Enterprise Competitive Analysis and
Consumer Sentiments on Social Media
Insights from Telecommunication Companies
Eric Afful-Dadzie
, Stephen Nabareseh
, Zuzana Komínková
and Petr Klímek
Faculty of Applied Informatics, Tomas Bata University, T.G Masaryka 5555, 760 01, Zlin, Czech Republic
Faculty of Management and Economics, Tomas Bata University, T.G Masaryka 5555, 760 01, Zlin, Czech Republic
Keywords: Sentiment Analysis, Competitive Analysis, Telecommunication Companies, Social Media, Facebook,
Abstract: The utilization of Social media tools in business enterprises has tremendously increased with an increased
number of users and a corresponding upsurge in time spent online. Online social media services such as
Facebook and Twitter are used by companies to introduce new products and services, provide various
supports and interact with customers on daily basis. This regular interaction of businesses and consumers
results in huge amount of customer-generated content which is becoming a source of insight in analysing
the often erratic consumer behaviour. For companies to harness the business potential of social media to
increase competitive advantage, sentiments behind textual data of both their customers and that of their
competitors must be keenly monitored and analysed. This paper demonstrates how companies especially
those in the Telecommunication industry can seize the opportunity presented by social media to mine
textual data to gain advantage over competitors by cumulatively understanding consumer opinions,
frustrations and satisfaction. Using Facebook and Twitter sites of the top three telecommunication
companies in Ghana: MTN, Vodafone and Tigo the paper reveals insights from unstructured texts of
customers of these three companies. The results show (1) the exponential growth of social media users in
Ghana (2) impact and numbers behind active social media participation in the telecommunication industry
(3) the power of social media opinion mining for competitive analysis (4) how business value could be
extracted from the huge unstructured textual data available on social media and (5) the company that is
more responsive to customer concerns.
Online social media is now an integral part of our
lives. Recent research works by (Wollan and Smith,
2010; Barlow and Thomas, 2011; Qualman, 2009;
Safko, 2010) corroborate the exponential growth of
social media as a new strategic asset for businesses.
In particular, (Barker, 2008; Sinderen and Almeida,
2011; Weber, 2009; Gillin and Schwartzman, 2011)
enumerate some of the ways social media can be put
to use by businesses. Some of these are (1)
identifying new product ideas (2) finding new
business opportunities (3) creating brand awareness
(4) strengthening customer relationships and (5)
establishing contacts with employees, partners and
even with competitors. A recent study report by
McKinsey (McKinsey, 2013) on how organizations
are using social media tools also reveals a range of
benefits for business enterprises as shown in table 1.
For example 69% of the respondents reported an
increase in how effective marketing on social media
has been to their companies with 52% reporting an
increase in customer satisfaction. Despite these
purported benefits, Harvard Business Review (HBR,
2010) posits that though companies are aware of the
huge business potential of social media, most of
them are only making investments for the future
because they still doubt or view the potential as
Even as the debate on the potential of social
media to businesses lingers on with little work from
academia to support the discussion according to
Jussila et al. (2014), the wealth of textual data from
social media continues to pile up on daily basis.
Textual data especially those expressing
concerns, frustrations and acceptance from
Afful-Dadzie E., Nabareseh S., Komínková Oplatková Z. and Klímek P..
Enterprise Competitive Analysis and Consumer Sentiments on Social Media - Insights from Telecommunication Companies.
DOI: 10.5220/0004991300220032
In Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA-2014), pages 22-32
ISBN: 978-989-758-035-2
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Table 1: Business and Web 2.0 - Benefits from customer
use to organizations. Source: McKinsey.
Benefits (%)
Increasing effectiveness of marketing 69
Reducing marketing costs 47
Reducing customer support costs 36
Reducing travel costs 47
Increasing customer satisfaction 52
Increasing revenue 24
Increasing number of successful innovations 22
Reducing time to markets 28
customers are rich in knowledge which needs to be
mined for insights. The wealth of knowledge behind
customers’ comments, posts and tweets on “wall
pages” of companies could prove valuable in
identifying popular brands and level of customer
satisfaction among others (Bradley and McDonald,
2011; Governatori and Iannella, 2011). For example
the ‘‘like” feature on Facebook which allows users
to approve a post can prove valuable in identifying
types of users and their interests in a service or
product (Lipsman et al., 2012).
The emergence of online social media now gives
consumers a rare but powerful influence for publicly
expressing their opinion and thoughts on products
and services. This brings revolution in the way
customers and companies interact by proffering new
ideas about customer relationship and brand
management. To process, aggregate and gauge the
influence of customers’ opinions, sentiment analysis
and text mining in general are used. Feldman (2013)
explains the role of sentiment analysis in
organizations and how it could be employed to
monitor the activities of customers in the various
social media sites in real time.
In the following sections, we briefly explained
the concept of sentiment analysis and its related
applications, followed by research questions posed
for this paper. Next is the methodology including the
samples and procedures used. A case study of the
sentiment analysis approach is conducted using the
top three telecommunication companies in Ghana.
The key findings are discussed followed by how
responsive the companies are to customer specific
Sentiment Analysis sometimes referred to as opinion
mining often combines the study of Natural
Language Processing (NLP), Statistics and Machine
learning to extract and classify the sentiments of a
textual input. There exists two basic sentiment
analysis tasks namely subjectivity and polarity
detection. In the subjectivity approach, prediction is
made on whether an inputted text is subjective or
otherwise whiles the polarity technique makes an
overall prediction of whether a subjective text is
positive, neutral or negative (Buckley and Paltoglou,
2012). The polarity technique learns to classify the
polarity (positive or negative) of a statement,
comment or post and determine whether the
sentiment behind a statement or comment is positive,
negative or neutral (Koppel and Schler, 2006; Pang
and Lee, 2008; Dave et al., 2003; Pang et al., 2002;
Wiebe, 1994). In the process a document is flagged
as positive, negative or neutral. A score of zero
denotes a neutral sentiment.
Sentiment analysis, unlike classical text mining
which focusses on topical words, picks only
sentiment signals for real time analysis (Pang and
Lee, 2008).
Additionally, two main approaches are used in
the task of automatically extracting sentiments from
text. These are text classification and the lexicon-
based method. In the former approach, a classifier is
typically built from labeled instances of texts or
sentences in a supervised classification
2002). This employs the use of statistical and
machine learning techniques. Some of the most
popular algorithms and techniques employed in text
classification based sentiment analysis include but
are not limited to support vector machines, naive
Bayes, Ada Boost, k-nearest neighbours, and
maximum entropy (Pang et al., 2002; Cao et al.,
2013). These techniques are used when appropriate
with Natural language processing and statistical
On the other hand, the lexicon-based approach
works by computing the polarity of a document
based on the semantic orientation of words or
phrases in the document (Toboada et al, 2011;
Vaithyanathan, 2002) In practice, the lexicon-based
approach depends on dictionaries of words marked
with the word’s semantic polarity in computing the
overall polarity of a document. Some of the widely
used dictionaries for lexicon-based approaches are
SentiWordNet and Wordnet though a customized
dictionary can be created (Tong, 2001) using seed
words to expand the list of existing words as
demonstrated by Taboada et al (2011). In this paper,
we use the lexicon-based approach with the
SentiWordNet (Esuli and Sebastiani, 2006)
dictionary to generate the sentiment scores.
Sentiment analysis has been applied to a wide
range of domains and disciplines. Notable among
these are in the area of consumer product and
services reviews (Feldman, 2013) and in voter
sentiments in politics (Pang and Lee, 2008). For
example (Zhang and Wan, 2013) used sentiment
analysis to find possible weaknesses of products
from customers’ feedback. Kang and Park (2014)
designed a new framework that uses sentiment
analysis and VIKOR method to measure customer
satisfaction in a mobile service. Ghiassi, Skinner and
Zimbra (2013) also combined neural network with n-
ngram for twitter sentiment analysis and to show that
their proposed Twitter-specific lexicon is
significantly more effective in classification recall
and accuracy metrics than some of the traditional
twitter lexicons. A sentiment analysis based on
unsupervised and domain-independent model was
designed by (Bagheri et al., 2013) to detect explicit
and implicit aspects in reviews whiles (Bai, 2011)
proposed a heuristic search-enhanced Markov
blanket model to predict consumer sentiments from
online text.
The interest in sentiment analysis has brought
about a wide range of tools both commercial and
open source. Dyer (2013) lists some of the top 50
sentiment analysis tools. These tools help find
insights and patterns from unstructured textual data
(Sashi, 2011; Zeng et al., 2012b).
This paper used a python programming language
module called pattern together with SentiWordNet
to generate the sentiment scores of user comments
for the research. In the following sections we explain
the sentiment analysis architecture adopted, the
methodology guiding the research and subsequently
use a case study of consumer comments of the top 3
Telecommunication companies in Ghana. The
resulting sentiment scores are then used for a
competitive analysis.
3.1 Research Questions
Studies have shown a gradual interest in user-
generated content on social media (Feldman, 2013;
Akehurst, 2009; Aggarwal et al., 2011). This paper
employed opinion mining to do a competitive
analysis using unstructured textual information on
Facebook and Twitter sites of the top 3
telecommunications company. The data was
collected from 02-02-2014 to 07-03-2014. The
following research questions guided the study.
1. What patterns can be found from their
Facebook and twitter sites respectively?
2. How do customers feel about their products
or brand?
3. Which Telecom company is most
competitive as far as issues of customers are
3.2 Methodology
The study was carried out in phases to help answer
the research questions. First, descriptive quantitative
data was collected from the respective individual
social media sites. In particular, we collected the
number of tweets, followers and following from
Twitter and the likes, talking about this and notes
features from Facebook. Relevant for the research
were also the demographics and geographical
location of users, number of postings from the
companies, comments made by users, time of the
day when most posts are made and the frequency of
postings between 02-2014 and 02-03-2014 as shown
in figures 2-6 respectively. Next, we applied
sentiment analysis techniques to analyse and flag
each user comment as negative, positive or neutral.
This helped in discovering the sentiments behind
each user comment, the manner and pace each
company responds to user concerns and the general
pattern of how the three companies have been active
on social media in relation to their customers.
The sentiment analysis of user comments on
social media adopted for the competitive analysis of
telecom companies follows the architecture shown in
figure 1. The methodology adopted is basically
broken into three steps below.
Step1: Text pre-processing:
In the pre-processing stage, we first used Repustate -
an online analytical tool to ‘scrape’ user comments
from the social media sites (Facebook and Twitter)
of the 3 top telecommunication companies. Each
user comment, post or review extracted was treated
as a document. The collection of documents-the
corpus was saved in comma separated value format
(CSV). Then the documents in the corpus were each
converted to text and pre-processed using linguistic
tools basically of tokenization and stemming in
python pattern module. The pre-processed corpus
was loaded together with the SentiWordNet
dictionary into python for subsequent processing.
Step2: Text processing
The sentiment analysis approach adopted for this
research is the lexicon-based approach. First, each
document (e.g., post, tweet, review etc.) collected is
tokenized into a word list. Next each token’s log
probability is identified in the word list. The log
probabilities of each token are then added together to
determine the probability of each sentiment for the
entire document. To determine a binary ‘positive’ or
‘negative’ label with scores for each document in the
corpus (user comment), the SentiWordNet scores are
accumulated with the more subjective comment
having a higher score.
Figure 1: Lexicon-based sentiment analysis architecture
for social media.
Step3: Python pattern vrs other software tools
To ascertain the effectiveness of the sentiment
analysis techniques we adopted and subsequently the
polarity scores for each user comment, comparison
was made with other sentiment analysis tools
notably RapidMiner and Python NLTK 2.0.4 text
classification. The RapidMiner sentiment analysis
tool detected the polarity of a document without
sentiment scores. However the results turned out to
be consistent with our results in terms of the polarity
(negative, positive or neutral) assigned to each of our
user comments. In the case of Python NLTK text
classification, whiles almost all the polarity labels
of ‘positive’, ‘negative’ or ‘neutral’ label for each of
our documents were consistent, the scores assigned
to each user comment turned out to be slightly
different. For instance in Table 5 row 3, the
comment “
MTN not good at all” though a negative
polarity in both Python pattern and NLTK text
classification, was assigned a score of -0.6 in NLTK
compared to a -1 in Python pattern module.
3.3 Case Study
The Telecommunications Industry in Africa
continues to grow at an unprecedented rate
(McKinsey, 2010; Chavula, 2013; Mahmoud and
Hinson, 2012). In Ghana, there are currently six (6)
major operators providing wide range of services
such as cell phone telephony, broadband internet,
and mobile financial services among others. These
are as at 2013, Expresso, Millicom Ghana known in
the market as Tigo SCANCOM known as MTN,
Vodafone Mobile, Airtel, and Glo Mobile. According
to the National Communications Authority (NCA); a
body mandated by the Government of Ghana to
among other things, grant licenses, monitor telecom
service quality and protect consumers; Ghana’s total
mobile subscribership currently stands at 28,026482,
a growth of 7.4% from 2012 (NCA-Ghana, 2013). In
spite of the apparent growth, service quality
continues to be a challenge for consumers prompting
the NCA-GH to slam five (5) of the operators a total
fine of USD 464,413.50 in 2011. The advent of
social media has therefore provided consumers in
Ghana, a platform to interact with telecom providers
in real-time; venting frustrations, reviewing products
and generally receiving customer supports.
It is on this premise that we use sentiment
analysis to find how telecom companies in Ghana
competitively respond to issues of customer
concerns and also generally delve into what
consumers think of these companies. We use the top
three telecom companies (by subscriber base) in
Ghana according to (NCA-Ghana, 2013). These are
MTN, Vodafone and Tigo respectively in the order of
customer size. As at December 2013, MTN had a
subscriber base of 12,929,528 users with Vodafone
and Tigo recording 6,048,792 and 4,021,225
The selection of the three (3) telecommunication
companies for this research is necessitated by their
strong presence on social media compared to the
other operators. MTN, Vodafone and Tigo interact
with their customers almost on daily basis The time
period of Feb 2 –March 7 was also chosen because
of two important events within this period. These
events are Valentine’s and Independence Day
celebrations. During these events, a lot of telephony
activity is recorded because of the large numbers of
people both youth and elderly who access
telecommunication services. Our initial findings in
this research reveal that MTN made an average of 1
and 9 posts on their Facebook and Twitter pages
respectively between 02-02-2014 to 07-03-2014.
Vodafone made an average of 4 and 2 posts on
Facebook and Twitter respectively whiles Tigo
posted an average of 2 and 1 message respectively as
shown in table 2 and 3.
In figures 2 and 3, the daily trend of user
comments posted on Twitter and Facebook is shown.
The trend shows that within the period of the
research, MTN users interacted more with their
telecom operator on Facebook than Vodafone and
TiGo users. On Twitter, TiGo users exchanged more
Step 2: Text Analysis
Facebook &
Comments extraction
from user
Step 1: Text pre-processing
Figure 2: Trend of Tweets from Feb 2 - March 7, 2014 for the 3 companies.
Figure 3: Trend of Facebook posts from Feb 2 - March 7, 2014 for the 3 companies.
comments and posts between them and their
operators than as it happened on Vodafone and
3.4 Preliminary Findings
The web scraping for textual data (posts, comments,
tweets etc.) returned valuable information about the
social media activity of the three telecom
companies. This included the number of posts made
in a day, number of likes/followers, and other
features specific to one social media such as notes,
talking about this on Facebook and following on
Twitter as shown in tables 2 and 3 respectively.
The number of tweets and posts on Twitter and
Table 2: Stats on Facebook Feb. 2 – Mar. 7, 2014.
about this
Tigo 313,287
6,011 13
MTN 211,649
3,179 19
Vodafone 274,863
1,600 143
Facebook respectively were generated within the
month dedicated to the research.
The study shows that the three companies seem to
have a strong presence on Facebook than on Twitter.
MTN for example posted 289 messages on their
Facebook wall compared to only 46 on Twitter in
the same period as shown in table 2 and 3. Vodafone
recorded 127 posts on Facebook with 89 on twitter
whiles Tigo managed 67 posts on Facebook and 74
on Twitter as indicated in tables 2 and 3. The trend
showed MTN as the most active telecom company
in Ghana on Facebook as far as posts emanating
from the company was concerned whiles Tigo
appeared slightly the most active on Twitter in the
period as shown in figures 2 and 3.
Table 3: Stats on Twitter from Feb 2 - Mar 2, 2014.
Tweets Followers Following
Tigo 74 9,834 6,084
MTN 46 30,170 3,147
Vodafone 89 21,911 1,743
Table 4: Classification of sentiments on Facebook.
Negative Positive Neutral Total
Tigo 161 381 462 1004
MTN 101 297 291 689
Vodafone 301 966 1721 2988
It can be seen in figures 2 and 3 above that the
peak time of posts and tweets on Facebook and
Twitter respectively did not occur at the same time
period. A content analysis of the entire posts and
tweets revealed that the reason for the discrepancies
was because the three telecom companies embarked
on different events, deals, special monthly offers
such as discounts and incentives at different days.
MTN for example had an SMS scam alert message
to their fans/customers on Facebook which
generated responses and re-posts within the period.
The disparity in the postings and tweets was again
evident in the times of the day that customers/fans
interacted with their telecom providers on social
media. In figures 4-6, an interesting pattern emerges
on the time of the day that customers/fans were most
active on the pages of these three companies. The
action intelligence to be extracted from this would
be the ideal time for the companies to target their
customers/ fans with new products, special offers
and important messages such as alert on scams as
was done by MTN.
This section briefly explains some of the key
findings from the social media sites of the three
telecom companies as far as business competitive
and sentiment analysis were concerned. In table 4, a
summary of the classification of all the textual
information extracted from the social media sites of
the three companies into negative, neutral and
positive comments is presented.
On Facebook, Vodafone had the largest number
of interactions than MTN and Tigo within the period
of the research. A total of 2,988 textual information
were extracted from Vodafone’s Facebook wall.
This number includes both posts originating from
the company and the responses/comments generated
as a result of the posts. The implication is that, with
127 posts on Vodafone’s Facebook’s wall as seen in
table 2, it elicited 2,861 responses from
fans/customers. Competitively, it means a far
positive customer-company interaction occurred for
Vodafone than MTN which posted 289 posts (see
table 2) but generated a paltry 654 user comments
and responses within the period. It is also worthy to
note that even with Tigo’s 67 posts on their wall
within the month of February to March, customer
generated comments/responses and posts (1004) was
more than that of MTN.
4.1 Sentiment Analysis on Facebook
The sentiment analysis architecture adopted
generated sentiment scores of -1 to +1 indicating
most negative to most positive comments to classify
the sentiments behind the textual data on the three
companies. As shown in table 4, Vodafone had 301
negative comments representing 10.07%, 966
positives (32.33%) and 1,721 (57.60%) neutral
comments out of the total of 2,988 texts generated.
The classification generated 161 negative comments
on Tigo representing (16.03%), 381 (37.95%)
positives and 462 (46.02%) neutral comments. MTN
on the other hand had 101 negative comments
representing 14.66%, 297 positives (43.10%) and
291 (42.20%) neutral comments.
Whereas a cursory analysis of table 4 does not
reveal any disturbing trend for any of the three
companies as far as the numbers recorded for the
total negative comments is concerned, content
analysis of the actual individual comments and their
relative sentiment scores shown in tables 5-7 is
telling on the performance of the companies and
their brands in Ghana.
The individual sentiments from the three companies
in tables 5-7 reveal gravely dissatisfied customers
with strong statements of dissatisfaction. For
instance comments like “
Airtime s**kers. Can't even
browse for 30mins with 2gh airtime” for MTN, “tigo is
the most useless network in Ghana. bought the ghc39.99
bundle its too slow. I can't even open common google. you
have just lost a customer and I will never” for Tigo and “a
disappointing network, in Takoradi but yet barely
able to
connect. would not recommend” for Vodafone
signify discontent. To gain competitive advantage,
the companies must respond positively and quickly
to these comments to have an edge over their
. In-depth content analyses of the
positive comments on Facebook reveal interesting
responses by the three companies to specific
customer queries or enquiries. Tables 8-10 are some
specific responses to enquiries by their customers.
Customers feel more relaxed and cared for if
they are being identified by name on specific and
direct responses to their concerns (Mittal and Lassar,
1996; Peppers and Rogers, 1995). From our content
analysis and as can be seen in table 8, MTN
Table 5: Some selected MTN negative comments.
Comment Score
Airtime s**kers. Can't even browse for 30mins with 2gh airtime. -1
Its annoying if as at now you call yourselves best network in Ghana and still we can't get good
network to make calls for browsing, don't wanna talk, I know what to do. :(
MTN not good at all -1
MTN, Like seriously, u r the most useless and disgraceful network I've ever experienced in my life... I
regret the day I bought ur chip. I'm porting straight to vodafone and I
so..when..would..u..stop..stealing..my..airtime.? -1
Table 6: Some selected Tigo negative comments.
tigo is the most useless network in Ghana. bought the ghc39.99 bundle its too slow. I can't even open
common google. you have just lost a customer and I will never
In fact ur internet network is the poorest in Ghana, what network be this?. What it pains me koraa
is....I persuaded my galfriend to port her favorite MTN to Tigo, further on to
masa if u think we re goin to encounter netwrk problems den dont let us activate internet bundles and
later on we cnt use dem.#Poorservice "#Frown uv got tigo"
Tigo Ghana your internet service is really killing me guys, I have a deadline today and I can't even
get anything done. Your Internet is soo slow I can't even open a single page.
hw3 all u pple do is brag. ur service is v.slow. on and off lyk ecg. 0278686875. i will b leaving soon if
u guys dnt gt serious
Table 7: Some selected Vodafone negative comments.
Comment Score
too bad network.. my interent isnt workn again! bomb! im even usin difrnt ntwrk -1
stupid network Vodafone for how long will it take your stupid company to fixed my problem my
broadband problem . what the f**k is wrong you with you guys you sick, see
And ur internet service has been down for 3 months now.. unbelievable -1
a disappointing network, in Takoradi but yet barely able to connect. would not recommend -1
And as a company what do you do?when there is a problem.6days of no browsing and nothing has
been.Tweaaaaaaaaaaaaaa what an incompetent data provider
Table 8: Some selected MTN positive comments.
Comment Score
Y'ello Emmanuel, As per our discussion,kindly visit any MTN office with an ID and relevant
documents to prove ownership of the number. Thank you.
Y'ello Vida, Sorry for your experience. Kindly provide your number to enable us investigate your
complaint. Thank you
Y'ello Bridget, Thank you for your suggestion,Kindly be informed that it is well noted. Thank you 1
Table 9: Some selected Tigo positive comments.
We sincerely apologize for any inconvenience caused.
Kindly confirm your number and current location for assistance.
Kindly elaborate on your issue to enable us assist you.
Table 10: Some selected Vodafone positive comments.
Comment Score
Dial 255 for expert medical advice between 4:00pm and 10:00pm daily. 0.38518
In the spirit of love, telecom operator, Vodafone Ghana, has settled the medical bills of over 180
patients who were struggling to raise funds to pay their medical bills through
Music doesn't lie. If there is something to be changed in this world, then it can only happen through
addresses specific customer concerns and directly
mentions the name of each customer in the response.
It was also realized in one response that MTN takes
on suggestions of customers and acknowledges the
customer for the input. Tigo also addresses
customers per their concerns. Scanning through the
content, there was some deficiency in Vodafone’s
responses to customer specific queries/enquiries.
The company provided Omnibus statements that
were meant to address general customer issues. To
gain competitive advantage, companies must be seen
to be more responsive to customers concerns.
Companies who personalize customers concerns turn
to retain such customers and increase its competitive
urge over rivals. It can be inferred that MTN has an
urge in terms of responses to customer concerns per
extracted comments on Facebook under the period
of study.
4.2 Sentiment Analysis on Twitter
Adopting the same sentiment analysis score of -1 to
+1 representing most negative to most positive
comments respectively, content analysis of the
actual individual comments and their relative
sentiment score on twitter for the three companies
for randomly selected customers are shown in tables
11-13. Negative comments on twitter for the three
companies are similar to that of Facebook. The
choice of words indicates that these customers are
disgruntled and desire specific response to their
concerns. Content analysis of the positive responses
on twitter was similar to that of Facebook. However,
there were more omnibus statements from the three
companies on twitter as compared to Facebook.
Since the word list in SentiWordNet is not
comprehensive and does not contain vernacular
words some sentiments would not be classified in
the analysis. This affects the accuracy of the
classification. We will therefore develop a wordlist
in future to embody local Ghanaian vernacular
words for a comprehensive analysis.
The explosion of data on social media leaves no
business enterprise with a focus on customer
satisfaction and expansion to be complacent. The
emergence of social media presents businesses with
a rare chance of analysing publicly available data of
their customers/fans and even more importantly that
of their competitors for business advantages. Such
data driven analysis could help businesses to identify
their weaknesses and exploit the weaknesses of their
competitors as far as customer retention and brand
management is concerned It would also help to.
Table 11: Some selected MTN negative comments.
Comment Score
Some stupid networks we have in our country @MTNGhana fire burn u all -1
u dont care about the plight of Ghanians at all...and also why does it keep sooo long for attendants to
pic calls of customers?
u people y?? U pple dey take ma credit small small. I haven't subscribed for any service aside internet
bundles wai. Will port oo
Table 12: Some selected Tigo negative comments.
Everyday for the last 2months “@Wiredu_: Thou sucketh today @TigoGhana”” << it's becoming
too much. Fix it tiGo
massa, ur internet service dey bore.wk on it den shon dey talk trash.
bt i tot u said 24hrs unlimited. rather 14hrs, such a shame
Table 13: Some selected Vodafone negative comments.
Comment Score
too bad network.. my interent isnt workn again! bomb! im even usin difrnt ntwrk -1
stupid network Vodafone for how long will it take your stupid company to fixed my problem my
broadband problem . what the fuck is wrong you with you guys you sick, see
And ur internet service has been down for 3 months now.. unbelievable -0.7765
consolidate strengths and identify new opportunities
and threats in the competitive market. In Ghana and
Africa as a whole, social media competitive analysis
may be a new thing but as our research shows, the
numbers of social media users in Africa keep
growing at exponential rates (Deloitte and Touche,
2012; United Nations (UN), 2010).
This study demonstrated how businesses could
take advantage of social media data to improve upon
customer relationship and maintain a competitive
urge over rivals. Using a case study of the top three
Ghanaian Telecom companies, we demonstrated
with sentiment analysis, the general feeling of
telecom users in Ghana. The use of sentiment
analysis is one of the potent ways in attempting to
understand customer behaviour.
The results show how active the three telecom
companies are on social media and how they engage
with their customers/fans on daily basis. Content
analysis of the textual information we extracted
reveal a number insights about how customers
perceive the services of the three companies. Some
of the key patterns and trends we identified are the
(1) Response to User Comments
The study revealed that to stay in touch with their
customers, the three Telecom companies have
specific staff assigned to addressing daily customer
complaints. They use the social media platform to
launch new products and listen to their feedback on
services and products. In responding to customer
concerns, two of the companies were seen to be
giving omnibus answers to complaints. MTN was
innovative in the way they addressed customers by
name and proffered real-time solutions to concerns.
(2) Unusual Posts and Tweets
It was also realized in the content analysis that not
every posts or tweets by the company was related to
their service, products or meant to address a
customer complaint. Some of the companies, TiGo
in particular made several casual postings about
topical issues on their pages to engage their fans in
the day. Most of the topics centered on relationships,
security tips and upcoming entertainment events in
the cities and towns in the country. Though most of
these casual posts yielded lots of responses, we
could not determine their impact on brand awareness
and therefore the competitive urge it gives to such
companies over their competitors.
(3) Service Quality
Though some of the posts and comments from
customers were not business related, majority (60%)
of them were about criticizing or hailing one
company or the other about their service or product
as shown in Tables 5-13. With mobile number
portability service in place in Ghana where users can
port their telephone numbers from one company to
the other for free, competition has become keen
among the various operators about maintaining their
core customer base and capitalizing on the number
portability service to snatch users from their
competitors. In spite of this, some service
complaints were not satisfactorily addressed
prompting others to threaten to port their numbers to
other companies as shown for example by one
comment in Table 11 row 3.
(4) Freebies
We also realized that almost all the Telecom
companies engage in giving out free phone credits to
their customers on social media. Some of these
freebies came in the form of answering simple
questions about their products and services. Some
also came in the form of purchasing tickets to major
entertainments programs for customers who got
some questions rightly answered.
In all MTN seemed to have a slight competitive
urge over their rivals in terms of how the company
engages with customers on social media. A
company’s relative responsiveness to customer
concerns such as MTN makes consumers advocate
of their brand and therefore increase brand referrals
culminating in increased profits and market
This work was supported by Internal Grant Agency of
Tomas Bata University IGA/FAI/2014/037,
IGA/FAI/2014/007 and by the European Regional
Development Fund under the project CEBIA-Tech No.
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