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Title: From sentiment analysis to choreography of emotions : social media analysis for improved customer relationship management (CRM) in the Omani telecom sector
Author: Al-Ansari, Jihad Ahmed
ISNI:       0000 0004 7972 2830
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2019
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Across the globe telecom firms are facing fierce competition amidst rapid technological advancements; thus, in order to succeed in the telecom business and remain profitable, satisfying and retaining loyal customers is vital. The process of providing superior services to customers who are socially interconnected has changed the landscape of customer relationship management (CRM). Following a detailed review of CRM, this study has identified the constructs that are critical for customer satisfaction in the Omani telecom sector. The literature review established that the primary reasons for organizations to implement CRM are to improve customer satisfaction, retain their customer base, obtain strategic information and enhance the value of the customer. It was also established that by collecting pertinent information on customers, such as their interests, habits and interactions, this enables organizations to provide a superior service which is specifically tailored to meet the individual needs of each customer. The sentiment analysis (SA) is particularly useful for CRM data collection. It is an efficient tool that has evolved through machine learning algorithms to provide an efficient and robust mechanism to observe customer sentiment in real-time. This research has demonstrated that such data will help to enhance CRM in Omani telecom firms because positive, negative and neutral sentiments alone cannot be used by firms to make strategic marketing decisions; therefore, sentiment analysis has been extended to include emotional analysis. To my knowledge, this is the first time that a novel visualisation tool has been used to demonstrate the choreographic emotion of tweets in real-time. With the recent push towards Marketing 3.0 where customers are considered to be multidimensional entities, emotional scores, more than sentiment scores, can play an active role in firms' decision-making processes. Questionnaires were developed for customers and social media managers based on the constructs identified during the literature review. Qualitative data was also collected through semi-structured interviews conducted with social media managers. Sentiment and emotional analysis were then applied to social media data pertaining to 83,981 tweets collected over one year in the Omani telecom sector. The data collected from social media managers, customers and findings of literature review were triangulated in order to arrive at meaningful conclusions. Based on this, a series of recommendations designed to enhance CRM in Omani telecom firms have been provided. Whilst 80% of the managers interviewed confirmed that they offer interactive online customer support, customers' responses relating to their level of satisfaction with the support they received remained neutral. This shows that the effectiveness of customer support remains questionable. Female customers indicated that they were satisfied with the support they received; however, male customers were not. Similarly, younger respondents (less than 19 years of age and between 20 to 30 years of age) responded with lower satisfaction scores than the other age groups. This information has to be taken into consideration during resolution management; the implication being that telecom firms should not only implement customer-centric CRM but also enable segment-focused service delivery. Students and government employees also provided lower scores for their satisfaction regarding the level of customer support they received. I have identified that the emotional scores of live social media data can play a big role in CRM processes, and when these data were visualised as a choreography of emotions using Plutchik's Wheel of Emotion, it was observed that the customers who received better customer service may have experienced feelings such as 'surprise' and 'happiness'. Conversely, when customers were not satisfied, the emotions shown included 'disappointment', 'anger', 'irritation' and/or 'anxiety'. These findings have been mapped on the Wheel of Emotion. This study, therefore, led to developing a model that integrates the SA of social media data along with the CRM processes of telecom firms in real-time. This involves horizontal and vertical integration of the outcomes of emotional analyses to be shared across the organisation. I recommended the implementation of a 7-step approach to capitalise on the robustness of machine learning algorithms by choreographing emotions from live stream social media data. This can equip firms with better tools as they strive to prosper in a world of competitors and rapidly advancing technology.
Supervisor: McMenemy, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral