (cancelled reason) for those who cancelled
particular kind of orders.
No courier available:
This is one of the reasons for the customer characteristics for rejecting the order. For
example, the agent missed that particular order hence can’t able to deliver the product. It happens
only in the worst case scenarios or if the customer from an area ordered a product but the zip
code does not contain the courier option. Hence the cancellation occurs. The no courier available
is imposed by the retailer for saving the money and time and fuel energy. These are the places
avoided by the retailers, they are remote areas, larger distance area, and unreachable areas etc.
for achieving the optimization (Pasanisi and Tebano, 2016).

Figure 12 no courier available
The above histogram represents the courier option not available for the mentioned timings in
mentioned areas.
Package damaged:
It is one of the customer characteristics happens when their ordered item has damaged. At
that time itself, the customer simply returns the package. For example, there are nearly fifteen
thousand orders are ordered by various customers. Due to this huge orders, the time taken for the
process also high. So the people who are all preparing the orders may be missing some of the
ingredients to the package. Which may cause the product damage due to less weight. Or the
packages handled by the agents may mistakenly or unknowingly damage the product
(Thøgersen, 2016). Or the packages damages of its own due to heavy load may cause package
damaged. These are the reasons for the damaged packet.

Figure 13 package damaged
The above graph represents the number of damages taken place during the interval time. The line
represents the damage in that time.
Package incorrect:
It happens when the delivery agent wrongly delivers a product. The package may ordered
by different person. So this case affects two customers directly or indirectly. This reason not only
causes the two customers but also causes the retailer name by creating bad image. It is the most
important reason in the customer characteristics which gives immediate cancellation. For
example the customer ordered different item what they got is different item, which means lack of
concentration by the retailer who takes the order. There are some other reasons are also there but
the reasons given are the main reason for the incorrect package (Casciati et al., 2016).

Figure 14 package incorrect
The above histogram represents the number of damaged packages arrived for the particular time
interval.
Package not ready:
The customer waiting for his product after ordering. But does not receives anything due
to a large number of orders processed by the management. In this case, the customers lost their
patience and simply rejects the reasons from the retailer’s side and cancels what they ordered
(Cox and Battey, 2017). It may also happen because of huge orders already ordered and hence
can’t access the current order. Hence the package also not ready. The other reason is package
accidently fallen and damaged and can’t be reused again. The other reasons for the package not
ready are: retailer’s inactivity, the agent not receives the item etc. these are the reasons for the
package.

Figure 15 package not ready
The above graph represents the number of orders are ordered during the time interval and which
leads to package not ready.
Pickup closed:
This one of the cases happens when the customer is out of the range. And unable to pick
up the order. Or not able to pick up due to some other reasons. In these cases, this may happen.
When the delivery time initiated by the deliverer at the time customer unable to pick up the
product due to his own personal works. At that time the pickup was closed by the deliverer by
notifying once to the customer (Richmond and Perkins, 2009). This process has taken place after
the long time wait of pick up.

Figure 16 pickup closed
The above histogram represents the number of pickups are closed during the time interval.
Wrong transport type:
Without checking the conditions of the transport vehicles may cause the repair in
their vehicles and it leads to time delay and hence the chance of cancellation also very high
(Rashad and Habib, 2017). Because of the customer who ordered the particular product waiting
for a long time and affected mentally for that product delay. Hence they simply canceled the
order and gives the bad review about the retailer (Koutra et al., 2015). The customer also needs

and expects good and quality of services from the clients. If the delivery agents failed to fulfill
the customers’ expectations they will simply cancel what they ordered (Dean, 2012). The
transport types used by the deliverers are given an impact to the deliveries and plays the vital
role in the total income and total outcome. Because they used the car, cargo bike, motorbike,
motorbike XL and blank (nothing). The fuel efficiency and consumption and mileage are very
high when they used cars and a cargo bike for orders (Stuckey, 2017).
For example, there are different zip codes are available to deliver the products. But the
vehicle capability depends on the number of distance to be covered. The vehicles like bike,
motorbike etc.
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zipcode vs wrong transport type
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wrong transport type
The above histogram represents the customers who particularly cancels the orders because of
client’s wrong transport type.

Figure 17 wrong transport type
This histogram represents the number of wrong transport used and its causes are represented in
the inverted graph.

And other reasons:
The other reasons for cancellations are,
 Item is being conveyed to a wrong address (Customers or Retailers careless mistake)
 Item isn't required any longer.
 Less expensive option accessible for the lesser cost.
 The cost of the item has fallen because of offers as well as the client needs to get it at a
lesser cost.
 The terrible audit from friends/relatives in the wake of requesting the item.
The other reasons may happen for the reasons like number of orders received already is high
so hence there is no chance of offering the new order. The new orders are expected to deliver for
long distance (Alberti, Laloux and Zanieri, 2016).
Representation of rich and poor customer
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POSTAL CODE
(Rich to Poor)
RANK

The representation of rich customer and the poor customer can be calculated with the help of
zip codes in the data. The one who keeps on buying (ordering) the food orders are considered as
good customers. The one who keep on cancelling without any specific reasons are considered as
bad customers. Because the number of delivered orders are finished and completed successfully
done in most number of times. Among the total number of orders the customers are separated in
to types by the Restaurant agents. They split the customers into rich customers and poor
customers. The delivery agents delivers the food with in the specific distance. The average
delivery distance is 6 km. In this case the delivery’s done more than 10km to 12 km distances are
considered as rich customers. Because the average distance is just 6 km. the delivery done for the
rich customer’s doubles the actual kilometer. Also the customers in those kilometer paying extra
charges for their deliveries hence they are considered as rich customers. The customers living
more than the 6 km distance but not paying any extra charges for their delivery are considered as
poor customers.
a) Analyzed part about the significance characteristics of customers
i) Research methodology
The examination of the data has been received to decide if pay value serves to enhance
work conditions is one of a subjective investigation approach. There different parts are analyzed
in the data given for the Receipo Spain. The major parts like cancelation reasons for the order
and finished reason of the order. Based on the methodologies, some of the important details
regards to the decision making may identified. Everyone may aware that today's customers are
more effective and well connected to the environment than the previous customers (Correia et
al., 2018). But still, their feedback system is more powerful and effective than the previous. So it
is also analyzed.
ii) Article analysis
The primary aim of the paper is one that makes utilization of a particular number of
deliveries to play out a subjective examination. The deliveries have been chosen based on
particular criteria which have been talked about under a different area in the data. Each orders

has been broke down independently and the discoveries extricated from the retailer have then
been assembled to shape a positive subordinate finish of sorts (Bhaskar, 2012).
iii) Quantitative analysis
The exploration system for the paper is one that has been intended to take after a
subjective examination. The subjective investigation takes into consideration an inside and out
comprehension of the exploration subject to be obtained. The subjective examination is by and
large utilized as a part of circumstances where the subject of research within reach is one that
joins a huge level of investigation of social traits or the investigation of patterns that can't be
communicated utilizing quantitative figures (Hilbe, 2009).
b) Effect of Receipo Spain data
The data contains nearly fifteen thousand data for the online management food delivery
system. There are different properties available for each and every data set. The job ID is the
thing that is used to represent the delivery agent. The number of drops done by the delivery agent
is identified using the job id. there are different reasons for canceling an order like wrong
transport type used, address error, duplicate job etc. there is only one reason for successful orders
that is job perfection (Richmond and Perkins, 2009). The different timings like inviting time,
picking time, waiting time, delivered time etc. are analyzed and the time and date are not exactly
for each and every set of the data for achieving the efficiency and optimization (Sehra, Singh and
Rai, 2017). These are the effects in the given data.

c) Recommendations
After analyzing the data given there different recommendations are concluded. If the first
six thousand kilometers living range area customers are eighty six percentage which is very high,
hence the optimal radius will be easily achieved when the retailer mainly taken care of these
people. If they achieved that then the time, energy and fuel used for transport types, and cost will
be minimized and saved. The delivery durations may vary according to the different customers
and their characteristics (Devasena, 2011). It can also be optimized by differentiating the
customers into four types. They are rich and poor customers and good and bad customers. The
final recommendation is done through the characteristics of customers. It can also be optimized
by means of analyzing the reasons for canceling an order and reasons for successful order.
Conclusion
The significance characteristics of customers play an important role when it comes to
ordering and delivering the products and the data given also has different reasons for canceling
and finishing the order and these are the things analyzed in this topic. The main reasons for
significance characteristics of customers were identified and calculated using the histograms.
The various characteristics of customers are calculated successfully using the given data for the
receipo Spain.