Big Data and Analytics to Increase Business Value: A Case Study of Amazon and Walmart
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AI Summary
This research explores the application of Big Data and analytics in e-commerce, focusing on Amazon and Walmart. It examines how these companies leverage vast amounts of data to gain valuable insights, improve customer service, and enhance business value. The study delves into the opportunities and challenges associated with Big Data in e-commerce, analyzing various techniques and technologies used for data analysis. It also highlights the specific value added to each company through the implementation of Big Data initiatives, showcasing how these initiatives empower decision-makers and drive business growth.
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REAL-TIME ANALYTICS [ITECH7407]
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Big Data and Analytics to Increase Business Value
Organizations:
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Big Data and Analytics to Increase Business Value
Organizations:
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Executive Summary:
The aim of this research is the collection of unstructured data from different sources such as
mobile and internet, process it to get valuable information and analysing the data to get the
useful insights of the e-commerce. A lot of literature review was done on the previous studies
to accomplish this aim. The major task in the research is to study the generation of data in 2
industries and evaluate the technologies and techniques of Big Data analytics that can give
advantages to the companies like improved customer feedback and reduced risk prevention.
For the completion of this task, Big Data analytics is used to create business value for
Amazon and Walmart.
2
The aim of this research is the collection of unstructured data from different sources such as
mobile and internet, process it to get valuable information and analysing the data to get the
useful insights of the e-commerce. A lot of literature review was done on the previous studies
to accomplish this aim. The major task in the research is to study the generation of data in 2
industries and evaluate the technologies and techniques of Big Data analytics that can give
advantages to the companies like improved customer feedback and reduced risk prevention.
For the completion of this task, Big Data analytics is used to create business value for
Amazon and Walmart.
2
Table of Contents
Executive Summary:.................................................................................................................. 2
1. Introduction to Big Data.........................................................................................................5
Big data in Amazon and Walmart.......................................................................................... 6
2. Opportunities for the Big Data............................................................................................... 7
2.1 Increased Shopper Analysis:............................................................................................ 7
2.2 Improved Customer Service:............................................................................................8
2.3 Easier and more secure online payments:........................................................................8
2.4 Continued advances in mobile commerce:...................................................................... 8
3. Challenges faced by the Big Data in e-commerce...............................................................10
3.1 DATA:............................................................................................................................10
3.1.1 Data Availability:.................................................................................................... 10
3.1.2 Data quality:............................................................................................................ 10
3.1.3 Velocity:.................................................................................................................. 10
3.1.4 Veracity:.................................................................................................................. 10
3.1.5 Data Discovery:.......................................................................................................10
3.1.6 Relevance:............................................................................................................... 10
3.1.7 Personally Identifiable Information:.......................................................................10
3.1.8 Data Dogmatism:.................................................................................................... 11
3.2 PROCESS:.....................................................................................................................11
3.3 MANAGEMENT:..........................................................................................................11
4. Current techniques and technologies for Big Data Analytics..............................................13
Big Data analytics technique and application for e-commerce............................................14
Big data technologies for e-commerce.................................................................................15
5. The value added to each chosen industry by the Big Data initiative from building new
capabilities and facilitate decision-makers...............................................................................17
Walmart:...............................................................................................................................17
Amazon:............................................................................................................................... 18
3
Executive Summary:.................................................................................................................. 2
1. Introduction to Big Data.........................................................................................................5
Big data in Amazon and Walmart.......................................................................................... 6
2. Opportunities for the Big Data............................................................................................... 7
2.1 Increased Shopper Analysis:............................................................................................ 7
2.2 Improved Customer Service:............................................................................................8
2.3 Easier and more secure online payments:........................................................................8
2.4 Continued advances in mobile commerce:...................................................................... 8
3. Challenges faced by the Big Data in e-commerce...............................................................10
3.1 DATA:............................................................................................................................10
3.1.1 Data Availability:.................................................................................................... 10
3.1.2 Data quality:............................................................................................................ 10
3.1.3 Velocity:.................................................................................................................. 10
3.1.4 Veracity:.................................................................................................................. 10
3.1.5 Data Discovery:.......................................................................................................10
3.1.6 Relevance:............................................................................................................... 10
3.1.7 Personally Identifiable Information:.......................................................................10
3.1.8 Data Dogmatism:.................................................................................................... 11
3.2 PROCESS:.....................................................................................................................11
3.3 MANAGEMENT:..........................................................................................................11
4. Current techniques and technologies for Big Data Analytics..............................................13
Big Data analytics technique and application for e-commerce............................................14
Big data technologies for e-commerce.................................................................................15
5. The value added to each chosen industry by the Big Data initiative from building new
capabilities and facilitate decision-makers...............................................................................17
Walmart:...............................................................................................................................17
Amazon:............................................................................................................................... 18
3
Conclusion................................................................................................................................19
References................................................................................................................................ 21
List of Figures
Figure 1: Big Data using Top 10 Companies.............................................................................5
Figure 2: Key Befits of analytics in big data..............................................................................7
Figure 3: Opportunities for the Big Data...................................................................................9
Figure 4: Challenges faced by the Big Data in e-commerce....................................................12
Figure 5: Data growth in between 1986 and 2007...................................................................13
Figure 6: Big Data analytics technique and application for e-commerce................................15
Figure 7: Review of social media analytics process and Big Data pipeline............................16
Figure 8: How Supply Chain Flagship Newsletter..................................................................17
Figure 9: How Walmart Makes Money? Understanding Walmart Business Model...............18
Figure 10: How Supply Chain Flagship Newsletter................................................................19
4
References................................................................................................................................ 21
List of Figures
Figure 1: Big Data using Top 10 Companies.............................................................................5
Figure 2: Key Befits of analytics in big data..............................................................................7
Figure 3: Opportunities for the Big Data...................................................................................9
Figure 4: Challenges faced by the Big Data in e-commerce....................................................12
Figure 5: Data growth in between 1986 and 2007...................................................................13
Figure 6: Big Data analytics technique and application for e-commerce................................15
Figure 7: Review of social media analytics process and Big Data pipeline............................16
Figure 8: How Supply Chain Flagship Newsletter..................................................................17
Figure 9: How Walmart Makes Money? Understanding Walmart Business Model...............18
Figure 10: How Supply Chain Flagship Newsletter................................................................19
4
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1. Introduction to Big Data
Big data is basically the huge and complex data sets where a large amount of data is present.
The data includes a lot of information including social media analytics, data management
capabilities and real-time data. The requirement of the big data is originally generated for the
big companies like Facebook, Google and Yahoo etc.
Figure 1: Big Data using Top 10 Companies
Source: educationview, 2018
The new tools are added to the management of the Big Data to improve the handling and the
storage of the data. The new Big Data technology is both economically and technically
feasible. The new technology management can allow not only to store the huge data sets but
also to analyse the datasets to get a better insight into the information. The basic functions of
the Big include collection, storing, analysing and visualizing the huge volume of the data.
The collection of raw data is done from mobile devices or any other media collection device
and it is the common challenge faced by most of the organizations (Russom, 2011). A good
platform for Big Data can allow developers to ingest a variety of data varying from structured
to un-structured at any available speed which is available at a real time. A secure, durable and
scalable platform constitutes a good platform for fulfilling all the requirements of the Big
Data. The unstructured data is converted into meaningful data once it reaches the analysing
5
Big data is basically the huge and complex data sets where a large amount of data is present.
The data includes a lot of information including social media analytics, data management
capabilities and real-time data. The requirement of the big data is originally generated for the
big companies like Facebook, Google and Yahoo etc.
Figure 1: Big Data using Top 10 Companies
Source: educationview, 2018
The new tools are added to the management of the Big Data to improve the handling and the
storage of the data. The new Big Data technology is both economically and technically
feasible. The new technology management can allow not only to store the huge data sets but
also to analyse the datasets to get a better insight into the information. The basic functions of
the Big include collection, storing, analysing and visualizing the huge volume of the data.
The collection of raw data is done from mobile devices or any other media collection device
and it is the common challenge faced by most of the organizations (Russom, 2011). A good
platform for Big Data can allow developers to ingest a variety of data varying from structured
to un-structured at any available speed which is available at a real time. A secure, durable and
scalable platform constitutes a good platform for fulfilling all the requirements of the Big
Data. The unstructured data is converted into meaningful data once it reaches the analysing
5
state. The process of joining the data, aggregating the data using some of the advanced
functions comes under the analysing state (Jain, & Suryavanshi, 2017). The platform of the
Big Data provides data to the business stakeholders through agile visualization of the data
that makes the data easily accessible to the user. The type of analytics used can help the end-
user to get the best available statistical predictions and any recommendation action. This
makes the use of the Big Data optimal for the end users.
Big data in Amazon and Walmart
Huge e-commerce companies like, Amazon has improved its performance by utilizing the
Big Data available from its customer. As one of the dominant company in the market
Amazon has one of the biggest databases of the customer’s information. Though, Amazon
has started as a retailer brand but now Amazon started providing online entertainment store as
“Amazon Prime” which enabled users to get movies, television shows and series by just
taking membership through the Amazon channel. The tastes, preferences and the purchasing
history of the customers constitute a huge amount of information. Amazon improved its
customer care quality using the correct Big Data resources. Walmart is another big name in
the business of retail. It has 245 million customers who visit 10,900 stores of Walmart stores
across the globe. The data from the Walmart’s customer is handled by the Inkiru which helps
in the marketing, merchandising, and the fraud prevention of the Walmart across the globe
(Gupta, et. al., 2014).
6
functions comes under the analysing state (Jain, & Suryavanshi, 2017). The platform of the
Big Data provides data to the business stakeholders through agile visualization of the data
that makes the data easily accessible to the user. The type of analytics used can help the end-
user to get the best available statistical predictions and any recommendation action. This
makes the use of the Big Data optimal for the end users.
Big data in Amazon and Walmart
Huge e-commerce companies like, Amazon has improved its performance by utilizing the
Big Data available from its customer. As one of the dominant company in the market
Amazon has one of the biggest databases of the customer’s information. Though, Amazon
has started as a retailer brand but now Amazon started providing online entertainment store as
“Amazon Prime” which enabled users to get movies, television shows and series by just
taking membership through the Amazon channel. The tastes, preferences and the purchasing
history of the customers constitute a huge amount of information. Amazon improved its
customer care quality using the correct Big Data resources. Walmart is another big name in
the business of retail. It has 245 million customers who visit 10,900 stores of Walmart stores
across the globe. The data from the Walmart’s customer is handled by the Inkiru which helps
in the marketing, merchandising, and the fraud prevention of the Walmart across the globe
(Gupta, et. al., 2014).
6
2. Opportunities for the Big Data
In the world of emerging technology, everything that is done online or offline is a source of
data. Along with the increasing, the creation of the data, the tools to manage the data is also
developed. The data generation is reached to a point where the generated data is too much
data. The understanding of the data has become more complicated as the data leaves us with
various facts, perceptions, numbers, and percentages (Zhou, et. al., 2014). The concept of Big
Data is quite complex and it is not yet understood by many of the organizations. Big Data has
a great influence on the retail market and it influences them in many ways. Some of the
opportunities that are provided by the Big Data to the retail organizations like Amazon and
Walmart are mentioned below:
49.0%
16.0%
10.0%
9.0%
9.0% 6.0%
Chart Title
1
2
3
4
5
6
Figure 2: Key Befits of analytics in big data
2.1 Increased Shopper Analysis: It is very important for the business to understand the
behaviour of the shopper so that the business can grow. One of the essential parts of the
business success process is the management of the Big Data. It can provide data like the
ongoing trend in the market, any spike in the demands of the product and the preferences of
7
In the world of emerging technology, everything that is done online or offline is a source of
data. Along with the increasing, the creation of the data, the tools to manage the data is also
developed. The data generation is reached to a point where the generated data is too much
data. The understanding of the data has become more complicated as the data leaves us with
various facts, perceptions, numbers, and percentages (Zhou, et. al., 2014). The concept of Big
Data is quite complex and it is not yet understood by many of the organizations. Big Data has
a great influence on the retail market and it influences them in many ways. Some of the
opportunities that are provided by the Big Data to the retail organizations like Amazon and
Walmart are mentioned below:
49.0%
16.0%
10.0%
9.0%
9.0% 6.0%
Chart Title
1
2
3
4
5
6
Figure 2: Key Befits of analytics in big data
2.1 Increased Shopper Analysis: It is very important for the business to understand the
behaviour of the shopper so that the business can grow. One of the essential parts of the
business success process is the management of the Big Data. It can provide data like the
ongoing trend in the market, any spike in the demands of the product and the preferences of
7
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the customer (Zhou, et. al., 2014). The business can make the most popular product available
to the customer and market them for increasing the sale of the product based on the data
provided by the analysis of the Big Data. There are some products that are not available on
the website but customer tries to search them, then the Big Data will help to understand those
searches and help Amazon or Walmart grab the new opportunities in the market.
2.2 Improved Customer Service: The numbers which show the unhappy and unsatisfied
with the customer service are relatively high and alarming for both Amazon and Walmart.
91% of the unhappy customer opts out of continuing the shopping on e-commerce if they are
experienced with poor customer service experience. The more predictive side of the Big Data
can be used by Amazon and Walmart will help the companies to identify the problem in their
service and can positively solve the existing issues even before a customer can involve with
the company (Zhou, et. al., 2014).
2.3 Easier and more secure online payments: The role of the Big Data can be very
significant in the secure payment on the online websites of Amazon and Walmart. All the
different payment methods are integrated into one platform so that the payment option for the
users becomes easier and it also helps in reducing the risk of fraud. The fraud in the payment
gateways are identified at real time and the strong advanced analytics provides a proactive
solution for the identification and the prevention of frauds.
2.4 Continued advances in mobile commerce: The use of the mobile phones is increasing
day by day with the increasing population. The use of mobile phone has reached a point
where researchers predicted that the use of the desktop computers is declining and at some
point, it will become obsolete. Big data is an advanced tool that provides the mobility in the
field of e-commerce. The data can be collected from different sources by the companies and
the collected data can be analysed that will benefit the e-commerce companies (Bohrer,
2018).
8
to the customer and market them for increasing the sale of the product based on the data
provided by the analysis of the Big Data. There are some products that are not available on
the website but customer tries to search them, then the Big Data will help to understand those
searches and help Amazon or Walmart grab the new opportunities in the market.
2.2 Improved Customer Service: The numbers which show the unhappy and unsatisfied
with the customer service are relatively high and alarming for both Amazon and Walmart.
91% of the unhappy customer opts out of continuing the shopping on e-commerce if they are
experienced with poor customer service experience. The more predictive side of the Big Data
can be used by Amazon and Walmart will help the companies to identify the problem in their
service and can positively solve the existing issues even before a customer can involve with
the company (Zhou, et. al., 2014).
2.3 Easier and more secure online payments: The role of the Big Data can be very
significant in the secure payment on the online websites of Amazon and Walmart. All the
different payment methods are integrated into one platform so that the payment option for the
users becomes easier and it also helps in reducing the risk of fraud. The fraud in the payment
gateways are identified at real time and the strong advanced analytics provides a proactive
solution for the identification and the prevention of frauds.
2.4 Continued advances in mobile commerce: The use of the mobile phones is increasing
day by day with the increasing population. The use of mobile phone has reached a point
where researchers predicted that the use of the desktop computers is declining and at some
point, it will become obsolete. Big data is an advanced tool that provides the mobility in the
field of e-commerce. The data can be collected from different sources by the companies and
the collected data can be analysed that will benefit the e-commerce companies (Bohrer,
2018).
8
Figure 3: Opportunities for the Big Data
9
Increased Shopper Analysis
Improved Customer Service
Easier and more secure
online payments
Continued advances in
mobile commerce
9
Increased Shopper Analysis
Improved Customer Service
Easier and more secure
online payments
Continued advances in
mobile commerce
3. Challenges faced by the Big Data in e-commerce
Each day, about 2.5 quintillion bytes of data is generated from the e-commerce. The data is
generally coming from videos, pictures, the posting on the social media, different sensors,
records of the purchase, GPS signals from mobile etc. There are challenges to the Big Data in
data collection, processing of the data and the management of data (Zhou, et. al., 2014).
Some of the challenges faced by Amazon and Walmart are mentioned below:
3.1 DATA:
The challenges faced in the field of Data for e-commerce are:
3.1.1 Data Availability: The availability of data is a major part for any company. The
companies like Amazon and Walmart hugely depends on the data for their decision making
or planning for the future. The companies can take a bad decision based on bad data.
3.1.2 Data quality: The questions on the quality of data like the goodness of data, the
broadness of data covered, fineness of the sampling resolution and the timeliness readings all
together decide the quality of the data (Kwon, et. al., 2014).
3.1.3 Velocity: It is generally the speed at which the data is retracted by the application for
the user. The faster the availability of data the easier it becomes for any company to make
decisions based on that data.
3.1.4 Veracity: The data can sometimes be missing some of the important parts. Sometimes
uncertainty, misstatements, untruths and imprecision can cause the bad decision making by
the companies. So it is very important for the Big Data service providers to give the accurate
and precise data to the user.
3.1.5 Data Discovery: A lot of huge data sets are collected across the web which makes it
crucial to take out the meaningful and high-quality data from the unstructured data.
3.1.6 Relevance: When a user search for some data on the web or from the huge data sets
presents on the Big Data servers, the user expects a relevant data for his search. The service
providers should keep in mind that the data retrieved should not be un-relevant to the topic in
order to give the user complete information (Zhou, et. al., 2014).
3.1.7 Personally Identifiable Information: Most of the information that is searched about
the people can compromise the privacy of them. So the data service providers must integrate
the security modules so as to extract enough information that will not harm the privacy of
10
Each day, about 2.5 quintillion bytes of data is generated from the e-commerce. The data is
generally coming from videos, pictures, the posting on the social media, different sensors,
records of the purchase, GPS signals from mobile etc. There are challenges to the Big Data in
data collection, processing of the data and the management of data (Zhou, et. al., 2014).
Some of the challenges faced by Amazon and Walmart are mentioned below:
3.1 DATA:
The challenges faced in the field of Data for e-commerce are:
3.1.1 Data Availability: The availability of data is a major part for any company. The
companies like Amazon and Walmart hugely depends on the data for their decision making
or planning for the future. The companies can take a bad decision based on bad data.
3.1.2 Data quality: The questions on the quality of data like the goodness of data, the
broadness of data covered, fineness of the sampling resolution and the timeliness readings all
together decide the quality of the data (Kwon, et. al., 2014).
3.1.3 Velocity: It is generally the speed at which the data is retracted by the application for
the user. The faster the availability of data the easier it becomes for any company to make
decisions based on that data.
3.1.4 Veracity: The data can sometimes be missing some of the important parts. Sometimes
uncertainty, misstatements, untruths and imprecision can cause the bad decision making by
the companies. So it is very important for the Big Data service providers to give the accurate
and precise data to the user.
3.1.5 Data Discovery: A lot of huge data sets are collected across the web which makes it
crucial to take out the meaningful and high-quality data from the unstructured data.
3.1.6 Relevance: When a user search for some data on the web or from the huge data sets
presents on the Big Data servers, the user expects a relevant data for his search. The service
providers should keep in mind that the data retrieved should not be un-relevant to the topic in
order to give the user complete information (Zhou, et. al., 2014).
3.1.7 Personally Identifiable Information: Most of the information that is searched about
the people can compromise the privacy of them. So the data service providers must integrate
the security modules so as to extract enough information that will not harm the privacy of
10
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other people. Some part of this should have an oversight of the government to preserve the
privacy of the people.
3.1.8 Data Dogmatism: Today most of the companies take help of the data analytics from
the Big Data but the companies should not be too dependent on this analytics for their big
decisions. Common sense and domain experts should take part in the decision making for the
big companies.
3.2 PROCESS:
There are various challenges that come up during the derivation of the useful and meaningful
insights from the data. Some of this includes the correct capturing of data from different
sources. The next step after data collection is the alignment of the data in a suitable form. For
example, resolving the issue of data duplicity when two datasets are the same. Then, the
transformation of this data into a form that can be used for analysis. The modelling is done
after that either mathematically or through some type of simulation. At last the visualization
and the sharing of the resultant output for the end user (Kwon, et. al., 2014).
3.3 MANAGEMENT:
The data privacy, data security and the correct governance of the gathered data comes under
the management of the Big Data. E-commerce companies like Amazon and Walmart face
challenges in this also as the data should be used correctly. Using the data correctly means
that it is abiding by all the rules and its intended use. Companies should track the usage of
data, how it is derived and transformed along with managing the lifecycle of the data ( Kwon,
et. al., 2014).
Amazon and Walmart are required to manage the data properly as there are legal and ethical
concerns attached to the data. These organizations should also make the data secure, access
controlled and it should be logged for audits.
11
privacy of the people.
3.1.8 Data Dogmatism: Today most of the companies take help of the data analytics from
the Big Data but the companies should not be too dependent on this analytics for their big
decisions. Common sense and domain experts should take part in the decision making for the
big companies.
3.2 PROCESS:
There are various challenges that come up during the derivation of the useful and meaningful
insights from the data. Some of this includes the correct capturing of data from different
sources. The next step after data collection is the alignment of the data in a suitable form. For
example, resolving the issue of data duplicity when two datasets are the same. Then, the
transformation of this data into a form that can be used for analysis. The modelling is done
after that either mathematically or through some type of simulation. At last the visualization
and the sharing of the resultant output for the end user (Kwon, et. al., 2014).
3.3 MANAGEMENT:
The data privacy, data security and the correct governance of the gathered data comes under
the management of the Big Data. E-commerce companies like Amazon and Walmart face
challenges in this also as the data should be used correctly. Using the data correctly means
that it is abiding by all the rules and its intended use. Companies should track the usage of
data, how it is derived and transformed along with managing the lifecycle of the data ( Kwon,
et. al., 2014).
Amazon and Walmart are required to manage the data properly as there are legal and ethical
concerns attached to the data. These organizations should also make the data secure, access
controlled and it should be logged for audits.
11
Figure 4: Challenges faced by the Big Data in e-commerce
12
Challenges faced by the Big
Data in e-commerce
DATA
Data Availability
Data quality
Velocity
Veracity
Data Discovery
Relevance
Personally Identifiable
Information
Data Dogmatism
PROCESS
MANAGEMENET
12
Challenges faced by the Big
Data in e-commerce
DATA
Data Availability
Data quality
Velocity
Veracity
Data Discovery
Relevance
Personally Identifiable
Information
Data Dogmatism
PROCESS
MANAGEMENET
4. Current techniques and technologies for Big Data Analytics
There is an explosive growth in the generation of data from different sources. The Big Data is
not only a by-product but it is considered as an asset which can provide valuable insights for
the company. It can predict the customer behaviour and help the company to propose a future
approach towards the advertisement (Chen, et. al., 2012). The companies like Amazon and
Walmart can make use of the data analytics to effectively meet the needs of the customers
need. Over the years the exchange of the data has been increased a lot in numbers which
makes it necessary for the companies like Amazon and Walmart to correctly evaluate the
unstructured data. In 2012, the database of the Walmart transaction is estimated to be 2.5
petabytes of data related to customer’s data. The growth of the data is mainly due to the
cheap availability of the internet services to the customers. In today’s world where everything
is done electronically, people use the internet to share information and use the internet to buy
or sell the items through the internet. The vendors of the e-commerce have taken advantage
of this and used the internet to increase their sales and services along with improving the
revenue and the awareness of the brand in the market and customers (Akter & Wamba,
2016).
Figure 5: Data growth in between 1986 and 2007
Source: researchgate.net, 2014
13
There is an explosive growth in the generation of data from different sources. The Big Data is
not only a by-product but it is considered as an asset which can provide valuable insights for
the company. It can predict the customer behaviour and help the company to propose a future
approach towards the advertisement (Chen, et. al., 2012). The companies like Amazon and
Walmart can make use of the data analytics to effectively meet the needs of the customers
need. Over the years the exchange of the data has been increased a lot in numbers which
makes it necessary for the companies like Amazon and Walmart to correctly evaluate the
unstructured data. In 2012, the database of the Walmart transaction is estimated to be 2.5
petabytes of data related to customer’s data. The growth of the data is mainly due to the
cheap availability of the internet services to the customers. In today’s world where everything
is done electronically, people use the internet to share information and use the internet to buy
or sell the items through the internet. The vendors of the e-commerce have taken advantage
of this and used the internet to increase their sales and services along with improving the
revenue and the awareness of the brand in the market and customers (Akter & Wamba,
2016).
Figure 5: Data growth in between 1986 and 2007
Source: researchgate.net, 2014
13
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Due to the characteristics like volume, veracity and the volume of the Big Data, the
traditional databases are not likely to handle the operations of the big data analytics. The new
advanced technologies like Hadoop have made it a lot easier to analyse the Big Data and
extract useful insights. Along with Hadoop, Data Mining also plays an important role in the
management of the Big Data for the big companies using the machine learning and clustering
algorithms (Chen, et. al., 2012).
Big Data analytics technique and application for e-commerce
Some of the techniques that are used in the e-commerce are mentioned below:
Social Media Analytics: it includes the collection of data from the social networking
sites like Facebook, Twitter etc. and that data can be analysed to get the important
insights about the users (Chen, et. al., 2012). Statistics show that about 40% of people are
likely to buy an item if they shared in on social media. The referrals attract about 71% of
social media users. While purchasing something, 74% of the users depend on the social
media to make a decision.
Predictive Analytics: The usage of the old transaction or the purchasing history of the
user so that the future trends can be predicted is commonly termed as predictive analysis.
The statistical models and the machine learning algorithm help in the analysis of the
customer data. Using the Big Data, the e-commerce vendors can easily predict the
customer behaviour at an efficient cost and very faster (Akter & Wamba, 2016). The use
of the predictive analysis can be done to improve product recommendation, price
management, and predictive search for the vendors,
Mobile Analytics: The number of mobile phone users is increasing day by day. From
using the mobiles as communication tools to using mobiles for practically everything, the
importance of mobile is increased. The analysis of the data provided by the mobile user
can allow the e-commerce vendors to provide services like advertising based on location,
offers based on location. Also, the use of this process is very cheap and effective ( Akter
& Wamba, 2016).
14
traditional databases are not likely to handle the operations of the big data analytics. The new
advanced technologies like Hadoop have made it a lot easier to analyse the Big Data and
extract useful insights. Along with Hadoop, Data Mining also plays an important role in the
management of the Big Data for the big companies using the machine learning and clustering
algorithms (Chen, et. al., 2012).
Big Data analytics technique and application for e-commerce
Some of the techniques that are used in the e-commerce are mentioned below:
Social Media Analytics: it includes the collection of data from the social networking
sites like Facebook, Twitter etc. and that data can be analysed to get the important
insights about the users (Chen, et. al., 2012). Statistics show that about 40% of people are
likely to buy an item if they shared in on social media. The referrals attract about 71% of
social media users. While purchasing something, 74% of the users depend on the social
media to make a decision.
Predictive Analytics: The usage of the old transaction or the purchasing history of the
user so that the future trends can be predicted is commonly termed as predictive analysis.
The statistical models and the machine learning algorithm help in the analysis of the
customer data. Using the Big Data, the e-commerce vendors can easily predict the
customer behaviour at an efficient cost and very faster (Akter & Wamba, 2016). The use
of the predictive analysis can be done to improve product recommendation, price
management, and predictive search for the vendors,
Mobile Analytics: The number of mobile phone users is increasing day by day. From
using the mobiles as communication tools to using mobiles for practically everything, the
importance of mobile is increased. The analysis of the data provided by the mobile user
can allow the e-commerce vendors to provide services like advertising based on location,
offers based on location. Also, the use of this process is very cheap and effective ( Akter
& Wamba, 2016).
14
Figure 6: Big Data analytics technique and application for e-commerce
Big data technologies for e-commerce
Text Mining: This technology is based on the text available on the social sites or blogs
for framing a judgement or on the relevance of the data. A list of keywords is generated
by the e-commerce vendors related to the product they wish to monitor. The sentiments or
popularity of the product can be identified using this (Edosio, 2014).
Machine learning: this technology works in integration with the artificial intelligence.
Each letter of the obtained information is tagged together and then it is analysed. After
analysing, the word is then referenced with a predefined word combination that can
evaluate whether the comment is positive or negative.
15
Big Data analytics
technique and
application for e-
commerce
1. Social
Media
Analytics
2. Predictive
Analytics
3. Mobile
Analytics
Big data technologies for e-commerce
Text Mining: This technology is based on the text available on the social sites or blogs
for framing a judgement or on the relevance of the data. A list of keywords is generated
by the e-commerce vendors related to the product they wish to monitor. The sentiments or
popularity of the product can be identified using this (Edosio, 2014).
Machine learning: this technology works in integration with the artificial intelligence.
Each letter of the obtained information is tagged together and then it is analysed. After
analysing, the word is then referenced with a predefined word combination that can
evaluate whether the comment is positive or negative.
15
Big Data analytics
technique and
application for e-
commerce
1. Social
Media
Analytics
2. Predictive
Analytics
3. Mobile
Analytics
Figure 7: Review of social media analytics process and Big Data pipeline
Source: link.springer, 2018
Collaborative Filtering: A database with the user preferences in the history of the search
is preparing (Edosio, 2014). Whenever a new user searches in the e-commerce site the
algorithm matches it with the closest preference class that matches the customer’s taste.
Clustering algorithm: The users who have a similar preference are clustered in a single
group. Any new customer is added to a cluster by matching any similarities between them
(Jain, & Suryavanshi, 2017).
Bluetooth location-based advertiser: This technology makes use of the Bluetooth to
provide the targeted customer with some special offers at a particular time of the day.
16
Source: link.springer, 2018
Collaborative Filtering: A database with the user preferences in the history of the search
is preparing (Edosio, 2014). Whenever a new user searches in the e-commerce site the
algorithm matches it with the closest preference class that matches the customer’s taste.
Clustering algorithm: The users who have a similar preference are clustered in a single
group. Any new customer is added to a cluster by matching any similarities between them
(Jain, & Suryavanshi, 2017).
Bluetooth location-based advertiser: This technology makes use of the Bluetooth to
provide the targeted customer with some special offers at a particular time of the day.
16
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5. The value added to each chosen industry by the Big Data initiative from building new
capabilities and facilitate decision-makers
There are a lot of techniques and technologies which can help the companies like Amazon
and Walmart grow their business upon (GalbRaith, 2014). Some of the advantages that are
taken by these companies using the Big Data are mentioned below:
Figure 8: How Supply Chain Flagship Newsletter
Source: scdigest, 2014
Walmart:
Walmart is an American multinational retail corporation. The acquisition of the Kosmix in
2011 made them take help of the software developed by Kosmix that can search and analyse
the media applications so that they can provide optimized insights to the users. The Walmart
Shopycat is used to capture real-time data from social media sites using millions of entities
and relations. In 2013, Walmart acquired another company Inkiru which uses a software
based on predictive analytics (Mujtaba & Maxwell, 2007). It captures data from across the
internet and helps Walmart to create personalized or customized campaigns for their
advertising. In January 2013, the company reportedly stated that their media analytics
software can index and search 60 billion of the social media documents which will help the
company to understand the sentiments, popularity and the trends in those records on a real-
17
capabilities and facilitate decision-makers
There are a lot of techniques and technologies which can help the companies like Amazon
and Walmart grow their business upon (GalbRaith, 2014). Some of the advantages that are
taken by these companies using the Big Data are mentioned below:
Figure 8: How Supply Chain Flagship Newsletter
Source: scdigest, 2014
Walmart:
Walmart is an American multinational retail corporation. The acquisition of the Kosmix in
2011 made them take help of the software developed by Kosmix that can search and analyse
the media applications so that they can provide optimized insights to the users. The Walmart
Shopycat is used to capture real-time data from social media sites using millions of entities
and relations. In 2013, Walmart acquired another company Inkiru which uses a software
based on predictive analytics (Mujtaba & Maxwell, 2007). It captures data from across the
internet and helps Walmart to create personalized or customized campaigns for their
advertising. In January 2013, the company reportedly stated that their media analytics
software can index and search 60 billion of the social media documents which will help the
company to understand the sentiments, popularity and the trends in those records on a real-
17
time basis. Their software is also capable of watching over the sentiments of all Walmart
stores across the world based on their geographic locations (Mujtaba & Maxwell, 2007).
Their software called as Social Genome (Now Walmart Shopycat) is also capable of
capturing information and relationships about people, events, products, organizations, topics
and location.
Figure 9: How Walmart Makes Money? Understanding Walmart Business Model
Source: revenuesandprofits, 2015
Amazon:
Amazon is considered one of the world’s largest online retail store and entertainment site.
The clustering algorithm and collaborative filtering are used by Amazon to cluster the users
with the same taste in one preference. The products are arranged to suit each customer
preference on a real-time which is a challenge in itself keeping in mind the collection of
millions of user's data. Group of a user are based on similar search and item to item
collaborative filtering so that the best products for a user are recommended. Products are also
recommended based on the search history of the user. This uses the user history preferences
18
stores across the world based on their geographic locations (Mujtaba & Maxwell, 2007).
Their software called as Social Genome (Now Walmart Shopycat) is also capable of
capturing information and relationships about people, events, products, organizations, topics
and location.
Figure 9: How Walmart Makes Money? Understanding Walmart Business Model
Source: revenuesandprofits, 2015
Amazon:
Amazon is considered one of the world’s largest online retail store and entertainment site.
The clustering algorithm and collaborative filtering are used by Amazon to cluster the users
with the same taste in one preference. The products are arranged to suit each customer
preference on a real-time which is a challenge in itself keeping in mind the collection of
millions of user's data. Group of a user are based on similar search and item to item
collaborative filtering so that the best products for a user are recommended. Products are also
recommended based on the search history of the user. This uses the user history preferences
18
and rated item so that the items similar to user’s taste are listed. The other features of
Amazon product recommendations include recommending the products based on the items
already available in the cart of the user. The algorithm can build a table with similar items
that the user can tend to buy (Lang, et. al., 2012).
Amazon uses the dynamic pricing for some of its users. Dynamic pricing means the resource
planning systems are used to dynamically set prices or customized discounts to selected
customers based on the historical data such as the previous history of purchase, cookies, or
clickstream. In September 2000, CNN reported that one of the buyers is effected by $2.50 in
purchasing of a DVD after deleting the cookies. In another report, CNN reported that the
dynamic pricing algorithm is used by Amazon to sell a product for $51 then its original price
on Amazon (Lang, et. al., 2012).
Figure 10: How Supply Chain Flagship Newsletter
Source: scdigest, 2014
Conclusion
The introduction of the Big Data has only provided an advantage to the e-commerce retailers.
The new and advanced tools and techniques to manage the Big Data make it easier for the
online stores to be successful and useful. The Big Data provides all tools for the online retail
19
Amazon product recommendations include recommending the products based on the items
already available in the cart of the user. The algorithm can build a table with similar items
that the user can tend to buy (Lang, et. al., 2012).
Amazon uses the dynamic pricing for some of its users. Dynamic pricing means the resource
planning systems are used to dynamically set prices or customized discounts to selected
customers based on the historical data such as the previous history of purchase, cookies, or
clickstream. In September 2000, CNN reported that one of the buyers is effected by $2.50 in
purchasing of a DVD after deleting the cookies. In another report, CNN reported that the
dynamic pricing algorithm is used by Amazon to sell a product for $51 then its original price
on Amazon (Lang, et. al., 2012).
Figure 10: How Supply Chain Flagship Newsletter
Source: scdigest, 2014
Conclusion
The introduction of the Big Data has only provided an advantage to the e-commerce retailers.
The new and advanced tools and techniques to manage the Big Data make it easier for the
online stores to be successful and useful. The Big Data provides all tools for the online retail
19
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to be successful. Though the analytics of Big Data faces some challenges like staffing,
training and privacy concerns, many of the big online companies like Amazon and Walmart
are adopting it and gained a lot of success in the field of business growth and providing better
services to the customers. On average 5 of the top business organization in the USA use the
Big Data analytics and has achieved a tremendous success in business (Kwon, et. al., 2014).
Every store online or offline wants to earn money and grow their business along with
satisfying the needs of the customers. For that purpose, the companies are required to have as
much information about the customer as possible. The discounts are another great way to
attract customer as customers tend to go where they get their desired item at a favourable cost
(Zhou, et. al., 2014). The companies like Amazon and Walmart have successfully made use
of the different types of Big Data analytics to gain profit and grow their business.
20
training and privacy concerns, many of the big online companies like Amazon and Walmart
are adopting it and gained a lot of success in the field of business growth and providing better
services to the customers. On average 5 of the top business organization in the USA use the
Big Data analytics and has achieved a tremendous success in business (Kwon, et. al., 2014).
Every store online or offline wants to earn money and grow their business along with
satisfying the needs of the customers. For that purpose, the companies are required to have as
much information about the customer as possible. The discounts are another great way to
attract customer as customers tend to go where they get their desired item at a favourable cost
(Zhou, et. al., 2014). The companies like Amazon and Walmart have successfully made use
of the different types of Big Data analytics to gain profit and grow their business.
20
References
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and agenda for future research. Electronic Markets, 26(2), 173-194. [Accessed on: 1 Sep
2018]
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics:
from big data to big impact. MIS quarterly, 1165-1188. [Accessed on: 1 Sep 2018]
Edosio, U. Z. (2014). Big data Analytics and its Application in E-commerce. E-
Commerce Technologies, 1. [Accessed on: 1 Sep 2018] Edosio, U. Z., (2014). Big Data Paradigm- Analysis, Application, and Challenges.
[Online] researchgate.net. Available at: https://www.researchgate.net/figure/Data-
growth-between-1986-and-20071_fig1_261947134 [Accessed on: 1 Sep 2018].
GalbRaith, J. R. (2014). Organizational design challenges resulting from big data.
Gupta, U. K., Sharma, B., & Nayak, M. (2014). Big Data Analytics And Its Application
In E-Commerce. [Accessed on: 1 Sep 2018]
Harsoor, A. S., & Patil, A. (2015). Forecast of sales of walmart store using big data
applications. IJRET: International Journal of Research in Engineering and Technology.
[Accessed on: 1 Sep 2018]
Jain, N., & Suryavanshi, A. (2017). Analysis of E-Commerce Big Data using Clustering
and CloudSim Load Balancing. International Journal of Computer Applications, 161(11).
[Accessed on: 1 Sep 2018]
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience
and acquisition intention of big data analytics. International Journal of Information
Management, 34(3), 387-394. [Accessed on: 1 Sep 2018]
Lang, S., Tinder, L., Zimmerman, J., & Harrison, J. S. (2012). Amazon. com: Offering
Everything from A to Z. [Accessed on: 1 Sep 2018] Maheshwari, S., (2017). Top 10 Companies Obsessed with Big Data Analytics. [Online]
educationviews.org Available at: http://www.educationviews.org/top-10-companies-
obsessed-big-data-analytics/ [Accessed on: 1 Sep 2018]
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and challenges. Journal of Business Case Studies, 3(2), 1-10. [Accessed on: 1 Sep 2018]
Revenuesandprofits, (2015). How Walmart Makes Money? Understanding Walmart
Business Model. [Online] revenuesandprofits.com Available at:
21
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review
and agenda for future research. Electronic Markets, 26(2), 173-194. [Accessed on: 1 Sep
2018]
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics:
from big data to big impact. MIS quarterly, 1165-1188. [Accessed on: 1 Sep 2018]
Edosio, U. Z. (2014). Big data Analytics and its Application in E-commerce. E-
Commerce Technologies, 1. [Accessed on: 1 Sep 2018] Edosio, U. Z., (2014). Big Data Paradigm- Analysis, Application, and Challenges.
[Online] researchgate.net. Available at: https://www.researchgate.net/figure/Data-
growth-between-1986-and-20071_fig1_261947134 [Accessed on: 1 Sep 2018].
GalbRaith, J. R. (2014). Organizational design challenges resulting from big data.
Gupta, U. K., Sharma, B., & Nayak, M. (2014). Big Data Analytics And Its Application
In E-Commerce. [Accessed on: 1 Sep 2018]
Harsoor, A. S., & Patil, A. (2015). Forecast of sales of walmart store using big data
applications. IJRET: International Journal of Research in Engineering and Technology.
[Accessed on: 1 Sep 2018]
Jain, N., & Suryavanshi, A. (2017). Analysis of E-Commerce Big Data using Clustering
and CloudSim Load Balancing. International Journal of Computer Applications, 161(11).
[Accessed on: 1 Sep 2018]
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience
and acquisition intention of big data analytics. International Journal of Information
Management, 34(3), 387-394. [Accessed on: 1 Sep 2018]
Lang, S., Tinder, L., Zimmerman, J., & Harrison, J. S. (2012). Amazon. com: Offering
Everything from A to Z. [Accessed on: 1 Sep 2018] Maheshwari, S., (2017). Top 10 Companies Obsessed with Big Data Analytics. [Online]
educationviews.org Available at: http://www.educationviews.org/top-10-companies-
obsessed-big-data-analytics/ [Accessed on: 1 Sep 2018]
Mujtaba, B. G., & Maxwell, S. (2007). Wal-mart in the global retail market: Its growth
and challenges. Journal of Business Case Studies, 3(2), 1-10. [Accessed on: 1 Sep 2018]
Revenuesandprofits, (2015). How Walmart Makes Money? Understanding Walmart
Business Model. [Online] revenuesandprofits.com Available at:
21
https://revenuesandprofits.com/how-walmart-makes-money-understanding-walmart-
business-model/ [Accessed on: 1 Sep 2018]
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4),
1-34. [Accessed on: 1 Sep 2018]
Scdigest, (2014). Supply Chain Flagship Newsletter. [Online] revenuesandprofits.com
Available at: http://www.scdigest.com/assets/news/14-02-27.htm [Accessed on: 1 Sep
2018]
Sebei, H., Taieb, M. A. H., & Aouicha, M. B. (2018). Review of social media analytics
process and Big Data pipeline. [Online] link.springer.com Available at:
https://link.springer.com/article/10.1007/s13278-018-0507-0 [Accessed on: 1 Sep 2018]
Zhou, Z. H., Chawla, N. V., Jin, Y., & Williams, G. J. (2014). Big data opportunities and
challenges: Discussions from data analytics perspectives [discussion forum]. IEEE
Computational Intelligence Magazine, 9(4), 62-74. [Accessed on: 1 Sep 2018]
22
business-model/ [Accessed on: 1 Sep 2018]
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4),
1-34. [Accessed on: 1 Sep 2018]
Scdigest, (2014). Supply Chain Flagship Newsletter. [Online] revenuesandprofits.com
Available at: http://www.scdigest.com/assets/news/14-02-27.htm [Accessed on: 1 Sep
2018]
Sebei, H., Taieb, M. A. H., & Aouicha, M. B. (2018). Review of social media analytics
process and Big Data pipeline. [Online] link.springer.com Available at:
https://link.springer.com/article/10.1007/s13278-018-0507-0 [Accessed on: 1 Sep 2018]
Zhou, Z. H., Chawla, N. V., Jin, Y., & Williams, G. J. (2014). Big data opportunities and
challenges: Discussions from data analytics perspectives [discussion forum]. IEEE
Computational Intelligence Magazine, 9(4), 62-74. [Accessed on: 1 Sep 2018]
22
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