The Determinants of Social Media Engagement for

Fashion Industry in Oman: A Descriptive Analysis

Fatma Salim Al Rabaani and Aiman Moyaid Said

Department of Information Systems, University of Nizwa, Nizwa, Oman

Keywords: Digital Marketing, Customer Engagement, Instagram, Descriptive Analysis, K-means Cluster.

Abstract: The use of social media has completely remodelled the way people interact, communicate, and engage. Social

media platforms play an essential role in reshaping the relationship between customers and companies.

Present companies establish their accounts in social media to reach and engage with their customers, listen

and take their opinion, enhance the purchase decision, and increase the revenue. The main goal of this study

is to determine the factors that affect customer engagement. From 296 Instagram business accounts with

530,366 posts published, the dataset was scraped and used to understand what impacts customer engagement.

Different descriptive analysis techniques were adopted to answer the questions of the study. Among the key

finding of this study, customer engagement is positively affected by the number of comments and shares. The

number of likes of published posts is not influenced. Moreover, video posts attract more customer interaction

than other types of posts. Uncovered the property of three kinds of customer engagement (low, moderately,

and high active).

1 INTRODUCTION

With the widespread of social media platforms,

marketrs change their approach to communication

with their current/potential customers. Social media

marketing is considered an effective and fastest

communication method to attract a large scale of

customers to pay attention to the advertisement and

pursue their purchasing decisions.

Social media is defined as a platform that permits

individuals to design content, engage, or disseminate

information, career interest, and pictures/videos

through workable communication and networks

(Sudarsanam, 2017). According to (Dolan et al.,

2015), social media has empowered customers,

flexibility, and visibility regarding marketing content

that differentiates the interconnect between customer

and organization. It transformed the customers from

passive recipients of marketing content to active

collaboration in the brand message.

Social media platforms such as Facebook,

Twitter, YouTube, and Instagram provide a dialogue

between companies and customers. Instagram, one of

the social media platforms, allows users to publish

text, images, and videos on their account page to

interact with their followers /visitors. (Marketo,

2019) reported that 44% of active internet users use

Instagram to research products. In Oman, 35% of the

population can be reached by advertisements, as

illustrated in Figure 1.

Figure 1: User of Instagram on Oman.

https://datareportal.com/reports/digital-2020-oman.

Small businesses and retailers use Instagram as a

tool to promote and sell their products and services.

Using Instagram in marketing gives both the

customers and advertisers the ability to communicate

with each other. The customers can express their

opinion about the advertising content or ask a

question about it. The advertiser could answer the

Al Rabaani, F. and Said, A.

The Determinants of Social Media Engagement for Fashion Industry in Oman: A Descriptive Analysis.

DOI: 10.5220/0010336511511159

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 2, pages 1151-1159

ISBN: 978-989-758-484-8

1151

question and make an idea about how he could

improve the advertising.

According to (Jordan, 2018), many small

businesses and retailers need to understand how to

deliver the advertisement to their customers. One of the

most significant confrontations they faced is building

an effective social media strategy to improve customer

engagement on their social media web page. Most

companies spend a lot of money on social media

marketing campaigns. According to (Laudon &

Traver, 2014), companies use online marketing

campaigns, which require many efforts and cost a huge

budget, to advertise and attract customers to their

products and services without an increase in revenue.

The challenge is how to accomplish social media

marketing’s advantage to improve the relationship

with customers and enhance revenue. Small businesses

and retailers must spotlight a successful marketing

campaign’s characteristic to achieve a high level of

customer engagement. Understanding the factors that

affect customer engagement on social media leads to

an increase in brand community engagement on social

media. It will modulate the customer’s attitudes toward

the brand and increase company revenue.

According to (Shehu, 2018), with the massive

posts published on social media platforms, businesses

must collect, analyze, and act on customer data

created on social media platforms. It provides an

insight to attain competitive advantage and enclose

brand relationships, product contentment, and service

delivery. The aggregation of likes, comments, shares,

and followers get actionable knowledge that small or

medium companies can use to enhance their products

and services and improve their content and delivery.

Data mining techniques supply a motivating

approach for extracting knowledge from raw data. K-

means clustering algorithm is one of the data mining

models used to classify the data set to clusters based

on the distance between the features of the data. It is

an unsupervised machine learning technique that

groups the data into subsets or clusters. Inside the

same cluster, the data is very similar, while outside

the cluster, the data is dissimilar. In this research,

using K-means cluster to define the different groups

of customer engagement in the fashion industry by

utilizing the features of the data collection like:’ type

of posts published, number of likes, number of

comments, number of shares, and other features’ to

identify the kind of customer interaction.

Determining the different types of customer

engagement helps the companies’ decision-makers

enhance engagement when delivering the marketing

campaign on social media.

This research focused on how the Fashion

Industry could use Instagram’s business account to

promote and market their products effectively. The

objective of this research is to answer the following

questions:

• Which type of posts attracts more interaction,

and at what time of year, these posts get more

interaction?

• Is there any difference in interaction on the posts

type according to years?

• What are the most influential features of the

customers’ engagement?

• What are the prevailing characteristics of the

existing social media accounts in the fashion

industry?

The next section illustrates the overview of the

literature review conducted in this area. The 3rd section

describes the method used, the dataset, and selected

features for the study. The 4th section answers the

objective questions by explaining the results obtained

after analyzing the study’s dataset. The last section

presents the main conclusions of this study.

2 LITERATURE REVIEW

Customer engagement on social media is one of the

marketing objectives that enhance return on

investment. Understand customer engagement and

how it could measure it is one of the challenges that

marketers seek to determine. Many kinds of literature

were conducted to define customer engagement and

provide a variety of concepts. Table 1 summarizes the

definition of customer engagement in literature.

Motivating users to engage in social media

platforms is an important challenge for researchers to

gain insight into consumer engagement. (Khan, 2017)

said comment behavior is a strong predictor to motive

YouTube followers to engage with the video. When

the follower writes a comment, that means he/she is

interested in the content and adds a comment about

what feels about the content. Like, dislike, comment,

and upload reflect the motivation of engagement with

the content published. The same result was founded

by (BİLGİLİER, 2020). The researcher mentioned

the importance of comments written on Instagram to

improve the relationship between the customers and

the company and increase customer engagement. The

type of post published has a significant impact on

lifetime post consumers.

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

1152

Table 1: Customer engagement related concept.

Autho

Conce

t Definition

Perreault &

Mosconi (2018)

Brand and

consumers’

engagement

Customers engage in several behaviors that strengthen their relationship with the

brand, which go beyond the traditional customer loyalty measures such as,

frequency of visits, purchasing behavior, and intended actions [such as, sharing,

commenting, and liking the brand page].

Volkmann et

al.(2019)

User-generated

content

Many consumers voluntarily publish on the internet and express their

experiences, opinions, feelings, and perceptions online on the social network, in

fora, blogs, or product review channels.

Kuntara et

al.(2019)

User engagement

in social

commerce

Factors affecting user participation include trust, information quality, attitude,

community involvement, perceived usefulness, and social support.

Oliveira &

Goussevskaia

(

2020

)

User engagement The function of the number of interactions (likes and comments) with the post,

and the number of followers of the poster

Vadivu &

Neelamalar

(2015)

Customer

engagement

Sequential psychological process in which customers move through to become

loyal towards a brand. In online customer engagement in social media platforms,

it is characterized by customer interactivity with the brand.

This result was founded by (Huey & Yazdanifard,

2015) and (Janani & Prabhalammbeka, 2017). They

found “Status” posts get the highest number of

comments, “video” posts get the most likes, and

“Photos” and “links” get the lowest number of

interactions on customer engagement. Besides, they

found the seasonality “month of published posts” has

a significant impact on user engagement with the

posts published. The type of content published,

“persuasive, or informative,” has a substantial impact

on customer engagement “like and comment”. (Lee et

al., 2018) said that persuasive content has a positive

effect on customer engagement while informative

content reduces the engagement.

Several previous researchers have done the

metrics that used to calculate and evaluate the

engagement on social media. The formula used to

measure the engagement varies between the

researches, but all the researchers adopted the number

of comments, likes, posts, and followers as the most

important metrics. (Vrana et al., 2019) adopted

number of followers, number of following, and

number of likes as metrics to determine customers’

engagement. The more followers an account has, the

more impact the account has. (Barnes & Rutter, 2019;

Muhammad et al., 2018; Segev et al., 2018) also used

the same metrics in their researches. (

Yew et al., 2018)

suggested a new measurement to evaluate the

engagement. They used the average number of likes,

the average number of comments, and the average

number of views for the video posts and out of the

total number of posts in the last three months divided

by the average of reach achieved in the last three

months. (Arman

& Sidik, 2019) suggest new formula

to calculate the engagement because they viewed that

number of comments, likes, and followers as crucial

metrics to determine the engagement and the number

of posts in the page account and the probability for

followers to see the posts. They considered that not

all the likes and comments that post has come from

the business page’s followers. It also may become

from the visitors of the business page. (Mariani et al.,

2017) add the number of shares in their formula to

calculate the engagement. They calculate the number

of likes, comments, and shares for the post and

divided by total posts, then multiplied by 100 to get

the engagement rate. They consider that most of the

business page’s followers or visitors could click like

to the post, some of them write a comment, and who

is interested in the post will share it with others.

Some studies focused on customer engagement

and used different data mining techniques and

algorithms to understand what motivates them to

engage in social media. (Segev et al., 2018) using

regression models (Ridge Regression and Random

Forest), they found that Multi-Regression was not a

beneficial method while feature reduction resulted in

powerful models. (Oliveira & Goussevskaia, 2020)

adopted a classification model (Extremely

Randomized Tree algorithm) to classify the features

that affect customer engagement on Instagram, also

used (Area Under the ROC Curve ‘AUC’) to evaluate

the model. They found that the average text size is the

most notable feature. (Arman

& Sidik, 2019) referred

that used data mining approach, but the authors don’t

describe any algorithm that adopted. They used

correlation analysis and arithmetic mean to analyze

the engagement. (Lee et al., 2018) build NLP

algorithm to understand customer engagement on

FaceBook and adopted (accuracy, recall, precision) to

evaluate the algorithm. This algorithm achieves 99%

accuracy. Besides, adopting a descriptive analysis.

The Determinants of Social Media Engagement for Fashion Industry in Oman: A Descriptive Analysis

1153

(Muhammad et al., 2018) implemented a K-means

algorithm to classify the posts published on Instagram

and used five variables (day, hours, likes, comments,

and location name). The result indicated that the data

set was classified into three different clusters. Also,

they used descriptive analysis to visualize customer

engagement. (Barnes & Rutter, 2020) discussed some

big data and artificial intelligence techniques (V3

convolutional neural network) to describe customer

engagement on social media Influencer posts. They

found that general influencer gains the best

performance while traveling influencers

accomplished greater overall engagement and

implementing some data visualization.

According to (Anitha & Patil, 2019), k-means

clustering is a data mining technique applied to

discover the different customer predilection patterns

in the fashion industry. (

Ližbetinová et al., 2019)

Understanding the data set feature by adopting

descriptive analysis is essential before applying the

clustering. Clustering is a technique to different

entities into a subset of groups. The entities inside the

same group or cluster share the same properties. K-

means algorithm is a popular classification algorithm

(Gurusamy et al., 2017).

3 METHODOLOGY

This research investigates and analyses the Instagram

account page of small businesses and retails from

Oman’s fashion industry.

3.1 Dataset Collection

To identify the main Omani fashion industry business

account on Instagram, WhatsApp Groups was created

to ask regular purchasers from online customers to

suggest three different accounts from the fashion

industry that follow and purchase from them. These

accounts must be Omani ones. In addition to that, the

researchers relied on suggestions of accounts done by

the Instagram platform. As a result of identifying the

fashion industry accounts, 305 were selected. After

checking all the accounts’ status, the researchers

decided to reject some of the accounts because they

were inactive, making the final number of accounts

296. The total number of the collected posts was

530,366 from all the 296 Instagram business accounts

published in a period above seven years from

11/12/2012 until 10/7/2020.

3.2 Dataset Features

The data set’s input features have been collected

from both the business account profile and the posts

published in the account. It is categorized into two

types directly taken from the business account page

or computed from other features. Table 2. explains

all the selected features used for this research with

its description.

3.3 Calculation of Customer

Engagement

The measure used to evaluate customer engagement

was adopted from (Mariani et al., 2017). The reason

for using this formula is because not all the likes,

comments, shares that the post gets come from the

follower of the business account. It may become from

any visitor to the account, as mentioned in (Mariani

et al., 2017) and (Arora

et al., 2019). Equation 1

illustrates the adopted formula in this study.

𝓛

𝓒𝓢

𝓟

 100

(1)

Where “ℒ” denotes the number of likes post, “𝒞”

indicates the number of comments, “𝒮” indicates the

number of shares, “𝒫” indicates the number of total

posts in the account, and “ERƤ” denotes Engagement

Rate for each post.

The study is a descriptive analysis. To answer

research questions, the researchers are going to use

statistical measurement and different visualization

techniques. In addition, the data analysis in this

research is adopting K-means to define the prevailing

characteristics of the existing social media accounts.

4 ANALYSIS AND RESULTS

To acquire a general understanding of how Instagram,

brand page accounts are applied to enhance marketing

engagement customers, first investigate the

descriptive statistic for the selected factor variables of

profile feature account. Table 3 provides the

descriptive statistic value for the features.

Regarding the posts published type, graph Image

is most frequently (401731 occurrences, 75.75% of

total), followed by graph side care (96337

occurrences, 18.16%), graph video has the lowest

frequency (32298 occurrences, 6.09% of total). In

aggregated posts published over the seven years,

more than 50000 posts were published in (May, June,

March), and less than 40000 posts were published in

(August, September). The result indicates the trade of

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

1154

fashion industry in Oman is booming in May due to

upcoming the season of end of the school year, and

Summer holidays, besides, the trade of fashion

industry decreases in August and September as the

interest of consumers shifts to prepare for the start of

the School year. Figure 2. shows the aggregation of

posts published over the seven years in the month.

280160 posts from all the posts published did not

have any comments or shares. When focusing more

on these posts, why didn’t the posts get any comments

or shares? Finding 12443 posts on the account page,

Instagram users disabled the followers or visitors to

add a comment for these posts, while 267717 did not

interest the followers or visitors to add comments or

share it, even if these posts get several likes.

Table 2: Dataset features.

Feature Type of feature Description

Follower count Directly taken Number of followers who follow the page of the business account.

Following count Directly taken Number of following that the user of account following them.

Posts count Directly taken Total number of posts published that in the business account page.

Post type Directly taken Type of post published (three types of posts published Graph Image, Graph Side care,

and Graph Video)

Video view count Directly taken Total number of views that the video post got.

No of likes Directly taken Total number of likes that the post got.

No of comments Directly taken Total number of comments that post gain it.

No of shares Computed Total number of shares that the post gain it.

Comment disabled Directly taken The boolean type determines of the user disabled the Comment for the followers who

saw the post or not.

Last comment date Computed Date Time type, last date that the posts got Comment.

Time publishing Directly taken At the time that the post published

day Computed Day of the post published (get from Time publishing)

month Computed The month the post published (get from Time publishing)

year Computed The year of post published (get from Time publishing)

Ave_ like Computed The average number of all number of likes that the account got from all posts in the

account.

Ave_ comment Computed Average number of all the number of comments that the account got from all posts

in the account.

Ave_ Share Computed Average number of all number of shares that the account got from all posts in the

account.

Is a business

account

Directly taken Determine if the user of the account makes his/her account business account or not.

This feature provided by Instagram for any account can use this feature and then get

analysis details for the account.

Is private Directly taken Some user makes their profile private, then allowed for who want to follow them or

not.

Table 3: Statistic value of profile feature account.

Posts

count

Video

view

count

No of

likes

No of

Comment

Ave_

video

view

Ave_

comments

Followers

count

Count 530,366 322,98 530,366 530,366 530,366 270 296 296 296 296 530,366

Mean 15,140 1,985 62 3 6 2,84 101 15 27 52,62 8

Std 20,090 6,384 324 285 377 11,41 226 175 284 67,56 1,12

Min 18 0 0 0 0 0 1 0 0 175 0

25% 2,123 143 5 0 0 451 17 0 2 10,63 0

50% 6,769 549 13 0 0 1,17 40 1 4 29,68 0

75% 16,009 1,747 38 1 3 2,24 102 3 11 716,9 2

Max 633,02 334,404 30,792 147,07 237,92 169,65 2,96 3,01 4,88 734,53 816,01

The Determinants of Social Media Engagement for Fashion Industry in Oman: A Descriptive Analysis

1155

Figure 2: Aggregation of posts published over the 7 years

in the month.

The Comments and Shares take time for the

followers to add, and if the post attracts the follower’s

interest, the follower will add a comment. The result

supports the previous literature in important

comments to make relationships with customers and

improve the interaction between the customers and

the company (Vadivu

& Neelamalar, 2015).

• Which type of posts attracts more interaction,

and at what time of year, these posts get more

interaction?

The posts type “Graph Video” receives the

highest average rate of likes, comments, and shares

overall the months in the year except in November,

the post type “Graph Image” gets the highest average

rate of shares. “Graph Video” gets the highest average

rate of likes in October and November this is because

upcoming of the season end of year discounts and the

National Day, while “Graph Image” gets the highest

average rate of likes in July and August, and “Graph

Sidecar” gets the highest average rate of likes in June

and August. “Graph Video” receives the highest

average rate of comments and shares in May, while

“Graph Image” gets the highest average rate of

comments and shares in November, and “Graph

Sidecar” gets the highest average rate of comments

and shares in May. Posts type “Graph Video” has the

best interaction. Figure 3. illustrates the difference

between the interaction type of posts in the month.

• Is there any difference in interaction on the posts

type according to years?

The posts type “Graph Sidecar” starts appearing

in 2017 and has average increase interaction (likes,

comments, and shares) over the years. “Graph Image”

has an oscillatory performance of interaction over the

years. “Graph video” gets the highest average rate of

Figure 3: The different of the interaction posts type in

month.

likes in 2015 while getting the highest average rate of

comments and shares in 2020. Figure 4. illustrates the

difference between the interaction types of posts in

years.

Figure 4: The different of the interaction posts type in year.

• What are the most influential features on the

customers’ engagement?

Table 4. demonstrates the correlation coefficient

between the Features for every post published, there

is a significant positive correlation between the

engagement rate and the No of Comments and No of

Shares, 0.879, 0.726, respectively. This result means

that the marketer must focus to the comments and

shares to understand what the customers need and

interest. The relationship between number of

comments and number of shares is very high

positively, 0.929 the comments and shares very

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

1156

related to each other. This means if the follower or

visitor writes a comment for the post published the

probability to share this post with others is very high.

No relationship between Posts Count and No of

Likes, No of Comments, No of Shares, and

Engagement Rate. The result suggests that it is not

important to publish more posts to get follower’s and

visitor’s interaction.

Table 4: The correlation coefficient between the Features

for every post published.

posts

count

No of

likes

No of

Commen

No of

E R

posts

count

1.000 -0.099 -0.009 -0.006 -0.01

No of likes

-0.099 1.000 0.121 0.105 0.09

No of

comment

-0.009 0.121 1.000 0.929 0.88

No of

-0.006 0.105 0.929 1.000 0.73

Engageme

nt Rate

-0.005 0.085 0.879 0.726 1.00

Figure 5. shows the correlation between the

features of average business accounts, there is a

significant positive corelation between Total posts

and Total video posts, 0.67. The Average video view

affects high positively the Average Likes, Comments

and Engagement Rate (0.90) and medium positively

to the average likes (0.42). Followers count affects

high positively to the average likes (0.62). The

Average likes have low positively affected the

Average Comments, Engagement Rate, and Shares,

0.22, 0.23, 0.19, respectively. Average Comment has

a very high positive relationship with the Average

Engagement Rate and Shares, 0.99, 1, respectively.

Average Shares has a very high positive relationship

with the Average Engagement Rate, 0.98.

Figure 5: The correlation between features of average

business accounts.

• What are the prevailing characteristics of the

existing social media accounts in the fashion

industry?

The Elbow curve method helps estimate the

number of prevailing characteristics of the existing

social media accounts in the fashion industry. It is

used to determine the maximum and a minimum

number of clusters in the dataset. This method applied

several K-mean clusters by increasing the number of

K (number of clusters in the dataset) every iteration

and recorded the sum of square error (SSE). The

goodness of cluster function is estimated by

computing the SSE after the centroids coverage. The

SSE is realized as the sum of squared Euclidean of

each point to its adjacent centroid. The lowest value

of SSE is the best for the number of clusters. As a

result, there are two to four different clusters in the

dataset, as figure

6 shows.

Figure 6: Maximum and minimum number of clusters.

Based on clustering analysis adopting K-means

Algorithm (Muhammad et al., 2018), posts published

in business account in Oman’s fashion industry are

divided into three groups. The first cluster has 527921

posts. The second cluster has 62 posts, while in the

third cluster there are 2380 posts. Table 5. describes

the most important features that describe and

categorize the interaction that posts get (Posts type,

Video view count, No of likes, No of shares, No of

comments, day, month, year, and Engagement rate).

For instance, Clusters # 1 shows posts with low

interaction the midpoints of features (posts type of

Graph Image, 55.3 Video view count, 59.6 Likes, 2.2

Shares, 5.3 Comments, the day of Wednesday in June

2017, and 6.1 Engagement Rate). Cluster # 2 shows

posts with high interaction; the midpoints of features

are (posts type Graph Video, 108880.9 Video view

count, 6087.3 likes, 26.9 Shares, 207.3 Comments,

the day of Thursday in May 2018, and 466.9

Engagement Rate). Cluster#3 shows posts with

moderate interaction; the midpoints of features are

(posts type of Graph Video, 11769.6 Video view

The Determinants of Social Media Engagement for Fashion Industry in Oman: A Descriptive Analysis

1157

Table 5: The K-means Features midpoints clusters.

Posts

type

Video view

count

No of

likes

No of

comments

day month Year E_R Interaction type

Cluster0 0.295587 55.32 59.61 2.25 5.37 2.96 6.23 2017.99 6.12 low interaction

cluster1 2 108880.92 6087.32 26.90 207.32 3.65 5.92 2018.76 466.98 high interaction

cluster2 2 11769.66 456.49 24.00 70.61 2.98 5.91 2018.86 60.97

moderate

interaction

count 456.5 likes, 23.9 Shares, 70.6 Comments, the

day of Wednesday, in May 2018, and 60.9

Engagement Rate). Table 5 shows the clusters’

Feature midpoints.

5 CONCLUSION

The current research aims to analyze and investigate

Instagram’s customer engagement in the fashion

industry and who could use Instagram as a marketing

tool. 296 Instagram users account with 530366 posts

published on their Instagram page account was used

in this research. The analysis comes with the

significant result that:

• comments and shares are critical factors that

affect the customer’s engagement on Instagram

fashion industry accounts.

• The interest in publishing more posts does not

lead to attract more customer engagement.

• Most of the posts published get low-

performance interaction. This result shows that the

users of business account pay more attention to

publishing posts than listening to what people say

about their products.

• The post type “Graph Video” attracts more

interaction than the other types of posts.

• When marketers want to establish a marketing

campaign on Instagram, they should adopt the type

post “Graph Video” to receive more followers and

visitors’ interaction.

• When the fashion industry marketers want to

establish a marketing campaign and attract more

interactions and opinions about the products from the

followers or attract new followers to their account, the

best month to develop this campaign in May,

October, or November.

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