Icdec2020_presentation_slides_22

1. Rim JALLOULI
Professor of Marketing and Innovation
University of Manouba- Tunisia
Keywords: Social Media, Data
Analytics, Big Data, Marketing
Decisions, Clustering.
Clustering of Social Media Data
and Marketing Decisions
Teissir BENSLAMA
PhD Student
University of Manouba- Tunisia

2. 2
INTRODUCTION
PROBLEMATIC
SOCIAL MEDIA DATA ANALYTICS, MARKETING DECISIONS, AND TECHNIQUES
Social Media Data Clustering and Marketing decisions: Methodology
Main results and discussions
Conclusion:Future Research and Limitations
PLAN
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3. 1.
INTRODUCTION
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4. ✓ Trip Advisor (2000)
✓ Wikipedia (2001)
✓ LinkedIn (2002)
✓ Friendster (2002)
✓ MySpace (2003)
✓ Facebook (2004)
✓ Twitter (2006)
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Technological revolution Social media

5. ✓ SOCIAL MEDIA DATA
Represents a large part of
Big Data and are
characterized by complex
and unstructured formats
✓ which makes their
analysis
➔ Difficult task
The challenge for researchers
and decision-makers is to find
a path to facilitate the analysis
of these huge data in order to
extract relevant information
and to improve marketing
decisions .
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6. 2. PROBLEMATIC6

7. ✓ To analyze Social Media Data
previous research proposed
several methods and techniques
Data Mining
Visualisation Machine Learning
CLUSTERING
Techniques
K-means
Hierarchical
Clustering PAM
Clustering …
✓ Numerous articles
on analyzing Social
Media Data for
Marketing using
Clustering have
been published…
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8. ✓ There has been a
rapid increase in the
number of
publications in the
areas of Social Media
Data and marketing,
in which several
Clustering methods
have been proposed
Zhang
et al.,
2017
Bello-Orgaz
et al., 2020
Jisun et al .,
2017
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Lanni et
al., 2019
Despite this increase, there is a lack of articles organizing these publications
according to Clustering techniques and added value.

9. Thus, it will be useful to present a review and
a classification of research articles on Social Media
Analysis in the field of marketing using Clustering
➔ to provide an overview to researchers and
managers looking to use these techniques.
The aim of this
paper is to answer
the following
questions
What are the
techniques for
aggregating Social
Media Data?
What are the
marketing decisions
generated by Social
Media Data
Clustering?
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10. 3. SOCIAL MEDIA DATA
ANALYTICS, MARKETING
DECISIONS, AND TECHNIQUES
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11. Volume Variety Velocity Value Veracity Variability Valence
.
Easy and free access to Social Media has pushed users to
publish content easily and without limits, and to generate
voluminous and unstructured data called Big Data or Social
Big Data. (Lyu and Kim, 2016).
TWEETS Comments Posts ReviewsLikes
Therefore , Big Social Media Data adopt the same characteristics of Big Data Saggi and Jain (2018).
Simply extracting these complex data from Social
Media has no real value !!
➔ Social Media Big Data Analysis brings significant benefits in many
disciplines for effective decision making, including marketing.
ANALYTICS
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12. The use of Social Media Data for marketing
decision making has recently attracted the
attention of both practitioners and researchers.
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Many researchers proposed new techniques and methods to
enable the marketing decisions by analyzing data available on
Social Media.
Marquez et al.
(2019)
Koubaa and Jallouli
(2019)
Rathore et al
(2020)
Van Dieijen
et al. (2019)

13. The problem facing companies today is the lack of capabilities to analyze
the information collected by Social Media tools.
Kaabi and Jallouli (2019)
Therefore, to effectively analyze Social Media Data, there
is an urgent demand for new techniques and analytical
methods to process these massive and complex data.
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14. Rehman et al
(2016)
classified the main
techniques of Big
Data Analysis into:
Machine
Learning
StatisticsData Mining
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15. 15
Galetsi et al
(2020)
indicated that the
main techniques
of Big Data
Analysis are:
Optimization
Machine
learning
Statistics
Modeling
Simulation
visualization
data
mining
Text
mining
Web
mining
Forecasting

16. Recently these techniques have been applied by many researchers to provide insights
to managers and marketing decision makers:
Lin, et al (2018)
used analysis of Twitter data
with natural language
processing to examine the
impact of luxury brand
Social Media marketing on
customer engagement.
Liu, et al (2019) Jabbar, et al (2019) Kirilenko, et al (2019)
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17. The selection
of Clustering
as an analysis
technique is
argued byby
present a literature review map and a classification of research articles on Social Media metrics
and analysis in the marketing field. Several methods and techniques were used: Artificial
intelligence, Data Mining and visualization.
Misirlis and Viachopoulou (2018)
showed that Data Mining techniques are widely used
by researchers in Social Media Data Analysis.
This paper adopts the same methodology but we will take, as
a criterion, a single Data Mining technique: "Clustering".
the lack of
previous research
classifying articles
on Social Media
according to this
technique.
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18. 4. Social Media Data
Clustering and Marketing
decisions.
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19. Bello-Orgaz (2020):
“Clustering can be described as a blind search on a collection of
unlabeled data, where elements with similar features are grouped in sets.
Elements included in the same cluster should be similar, and elements
included in different clusters should be dissimilar.”
K-means
Fuzzy C-means
Substractive Clustering
Hierarchical Clustering
PAM Clustering
Simple to apply
Cheap
Effective
Given these advantages, Many marketers have applied Clustering and
processed the Social Media Data extracted by companies to obtain valuable
solutions for marketing strategies and decision-making.
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20. The methodology in this paper is guided by the three stages of a content
analysis as stated by Bardin (2009):20
The Purpose of
this study is to
examines the marketing
literature to explain and
clarify the measures and
the effect of the
Clustering' use for Social
Media Data in marketing.

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Pre
analysis:
Search on
Science
Direct and
Google
Scholar using
keywords.
Exploitation of the
material:
Studying the
articles➔ The
search process
yielded 33 articles.
Treatment, inference and
interpretation of results:
revised the 33 articles and
rejected articles with content
not compatible with our field
of research. The research
has thus filtered 20 papers
from 2016 to 2020.
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22. ➔ These categories are inspired from metrics used by Misirlis and Viachopoulou (2018).
➔ The results of this research are presented in five tables. Each table classifies the
studied papers according to one retained category.
Social Media
platform
Method of analysis Clustering techniquefield of study marketing objectives
and added value
After performing a floating reading, we carried out a thematic analysis
and established a thematic content analysis grid.
(Robert and Bouillaguet (1997), referring to one of the following
categories:
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23. 5.Main results and discussions23

24. Regarding the platforms used by researchers in the 20
studied articles, Twitter and Facebook are the most
used Platforms.These results show that there is a
need to look for future contributions using platforms
other than Facebook and Twitter.
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Table 1. Social Media platforms’ classification used in the 20 articles: An article can
belong to several categories:

25. According to Table 2 most of the articles use the K-means technique which
represents the simplest and best-known technique among Clustering
techniques (9 articles out of 20). Therefore, there is a utility to apply
techniques other than K-means in future studies.
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Table 2. Classification of Clustering techniques used in the 20
studied articles: An article can belong to several categories

26. According to Table 3 the most used method with Clustering is the
Sentiment analysis , The use of sentiment analysis with Clustering
techniques is therefore effective in extracting valuable marketing insight.
The results shown in Table 3 can also encourage researchers to apply new
methods or rarely adopted methods and explore their effectiveness.
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Table 3. Classification of type of analysis used in the 20
studied articles: An article can belong to several categories

27. Sixteen fields of study were covered in the studied papers and are listed in Table 4, For each field the
application of Clustering techniques offered net advantages and added values in the marketing area
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Table 4. Marketing objectives / Value added according to field of study and Clustering technique

28. For example: K-means technique was largely the most adopted technique in the context
of travel, industry, banking, cosmetics, education, rare events and rural e-marketing.
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29. Findings:
■ .
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helped extract market knowledge by understanding customer preferences and offering a
strategy for businesses.
K-means Clustering
Hierarchical Clustering
was used in three papers to improve marketing strategy with a focus on supply chain
management and e-commerce competitive strategy.
Sequential Clustering
was selected to support the behavior modeling.
The hybrid variable-scale Clustering
helped managers discover potential customers according to users’ marketing preferences
Latent Semantic Clustering
was implemented to identify the product demand trends and enrich
marketing B to B strategies in an earlier stage

30. 6. Conclusion:
Future Research and Limitations
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31. This
Study
aimed to highlight the
importance of the
Social Media Data
Clustering for
marketing decision
orientation
highlights a large set
of fields that benefited
from using Clustering
tools
highlights a list of
methods that provides
a good starting point for
researchers in future
works
can help managers have a
clear idea on the
Clustering techniques of
Social Media Data
Analysis, and choose the
appropriate technique.
invites academics,
practitioners and
researchers to focus more
on a combination of data
analysis capabilities and
marketing insights.
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32. ➔ Another recommendation would be to use software allowing
the automatic search of more articles.
The main drawback of this study
is the limited number of the
sample of reviewed papers.
➔ This article encouraging future
investigations:32

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