=Paper=
{{Paper
|id=Vol-3382/Paper8
|storemode=property
|title=Adopting Data Mining and Social Media Analytics to Achieve Customer Satisfaction
|pdfUrl=https://ceur-ws.org/Vol-3382/Paper8.pdf
|volume=Vol-3382
|authors=Mohamed A. Hamada,Adejor E. Abiche
|dblpUrl=https://dblp.org/rec/conf/dtesi/HamadaA22
}}
==Adopting Data Mining and Social Media Analytics to Achieve Customer Satisfaction==
https://ceur-ws.org/Vol-3382/Paper8.pdf
Adopting Data Mining and Social Media Analytics to Achieve
Customer Satisfaction
Mohamed A. Hamada1, and Adejor E. Abiche1
1
International Information Technology University, Manas St. 34/1, Almaty, 050000, Kazakhstan
Abstract
Every business place priority first and foremost on its customers and products. Companies want
to achieve their best to satisfy their customers with respect to coming feedbacks and potential
new customers. Nowadays social media plays a crucial role in promoting a certain product and
even gathering customers’ feedbacks to achieve a high customer satisfaction levels. Mining
social media is a useful tool both for businesses and clients. The aim of this research paper is
to identify and evaluate effective methods of using social media and data mining in the
educational sector to improve customers' satisfaction level. Interviews and analyzing reports
were used to collect the data about opinions of adopting data mining and social media in order
to achieve customer satisfaction levels. 1
1. Introduction
Rapid development of information technology has opened new ways to improve business processes
and build stronger relationships with customers [1]. Social media is used as a tool for online promotion
and advertising of a product as well as collecting their feedbacks to achieve better performance [2].
Furthermore, companies turned social media into a method of gathering information about customers
to unlock hidden information and build new ways of reaching targeted audiences by personalizing their
promotion tools [3]. In this digital era, the era of social media where products and services are sold
immediately by making a few clicks online, business owners must be flexible and ready to adapt
business processes accordingly. Educational sector is not an exception in this regard. As an example,
this paper will reveal an international school case and will discuss how social media and data mining
can be implemented to achieve customers’ (pupils) satisfaction.
A government scholarship program is availably applicable for the pupils who are citizens of
Kazakhstan at the age of 15-16, who have solid academic achievements. In order to take part in the
scholarship program, the applicants should firstly go through one of the Olympiads on Math, Science
and English subjects. These Olympiads are considered as a first qualifying stage. The winners are
invited to proceed and take part at the scholarship selection exams. The role of marketing and sales
teams in this project is to advertise this opportunity and attract young people with potentials to achieve
academic excellence and develop strong leadership skills. Finding new talents as well as raising
awareness of academic programs at the international schools is the first priority. Thus, choosing the
right social media methodology, and using data mining innovative approaches has become one of the
important strategies to build strong relationships with the customers [4].
2. Statement of the research problem
The statement of the problem of this case study is to distinguish innovative and effective methods
of applying advanced methods such as data mining and social media in the educational sphere to
Proceedings of the 7th International Conference on Digital Technologies in Education, Science and Industry (DTESI 2022), October 20–21,
2022, Almaty, Kazakhstan
EMAIL: mohamed.hamada@iitu.edu.kz (Mohamed A. Hamada); egaemmy@iitu.edu.kz (Adejor E. Abiche)
ORCID: 0000-0002-0442-3663 (Mohamed A. Hamada)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
improve customer journey and as a result their better experience and satisfaction. Improving customer
service has become one of the crucial areas for every industry or organization to focus nowadays,
especially in these times when the market is overloaded [5]. However, as the educational industry differs
from other spheres in the means that the schools do not actually sell goods or provide traditional
customer service, adapting new technology tools such as data mining may also become a challenge. For
this purpose, this research will explore how educational institutions can adapt according to the recent
trends in the use of customer data through social media platforms.
2.1. Purpose of the research
The primary aim of this research is to identify features and benefits of applying data mining and the
use of social media and its impacts on customers in the educational sector. In addition, the researchers
aim is to focus on the analysis of customer’s behaviors from the perspective of marketing and sales
departments of a particular school.
2.2. Significance of the study
The problem of achieving customer satisfaction and improving their retention has been well studied.
Particularly, there are a lot of studies discussing the examples of retail, banking, hospitality cases etc.
Worldwide companies are steadily improving their competitiveness by adopting data mining and
machine learning technologies, developing their social media profiles, and promoting their goods using
new tools[7]. However, the extent of adoption of the data mining in educational sector is still limited.
Therefore, following research questions are stated for the study:
• What are the benefits of using social media and adoption of data mining in the sphere of
education?
• What methods of data mining approach can be used to better understand customer needs?
• What is the impact of using these tools in regard to achieving customer satisfaction?
3. Literature review
Customer satisfaction can be defined in a different way, and many authors have provided their own.
Tse and Wilton (1988) argues that customer satisfaction is a response to the evaluation of perceived
expectations and the actual performance of the product as perceived after its consumption [8]. From
that view, it is obvious that meeting customer expectations is the most important priority in the scale of
preference, this leads to customer satisfaction theory that was defined by Kotler (2000) as “person’s
feeling of pleasure or disappointment, which resulted from comparing products perceived performance
or outcome against customer expectations”
Customer satisfaction = f (perceived performance, buyer’s expectations)
Consumer loyalty or satisfaction is the key promoting idea which estimates the capacity of items or
administrations of a brand in gathering or surpassing the assumptions for clients. For instance, building
consumer loyalty empowers associations to get a few benefits, for acquiring better bits of knowledge
about purchaser purchasing conduct [9] and increasing their deals and overall revenues making higher
levels of client dedication and maintenance in the long haul. Sivadas and Baker-Prewitt (2000) viewed
consumer loyalty's as a vital determinant of an association's prosperity and it impacts purchaser conduct,
repurchase intention, and verbal exchange communication [2]. Considering the significance of
consumer loyalty in bringing different advantages for associations as referenced above. It has become
imperative to reliably check out the vital drivers of this idea regarding distinctive industrial settings as
shopper needs and assumptions will generally change now and again, particularly with the quick
increment of mechanical progressions being joined on flow items and administrations presented by
different businesses [14].
3.1. Data mining and CRM
To discover customer satisfaction, it is appropriate to apply data mining technique, which is a
technique that extract information from the data base and analyze it [10]. By applying this technique,
organization can perceive a clear view of company policies and limitations regarding customers. Data
mining technology is used to collect a lot of information for companies and from companies and analyze
them to find information that will be applicable to them. Such companies can make decisions or
judgments based on the corresponding information [16].
Data mining is not mining in the conventional sense. It intends to extricate data that is significant to
individuals from repetitive information. It's anything but a visually impaired extraction. These
significant data or laws accommodated people or undertakings [6]. For instance, nowadays, it is broadly
utilized in things suggested on internet shopping sites, just as customized pages on certain sites. Tracing
its source is inseparable from data warehouse technology, an innovation used to handle gigantic
business information, searching for the secret routineness behind it, and simultaneously displaying it to
give a reference technology to chiefs, to be in the market to take the high ground and gain a profitable
position. Contrasted and customary information investigation, it has no speculation, that is, it
concentrates and finds helpful data under the reason of no theory [17]. The relationship of organization
with its customers is important concept that called Customer Relationship Management (CRM). In
recent years, in every industry CRM plays significant role for the organization [7].
Within the development of communication technology, CRM has become more available and closer
to organizations, due to opportunity. This is quite similar to how customers gather and exchange vital
knowledge about various products to acquire and consume them. Through active sharing of information
on accessible products and services, the advent of new social media has empowered customers with a
variety of options. For instance, new social media websites, blogs, and other forms of digital
communication have made it possible for consumers to interact with corporate stakeholders and a huge
number of customers at any time, regardless of their geographic location [11]. Customers have gained
important experiences and a higher quality of life as a result of these improvements. Edosomwan et al.
(2011) described social media marketing as a sort of online communication in which people share and
exchange information regardless of where they are. In the same vein, social media has been defined as
a set of web-based or online-operated communication channels and mediums primarily geared to
facilitate interactive information sharing among individuals/users (Esu & Anyadighibe, 2014) [13].
Nikolas et al. (1999) provided the concept of MUSA (Multicriteria Satisfaction Analysis), the main goal
of this concept is to aggregate individual preferences into a collective value function [12].
4. Research Design
For this particular study, mixed research design was used. Recently, this method has been recognized
and used by many researchers in their studies [12]. Mixed approach includes two phases in which
qualitative and quantitative data are handled to answer the research questions. Thereby, by using both
qualitative and quantitative data, it is much easier to understand and gain a more complete picture of
the problem related to specific areas [15].
Qualitative data is commonly used by conducting face to face interviews, so this type of gathering
customer’s data is used to identify customer’s values and needs. Respondents’ answers may differ
somehow, but it is considered as a key to success in order to meet their preferences [13]. Internal
qualitative evaluation oriented with employees is the way of understanding customer preferences and
also provides customer critical areas [4]. In this research interviews with the marketing and sales team
were conducted and also reports on the promotion of the “Teaching English for Intercultural
Communication” school project was analyzed.
The research model identifies how the use of social media can achieve customer satisfaction as they
help to understand customers’ expectations and trust each other in order to attract more satisfied
customers [16].
4.1. Population and sampling
The population for this study is defined as any marketing and admissions departments who would
and business owners of educational institutions who would like to adopt data mining and social media
instruments to build better customer journeys and achieve their satisfaction. In addition, the findings of
this study will be applicable for any people from educational institutions, or people who are involved
in education management, who are interested in improving business processes, especially sales.
The international school where this research was conducted runs three Olympiads on different
subjects as a first and qualifying stage for the annual scholarship program. All Kazakhstan pupils at the
age of 15-16 can apply and try themselves. The scholarship itself gives a great opportunity to the pupils
to acquire international education and finally to be granted with an A-level certificate. The aim of this
program is to attract great talents with a huge potential of future leaders. To achieve this, it is important
to promote this project properly as this project is applicable for a specific set of customers. By customers
it is necessary to clarify that they might be parents or applicants of a set age on their own. Consequently,
members of marketing and admissions (sales flowingly) departments of this school agreed to share their
experience and contribute to this research study.
5. Research Results and Analysis
5.1. Findings of the promotion through social media
In order to answer the third research question, what is the impact of using these tools with respect to
achieve customer satisfaction, the researchers studied the reports of the promotions which were made
based on the data mining approach by using social media tools. To be more precise, promotion of
Olympiads for the scholarship program took place mainly through Instagram, and Facebook. The
reports were studied and were presented in graphics and organized with the summary of the main
promotions of scholarship program and each Olympiad separately. In Figure 1 below, the results were
presented in relation to performance overview, meaning how many clicks were presented, age and
gender distribution and placement per social media platform.
The promotion of the scholarship program took place between July 10th 2021 and September 7th
2021. The following figure illustrates the reach of 593 027. The reach of the audience fluctuated
throughout the stated period.
500 thousand
400 thousand
300 thousand
200 thousand
100 thousand
Figure 1: Performance Overview
Figure 2; introduces gender and age distribution of the scholarship program promotion. It states that
mainly the audience that viewed and interacted with this promotion are between 18-24 years old with a
number of approximately 120 000 people and around 100 000 viewers are at the ages between 25-34
with a slight difference from the previous group. Gender ratio is almost equal with a slight
preponderance in favor of the male, 53 % and 46%.
200 thousand
150 thousand
100 thousand
50 thousand
Figure 2: Gender and Age Distribution
In Figure 3, Placement per platform figure illustrates that the main reach of the audience, about 500
000 people, happened through Instagram platform and about 100 000 people from uncategorized
resources.
600 thousand
500 thousand
400 thousand
300 thousand
200 thousand
100 thousand
Figure 3: Placement per Platform
In order to have a clearer picture, it is better to analyze the results of the promotion of the Olympiads
separately too as in Figure 4. Firstly, the researchers processed the data of the Science Olympiad
advertisement through social media. Overall, this promotion reached a population of 1,945, with male
and female ratio of 32 % and 65% respectively as shown in Figure 5. In Figure 6, the biggest number
from the reached audience were females at the ages between 35-44. About 300 people are females at
the age between 18-24. And the rest are young people of 13-17 ages who might fall into potential
participants. The main platforms for the promotion are Instagram and Facebook with impressive
predominance of the first one.
Figure 4: Promotion of Olympiad
Figure 5: Age and Gender Distribution
40 thousand
30 thousand
20 thousand
10 thousand
Figure 6: Placement of Participants per platform
For the Mathematics Olympiad promotion, the researchers relied on similar data and dimensions.
The potential customers with the number of 1,348 people as shown in figure 7 was carried out. Age
distribution is 24% males and 74% females. Interestingly there is a predominance of reach to young
people at the ages of 13-17 as depicted in Figure 8 who might turn into the Olympiad participants, and
subsequently, females between 18-24 years old and 35-44 years. Consistently, Instagram is considered
to be the main primary platform to place a promotion followed by Facebook as illustrated in Figure 9.
Figure 7: Performance Overview
Figure 8: Age and Gender Distribution
80 thousand 2 thousand
50 thousand 1.5 thousand
40 thousand 1 thousand
20 thousand
Figure 9: Placement by Platform Promotion
5.2. Interview Findings
In order to get deeper understanding the nature of the promotions which were discussed at the
previous section and the ideas on which these social media advertisements were built, the researchers
decided to interview one member from marketing department who implements data mining methods
and works on social media development and one member of admissions department who then dealt with
the product of these promotions and enquiries. The answers of the marketing member will be displayed
as M consequently and the member of the admissions department as A respectively.
According to the answers of both interviewees, social media significantly helped to promote the
scholarship program and reach appropriate audiences and consequently in achieving their satisfaction.
However, the answers differed from the perspective of their specific role in this program.
M: Of course, social media played a crucial role first of all in building the awareness of the
scholarship program and Math and Science Olympiads separately too. In regards of achieving
customer satisfaction, I can underline that nowadays social media is a great tool where you can place
the information about your product, we did posts about the programs, included it in the highlights of
Instagram for example, put the tap links to improve customer journey and make it easier for them to
reach necessary information.
A: Yes, the existence of social media really simplified the communication with potential participants
of the Olympiads as well as with their parents. Because whenever there was a request and I fulfilled it,
I could then support and follow my answer with a specific link to our social media posts. And the
feedback from the parents is really good as it is really convenient when you have visual understanding
too. Also, there were times when customers came from Facebook or Instagram with direct messages
and communication where they received main information and followed to talk to the member of
Admissions and gain more detailed information. And before speaking to the parent, I could check what
he or she was interested about and could prepare useful information and predict customer’s questions
which made a good impression on the applicant.
Data mining also affected the process of building customer journeys and the way they are targeted
and treated. According to the member of the marketing team, they installed Facebook Pixel settings to
the website specific sections and could gain relevant information about visitors’ behavior and by
analyzing this data could take actions on targeting their promotions.
M: Well, we are not involved with the concept of big data actually as in banking or e-commerce
sectors for example, however we do use Facebook pixel ads if we can call it as a tool for data mining.
Basically, what Facebook pixel allows us to do is to gather customer’s data. Precisely, it collects users'
unique IPs, those who visited or started to take actions on our scholarship page or senior school, A-
level sections on a website. After receiving this information, we can filter it and target our actions
accordingly on those who somehow couldn’t finish their journey by applying. In other words, we could
remind and trigger them to take actions, or appear again if they decided to postpone the application
etc. Data mining also affected the settings that we customized to our advertisements in terms of the
age’s ratio, selection of the platforms.
During the interview the respondents also listed some pros and cons of using data mining and social
media.
M: In my opinion, the use of data mining and social media made a huge difference in our working
processes, and one of the benefits of it is that it simplifies your work by helping to understand customers'
behaviors and adapt decision-making accordingly. It also automizes the processes and shortens the
time that you spend on customizing the settings of *promotions and helps to narrow the audience.
However, at the same time it doesn’t mean that the targeted audience will reach the final stage of your
customer journey. So, you shouldn’t rely on it 100%.
A: As a person who directly works with customers, who interacts with them in the real world not as
marketing members, I think social media and data mining contributes to my work too as I can use social
media platforms as an additional path to interact with potential customers and even gain relevant
information to build close relationships. However, sometimes as social media are easily accessible and
convenient for interactions the applicants could text you at any time of the day and would expect your
assistance immediately which can sometimes disturb your work-life balance.
Both interviewees also agreed on the role of social media and data mining approach in this project
as these tools work together in a good way and help to achieve goals in improving customers’
experience. Social media manager added that it plays an image supporting role in this digital era.
6. Research discussions
The aim of this study was to identify features and benefits of applying data mining and use of social
media and its impacts on customers in the educational sector. Relying on three research questions, the
researchers identified that data mining and social media can be well combined when educational
institutions focus on specific projects such as Scholarship programs as in this particular case. Precisely,
one of the examples of data mining for the marketing departments of international schools might be
Facebook Pixel code which can be incorporated into website sections. This allows to trace customer
behavior patterns and help to attract a quality audience by reminding, raising awareness, and calling for
actions and individualizing your communication by understanding the needs. If to list the benefits of
using data mining and social media: it simplifies the working processes, saves time, helps to personalize
communications with customers and reach necessary audiences, however, it also worth mentioning that
you can’t rely 100% on data mining due to the possibility of incomplete information during the process.
7. Research conclusions
Customer satisfaction always has been one of the significant points in every business. Using data
mining and social media tools, companies can analyze and identify customer satisfactions and propose
ways to achieve customer expectations. The sphere of the business that we have analyzed in this paper
also relates to achieving customer satisfaction using social media tools. From the analysis provided
above, we can state that right targeting in social media and data gathering, and mining can describe
customer needs, and helps to identify, as well as reach customer satisfaction. Using mixed research
method, by performing interview on specialists from marketing department and admission department
who definitely have all data regarding the educational preferences of respondents, their level of
educations and possibility to study abroad; we can conclude that social media and data mining is a
bridge between customers and producers.
8. References
[1] C. Baumann, G. Elliott, S. Burton, Modeling customer satisfaction and loyalty: survey data versus
data mining, Journal of services marketing (2012).
[2] D. K. Yogesh, E. Ismagilova, D. L. Hughes, J. Carlson, R. Filieri, J. Jacobson, V. Jain et al., Setting
the future of digital and social media marketing research: Perspectives and research propositions,
International Journal of Information Management 59 (2021) 102168.
[3] T. S. Devaraja, C. Hemagangotri, V. K. Sruthi, An Overview of Customer Relationship
Management and Customer Value in Information Technology Industry, University of Mysore
Hassan (2012).
[4] T. I. Jeff, Chief Satisfaction Officer, Alternative Designs and Methods for Customer Satisfaction
Measurement, SatisFaction Strategies, Portland, Ore, USA, 2002.
[5] H. J. Rajeh, Impact of social media marketing, price promotion, and corporate social responsibility
on customer satisfaction, Jindal Journal of Business Research 6.2 (2017) 132-145.
[6] H. M. Elsayed, M. A. Hamada, A Big Data strategy to reinforce self-sustainability for
pharmaceutical companies in the digital transformation era: A case study of Egyptian
pharmaceutical companies, African Journal of Science, Technology, Innovation and Development
(2022) 1-13.
[7] K. M. Rajesh, J. Venkatesh, A. M. J. Rahman, Data mining and machine learning in retail business:
developing efficiencies for better customer retention, Journal of Ambient Intelligence and
Humanized Computing (2021) 1-13.
[8] K. David, P. C. Wilton, Models of consumer satisfaction formation: An extension, Journal of
marketing research 25.2 (1988) 204-212.
[9] K. Seiders, G. B. Voss, D. Grewal, A. L. Godfrey, Do satisfied customers buy more? Examining
moderating influences in a retailing context, International Retail and Marketing Review 8.1 (2012)
38-60.
[10] M. R. Aimee, Thorough Literature Review of Customer Satisfaction Definition, Factors Affecting
Customer Satisfaction and Measuring Customer Satisfaction, International Journal of Advanced
Research 7.9 (2019) 828-843.
[11] U. Y. Burcu, A. R. Kumru, O. Zeki, Customer satisfaction using data mining approach,
International Journal of Intelligent Systems and Applications in Engineering (2016) 63-66.
[12] T. Ch. Wee, Examining the mediating role of employee job engagement on employee retention
among gen Y in Malaysian manufacturing sectors, PhD diss., Universiti Utara Malaysia, 2019.
[13] M. F. Nikolaos, E. Ioannidou, E. Grigoroudis, Customer satisfaction using data mining techniques,
Decision Support System Laboratory University Campus, kounoupidiana, China, 1999.
[14] L. Mukankusi, F. Celimene, M. Louicius, A. Lawson-Body, A Qualitative Study of Customer
Satisfaction from an Electronic Commerce Perspective, Issues in Information Systems 9.2 (2008)
25-36.
[15] M. Nabi-Meybodi, S. Alidousti, A mixed methods research scheme for identifying the contextual
success factors of social networks sites (2015).
[16] L. Yangqianzi, Research on efficient data mining algorithm for large scale data sets [J], Journal of
Hunan City University: Natural Science Edition 25.4 (2016) 111-112.
[17] D. Jianghua, H. Qing, Cross-border E-commerce Course Construction Based on Data Mining
Algorithm, Journal of Physics: Conference Series, IOP Publishing 1852.2 (2021) 022040.