Dissertation on Big Data
Content
Ishaan Bahree SCIE: 1160039
Dissertation
Big Data in Marketing
Big Data is the new ‘buzz word’ of modern times. It refers to the ever increasing amount of
data that business’ collect over time. The data can be highly complicated in nature and can be
collected at increasing velocities. The term Big Data covers a large umbrella of challenges
such as storing and analysing huge data sets that conventional software cannot handle or
process. Companies are increasingly utilising Big Data in their decision making process as it
enables them to gain a better understanding of the challenges faced by businesses in a fast
paced and forever evolving world. It helps make more accurate and timely decisions than
previously possible.
It is not as if data was of less significance previously, there were simply less means to collect
it. Point of sale transactions and mailing campaigns are a few examples of the previous
sources of data. Since the rapid evolution of technology data can be collected from online
transaction activities, website browsing, phone network tracing, social media interactions etc.
Machine learning has come a long way, now it is possible to extract data from each and every
machine we use in our lives and that data can be stored and analysed for several purposes
with marketing being at the core.
IT technicians may be able to analyse the data however without understanding its true
essence it is impossible for them to draw any value. Therefore it is essential that key business
leaders be involved in the analytics process to generate significant value. Business leaders
have three main goals when it comes to marketing. They are as follows:
1) Engage the customer: This enables businesses to find out who their customers are,
where their customers are located, how they wish to be
contacted etc.
2) Customer retention: Being able to hold onto your customers and maintain loyalty is
key for any successful business. Management wants to know
what makes customers tick and keep coming back.
3) Strategic spending: Testing and measuring your modes of advertising is vital for
providing a strategy that works. Being able to analyse all past
advertising campaigns will lead to a more strategic allocation
of funds.
There are three types of data types that are ideal for marketing. Information relating to
customers such as transactional data which can be obtained from the company’s marketing
activities, website searches, random surveys and many other customer interactive sources.
Financial data is another type of data that is critical to Big Data analysis, this includes sales
figures, profits and other types of data that can be used to measure the finances of the
company. The last data type is operational data. Examples of this data type can be asset
management techniques, accounting software, CRM software etc. Data from these software
can illustrate the quality of marketing processes.
In today’s competitive business environment it is essential that businesses give themselves an
edge over their competition by using Big Data and analytics. This is easier said than done as
there are some challenges that businesses face when adopting this technology. Most analytic
software are not always compatible with the type of data, the decisions and processes
marketing companies require. Some of the challenges faced are as follows:
Ishaan Bahree SCIE: 1160039
1.) What data to collect: There is simply so much data out there and knowing what to
analyse can be challenging in itself. Therefore having the
technical ability is highly important.
2.) Software selection: Big Data can be collected in various forms and analysed using
different statistical packages. Some of the common statistical
packages are R, SAS and Python. SAS is more commercial and
widely spread while R and Python are open source and have a
rapidly increasing market share. Using these software is a skill
in itself and finding people with this skill set is tough.
3.) Implementation:
The data has provided the user with some key insights and now
making a strategy to implement the knowledge into the
marketing is a challenge in itself.
Big data is definitely a big deal as it can be of immense value when it comes to marketing.
Three steps can be taken to improve the marketing aspect of a business using Big Data and
analytics. They are:
1.) Data mining:
Analytics is the in depth analysis of Big Data. The data should
Be mined and examined in depth. Layer after layer can be
excavated revealing further insights into the business and in
specific its marketing activities. If this technique is optimally
utilised it can indeed provide strategic value to the business.
2.) Transfer knowledge: Extracting the knowledge from the data to those who can use it.
For example the CMO needs all the meaningful information but
so do all the other key members of the marketing team so that
they can act on it and make better informed judgements.
3.) Having a plan:
Providing there is a sound business plan in place with clearly
defined business strategies, knowing what questions to ask can
be of great benefit. Once it is established what to ask then the
required data can be identified.
KPMG states that a 92% of customers that use analytics use it for marketing purposes. This is
illustrated in the following figure:
Ishaan Bahree SCIE: 1160039
http://www.kpmg.com/Global/en/IssuesAndInsights/ArticlesPublicatio
ns/Documents/going-beyond-the-data-turning-v1.pdf
Dissertation
Big Data in Marketing
Big Data is the new ‘buzz word’ of modern times. It refers to the ever increasing amount of
data that business’ collect over time. The data can be highly complicated in nature and can be
collected at increasing velocities. The term Big Data covers a large umbrella of challenges
such as storing and analysing huge data sets that conventional software cannot handle or
process. Companies are increasingly utilising Big Data in their decision making process as it
enables them to gain a better understanding of the challenges faced by businesses in a fast
paced and forever evolving world. It helps make more accurate and timely decisions than
previously possible.
It is not as if data was of less significance previously, there were simply less means to collect
it. Point of sale transactions and mailing campaigns are a few examples of the previous
sources of data. Since the rapid evolution of technology data can be collected from online
transaction activities, website browsing, phone network tracing, social media interactions etc.
Machine learning has come a long way, now it is possible to extract data from each and every
machine we use in our lives and that data can be stored and analysed for several purposes
with marketing being at the core.
IT technicians may be able to analyse the data however without understanding its true
essence it is impossible for them to draw any value. Therefore it is essential that key business
leaders be involved in the analytics process to generate significant value. Business leaders
have three main goals when it comes to marketing. They are as follows:
1) Engage the customer: This enables businesses to find out who their customers are,
where their customers are located, how they wish to be
contacted etc.
2) Customer retention: Being able to hold onto your customers and maintain loyalty is
key for any successful business. Management wants to know
what makes customers tick and keep coming back.
3) Strategic spending: Testing and measuring your modes of advertising is vital for
providing a strategy that works. Being able to analyse all past
advertising campaigns will lead to a more strategic allocation
of funds.
There are three types of data types that are ideal for marketing. Information relating to
customers such as transactional data which can be obtained from the company’s marketing
activities, website searches, random surveys and many other customer interactive sources.
Financial data is another type of data that is critical to Big Data analysis, this includes sales
figures, profits and other types of data that can be used to measure the finances of the
company. The last data type is operational data. Examples of this data type can be asset
management techniques, accounting software, CRM software etc. Data from these software
can illustrate the quality of marketing processes.
In today’s competitive business environment it is essential that businesses give themselves an
edge over their competition by using Big Data and analytics. This is easier said than done as
there are some challenges that businesses face when adopting this technology. Most analytic
software are not always compatible with the type of data, the decisions and processes
marketing companies require. Some of the challenges faced are as follows:
Ishaan Bahree SCIE: 1160039
1.) What data to collect: There is simply so much data out there and knowing what to
analyse can be challenging in itself. Therefore having the
technical ability is highly important.
2.) Software selection: Big Data can be collected in various forms and analysed using
different statistical packages. Some of the common statistical
packages are R, SAS and Python. SAS is more commercial and
widely spread while R and Python are open source and have a
rapidly increasing market share. Using these software is a skill
in itself and finding people with this skill set is tough.
3.) Implementation:
The data has provided the user with some key insights and now
making a strategy to implement the knowledge into the
marketing is a challenge in itself.
Big data is definitely a big deal as it can be of immense value when it comes to marketing.
Three steps can be taken to improve the marketing aspect of a business using Big Data and
analytics. They are:
1.) Data mining:
Analytics is the in depth analysis of Big Data. The data should
Be mined and examined in depth. Layer after layer can be
excavated revealing further insights into the business and in
specific its marketing activities. If this technique is optimally
utilised it can indeed provide strategic value to the business.
2.) Transfer knowledge: Extracting the knowledge from the data to those who can use it.
For example the CMO needs all the meaningful information but
so do all the other key members of the marketing team so that
they can act on it and make better informed judgements.
3.) Having a plan:
Providing there is a sound business plan in place with clearly
defined business strategies, knowing what questions to ask can
be of great benefit. Once it is established what to ask then the
required data can be identified.
KPMG states that a 92% of customers that use analytics use it for marketing purposes. This is
illustrated in the following figure:
Ishaan Bahree SCIE: 1160039
http://www.kpmg.com/Global/en/IssuesAndInsights/ArticlesPublicatio
ns/Documents/going-beyond-the-data-turning-v1.pdf
