Saudi Electronic University Digital Marketing and Expansion of Technology Paper

Saudi Electronic University Digital Marketing and Expansion of Technology Paper

Digital Marketing and Expansion of Technology Paper


the required to be written:

1-General introduction

2-Research question

3-Research Objective

4-Significance of the study _ 1 Managerial Relevance 2_ Scientific Implications.


Saudi Electronic University

Machine Learning and Data Analytics in Digital Marketing
The introduction of the internet and internet-enabled devices shifted marketing strategies to
the digital platform. In the United States alone, 89 percent of consumers search for products on
the internet before shop visitations (AISOMA., 2018). The information suggests a considerable
amount of consumer data available on the internet that organizations can use in their digital
marketing strategies. Organizations have put aside their traditional operations and adopted
machine learning and data analytical tools to understand digital marketing in general (Miklosik
& Evans, 2020). Most marketing managers find machine learning and data analysis tools
application in specific marketing research challenging. Previous research in the field
substantially addressed how managers can integrate machine learning and data analytics on
digital marketing with minimal attention to specifications. Researching the specific digital
marketing areas will help managers apply machine learning and data analytics within the
particular market segments, which will eliminate errors and fear of massive data that Miklosik et
al. (2019) found as a potential issue among marketers today.
Statement of the problem
Marketing managers are slow in the implementation of machine learning and data analytics.
Previous research has identified that the slow performance has been a consequence of limited
information applicable to specific areas that significantly impact digital marketing.
Literature Review
The adoption of machine learning and data analytics in digital marketing has failed to catch
up with the rate of the creation of machine learning-based analytical tools (Miklosik, 2020). A
2018 article posted by the Digital Marketing Institute on How to Apply Machine Learning to
Your Digital Marketing Strategy indicates the current digital marketing environment’s
significance. However, such articles have proven worthless to many digital marketers because
the available data is raw, huge, and unspecific to apply in the dynamic digital market (Miklosik
& Evans, 2020). A better understanding of the target audience using machine learning-based
analytical tools is becoming a challenge to many digital marketers despite a projected success in
the future of digital marketing. The utilization of machine learning techniques on processing
large amounts of data has also failed to expedite digital marketers’ machine learning use within
their organizations (Miklosik et al., 2019).
Project Objectives
1. To identify the reasons for the low utilization of machine learning and data analytics in
digital marketing among marketing managers and find the possible ways of accelerating
2. To find the potential digital marketing areas where marketing managers need
specifications to promote the tools to compete with the digitalization process in the field.
3. To identify other reasons why machine learning implementation cannot compete
averagely with machine learning digitalization rate.
Target Population (Sampling Technique and Sample Size)
The research intends to select participants in senior management positions in small,
medium, or giant corporations in the state of Arizona. They must have 3 to 5 years of digital
marketing experience with excellent mastery of information systems in Search Engine
Optimization (SEO) and Pay per Click (PPC). SEO and PPC areas of expertise contribute five
participants making the total number of all the participants in the project to be ten. All the
participants are active employees in firms dealing with advertisement, businesses dealing with
media space, and organizations playing intermediaries in marketing. Six of the participants
belong to the SEO, while four belong to the PPC information systems.
Data Collection Approach
The participants’ collection of data will be through in-depth interviews after a filling in
questionnaires provided for them. The approach is to gauge the accuracy of the participants’
responses with regards to the research problem. As a qualitative research, gaining more in-depth
insight into managers’ slow implementation against technological development speed in the field
was crucial. The participants were aware of the research, and an assurance of compliance,
honesty, and diligence is mandatory from the respondents before the data collection commences.
Data Analysis
Analysis will entirely depend on the arrangement of the information gathered from the
respondents. Responses are about awareness of handling massive data, understanding and using
all the updated forms of machine learning technologies and data analytics, and the possible fears.
The studying and final analysis of the data will happen manually because the sample population
was small hence invalidating time wastage as a potential disadvantage of the strategy in studying
the data (Esser & Vliegenthart, 2017).
Potential Scope of the Project
The study is relevant to all students undertaking computer science as a course. Apart from
using the document as a source of reference in their research, they will use it to identify other
potential research areas. Organizational managers, digital marketing managers, and employees in
advertising corporations can extract information that can facilitate their online marketing
strategies in line with the emerging technologies in the field.
Project Implementation Plan
Time Frame (Gantt-Chart)
Activities Duration
Time (Month)
February March April May
Literature Review
Data collection
Report writing
Submission of final Report
AISOMA., (2018). The importance of machine learning in your digital marketing strategy.
Retrieved from:
Digital Marketing Institute, (2018). How to Apply Machine Learning to Your Digital Marketing
Strategy? Retrieved from:
Esser, F. & Vliegenthart, R., (2017). Comparative Research Methods. John Wiley & Sons, Inc.
Retrieved from:
Miklosik, A. & Evans, N., (2020). Impact of Big Data and Machine Learning on Digital
Transformation in Marketing: A Literature Review. Retrieved from:
Miklosiki, A., Kuchta, M., Evans, N. & Zak, S., (2019). Towards the adoption of machine
learning-based analytical tools in digital marketing. IEEE Access.

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