Analyzing Big Data's Influence on Marketing Strategy and Decisions

This report conducts a literature analysis on the significant influence of big data analytics on marketing strategy and decision-making processes. It identifies key problems in traditional marketing, such as targeting high-value growth sources, handling large datasets, and the slow pace of data analysis, along with decision-making challenges. The report then explores how big data analytics provides solutions to these problems, including conversion optimization, efficient data collection, and the use of properly timed content. It also details the implications of these solutions and suggests future research directions, such as machine learning and the evolving cost-effectiveness of big data analytics. The analysis emphasizes the crucial role of big data in modern marketing and its potential to reduce complexities and improve organizational success.

Running head: LITERATURE ANALYSIS
Literature Analysis: Big Data Analytics and How it Influences Marketing Strategy and
Decision-making about Strategy
Name of the Student
Name of the University
Author’s Note:

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LITERATURE ANALYSIS
Table of Contents
1. Problem Statement.................................................................................................................2
2. Approach................................................................................................................................2
3. Literature Analysis.................................................................................................................3
4. Conclusion..............................................................................................................................8
5. References..............................................................................................................................9

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LITERATURE ANALYSIS
1. Problem Statement
Marketing strategy is the most important strategy for any particular business. It is
responsible for all types of long-term survival of all organizations. Moreover, the decisions
about these strategies often turn out to be problematic in nature. For achieving this, a specific
process could be utilized. Big data analytics is one of the most important processes to
uncover any type of hidden information by simply testing big data.
This literature would be identifying the problems of the marketing strategies and
decision-making about those strategies with the help of big data analytics. This literature
would also provide relevant solutions to those problems and the implications. Future works
would also be mentioned here.
2. Approach
Method for finding, analysing and comparing literature
The method utilized for searching for literature was:
i) Identification of proper key words that are solely related to the problem
ii) Using Google Scholar for searching all the key words and find out Journal Articles.
Several peer articles, journal articles and conference papers were searched and around
twenty eight papers were eventually shortlisted. After evaluating each of these papers, twelve
journal articles were selected. These papers completely define and help the literature analysis
to be successful.
Organizing the Literature analysis
This literature analysis is organized with the following areas:

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LITERATURE ANALYSIS
i) Influence of big data analytics in marketing strategy.
ii) Influence in decision-making about strategies.
Scope of Literature Analysis
This particular literature analysis is strictly restricted to peer reviewed journal articles.
The specific research problem could be approached from several perspectives. This literature
analysis mainly aims at the solutions for making marketing strategy and decision-making
strategy easier and simpler for any organization.
The scopes of the literature review are given below:
i) Small and medium sized enterprises with mitigating marketing risks.
ii) Decision making about the strategies becoming easier.
3. Literature Analysis
Problems in Marketing Strategy
Marketing strategies are the significant strategies in an organization. A company for
gaining their competitive advantages and maximum profit utilizes those (Pappas, 2016).
However, often few problems are observed within this strategy. These problems become an
important barrier in the way to the success of that organization. The major problems are as
follows:
i) Targeting High Value Sources of Growth: This is the first and the foremost
problem in marketing strategy. High value sources of growth should be effectively targeted
for any successful business (Hallbäck & Gabrielsson, 2013). If a company chooses the wrong
target or of lesser value target, it eventually lowers the overall growth and potential of return-
on-investment.

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LITERATURE ANALYSIS
ii) Dealing with Bulk Data: This is the second important problem in marketing
strategy. The organizations have to collect bulk amount of data about the market for
understanding their position in the market. Any type of wrong data entry can backfire their
plan and their strategy would fail.
iii) Slower Process of Data Analysis: Pappas, 2016 state that, the third important
problem in the marketing strategy is the slower process of data analysis. The world is
progressing with excess speed and every organization should know about their strengths and
weaknesses with respect to analysis of data. If the process of analyzing data is slower than
usual, there is a high chance that the marketing strategy would be a major failure.
iv) Utilizing Insight for Shaping Strategies: The data orientation in any particular
organization might reach to a high level. However, in comparison to that data, the insight
might not reach to its height. New and better customer’s insights are highly recommendable
for any marketing company.
v) Decision Making: According to Hallbäck & Gabrielsson, 2013, this is again one of
the most significant factors in the marketing strategies of any particular organization. The
company should undertake the perfect decision for their business so that there exists no
loophole in the undertaken strategy. Moreover, the company for gaining advantages that are
more competitive as well as profit should properly execute this strategy. There are various
reasons for problems in decision making in any organization (Pappas, 2016). The most
significant reasons for the decision-making problem mainly include impulsiveness, risk
avoidance, ignorance, halo effect, one solution threat, no follow-ups, no delegation and many
more.

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LITERATURE ANALYSIS
Big Data Analytics
Big data analytics is the procedure of examination of all types of varied and large sets
of data or big data for uncovering any hidden pattern, preferences of customers, unknown
correlations and any kind of important information (Kambatla et al., 2014). This type of
analytics eventually helps the organizations in taking any type of decision. The entire concept
of big data is utilized for capturing data, which are streamed into the businesses. The most
significant advantages of this big data analytics is the speed and efficient. It is extremely
efficient and could be easily utilized by any organization (Gandomi & Haider, 2015). The
most important advantages of big data analytics in an organization are as follows:
i) Implementation of new Strategies: This is the most important advantage of big data
analytics in a business. It helps to implement new strategies by reducing the overall
complexities of data analysis.
ii) Analyzing Data without Errors: As per Waller & Fawcett, 2013, the data could be
easily analyzed with the help of big data analytics and this could be done without errors
within it.
iii) Cost Effective: The third advantage of big data analytics is that it is extremely cost
effective (Kwon, Lee & Shin, 2014). It does not incur huge cost and could be easily afforded
by all organizations irrespective of its size.
iv) Better Sales Insights: Big data analytics is also responsible for bringing better
sales insights within the organization.
v) Easy Fraud Detection: Frauds could be easily detected and prevented with the help
of big data analytics (Loebbecke & Picot, 2015).

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LITERATURE ANALYSIS
These advantages has made big data analytics extremely popular and acceptable by
all.
Solutions to the Problems of Marketing Strategy
Big data analytics can be easily termed as the significant game changer for the
marketers. In today’s world, the entire marketing industry is solely driven by data. Any type
of erroneous or wrong data could lead a company to major losses (Moniruzzaman & Hossain,
2013). The problems with the marketing strategy and the decision making about those
strategies could be easily and efficiently resolved by big data analytics.
According to Demirkan & Delen, 2013, the probable solutions to any type of
problems in marketing strategies and decision making about those strategies by big data
analytics are given below:
i) Conversion Optimization: This is one of the significant ways to solve the problem
of marketing strategy in an organization (George, Haas & Pentland, 2014). Big data analytics
help the marketers to draw the statistics of their business. According to the big data analytics,
48% of the total data belongs to customer analytics, 21% to the operational analytics, 12% to
fraud and compliance, 10% to new product and service innovation and the final 10% to the
enterprise data warehouse optimization (Gandomi & Haider, 2015).
ii) Collection of Data: The big data analytics allows the organizations to collect, as
much data is possible for their business (Power, 2014). There is no such restriction regarding
the amount of data. The organization can collect huge amount of data and big data analytics
could easily examine and analyze them.
iii) Properly Timed Content: The third important solution to the problem of
marketing strategy is the properly timed content (Duan & Xiong, 2015). The timing and

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LITERATURE ANALYSIS
content are evenly distributed by big data analytics. For example, the email marketing
platform of an organization eventually optimizes and sends time based peak activities. This is
completely based on big data (Moniruzzaman & Hossain, 2013). Due to even distribution of
time and content, decisions could be easily taken by the organization.
iv) Follows structured Analysis: There are few distinct steps in big data analytics and
by following these steps, the marketers could take their decision easily (Kwon, Lee & Shin,
2014). The steps in big data analytics include:
a) Defining of the Problem
b) Researching
c) Mind mapping and Sketching
d) Feedback
e) Creating Digital Design
f) Feedback
g) Finalizing
These above mentioned steps would be useful for the marketers to undertake any
decision regarding marketing strategies.
Implications of the Solutions
These above mentioned solutions would definitely reduce the overall complexity of
the problems of marketing strategy (George, Haas & Pentland, 2014). Moreover, the
marketers could easily take the decisions about those strategies.

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LITERATURE ANALYSIS
However, often big data analytics could also be a major threat for the organization.
The main problem with this analytics is that it deals with huge amount of data and if there is
any problem in the data, the entire data set would crash.
Future Research Areas
As per Waller & Fawcett, 2013, this literature analysis suggests various areas of
future work. The first significant are would be machine learning. This would be undertaking
the entire world of big data analytics. The next important factor would be that the businesses
would be buying algorithms, instead of software (Power, 2014). The cost effectiveness might
not long last and big data analytics might be costly in future.
4. Conclusion
Therefore, from the above discussion it can be concluded that, big data analytics is the
method for analyzing big data or large set of sets to help in any type of difficult and
information related decision-taking situations. The marketing strategy in any particular
business is the long term and forward looking approach to plan all the significant goals and
objectives in a business. Often the marketers face problem in deciding the right strategy. Big
data analytics plays the most significant role in this type of situation. It helps to reduce the
complexity of evaluating the marketing strategies and thus make the organization successful.
The above literature analysis as clearly depicted the problem of big data analytics and how
this influences the marketing strategy as well as decision-making strategy. The problem
statement, approach, and the literature analysis are properly given here.

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LITERATURE ANALYSIS
5. References
Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision
support systems: Putting analytics and big data in cloud. Decision Support
Systems, 55(1), 412-421.
Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of
Management Analytics, 2(1), 1-21.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and
analytics. International Journal of Information Management, 35(2), 137-144.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of
management Journal, 57(2), 321-326.
Hallbäck, J., & Gabrielsson, P. (2013). Entrepreneurial marketing strategies during the
growth of international new ventures originating in small and open
economies. International Business Review, 22(6), 1008-1020.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data
analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.
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.
Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation
arising from digitization and big data analytics: A research agenda. The Journal of
Strategic Information Systems, 24(3), 149-157.

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LITERATURE ANALYSIS
Moniruzzaman, A. B. M., & Hossain, S. A. (2013). Nosql database: New era of databases for
big data analytics-classification, characteristics and comparison. arXiv preprint
arXiv:1307.0191.
Pappas, N. (2016). Marketing strategies, perceived risks, and consumer trust in online buying
behaviour. Journal of Retailing and Consumer Services, 29, 92-103.
Power, D. J. (2014). Using ‘Big Data’for analytics and decision support. Journal of Decision
Systems, 23(2), 222-228.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a
revolution that will transform supply chain design and management. Journal of
Business Logistics, 34(2), 77-84.