Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

Systematic Literature Review on Customer Switching Behaviour from Marketing and Data Science Perspectives

This paper systematically examines the literature review in the field of customer switching behavior. Based on the literature review, it can be concluded that customer switching behavior is a topic that has been widely researched, with a focus on various industries, particularly banking and telecommunications. Research trends in this area have shown a positive direction in recent years, and the amount of research being done in marketing and data science is relatively balanced. In marketing, correlational studies are predominant, with a focus on identifying relationships between customer satisfaction, price-related variables, attractiveness of alternatives, service failure, quality, and switching costs to switching behavior. The PPM model is also gaining popularity as an important development for switching behavior because it considers both push and pull factors. Data science research has shown promising results in predicting customer switching behavior, with each research paper achieving good predictive accuracy. However, research gaps spanning the fields of marketing and data science need to be addressed to provide a comprehensive understanding of the drivers of customer switching behavior. Overall, the literature review shows that customer switching behavior is an important concern for businesses, and further research in this area is essential to gain a better understanding of customer behavior and develop effective strategies to retain customers

Adaptive algorithms for real-world transactional data mining.

The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order to be efficient and accurate. Using real-world transactional data, this thesis presents the findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling

The use of differentiating communication tools to attract and retain different generational cohorts: case of a commercial bank in South Africa.

Published ThesisIt is inconceivable for any organisation to think that communicating with all of its clients using the same communications tools would make these clients more loyal. The problem of using the right tools of communication becomes more complex when the organisation deals with different generations. Previous scholars have emphasised the importance of Customer Relationship Management (CRM), both as a business philosophy and as part of an organisation’s IT systems to attract and retain clients. The IT systems are put in place so that clients can easily communicate with the organisation and vice versa. The CRM business philosophy is meant to change the method of dealing with clients as a top-down approach. This means top management will create the type of environment in the organisation that positions the needs of customers first. The primary objective of this study was to investigate the use of different communication tools by a commercial bank to attract and retain clients from different generations. The researcher identified four different branches from the same commercial bank in Bloemfontein to conduct the study. The location of these branches in and around Malls was important because it allowed the researcher to get a wide variety of different clients of the bank. A total of 50 clients of the bank per branch were asked to complete a questionnaire. The statistical calculations that were used were frequency tables, cross tables, McNemar test and the Chi-Square test. The research findings revealed that respondents from both generations made use of a variety of traditional and modern communication tools that were given in the questionnaire. It also indicates that this commercial bank at times utilises the wrong communication tools to communicate with these two cohorts, whether it is traditional or modern communication tools. The usage of each specific traditional and modern communication tool is also important. The results indicate that the usage of the specific communication tools for both traditional and modern communication tools vary during the course of the day. This is true for both generational cohort respondents. Based on the findings of this empirical study, the bank should focus more on utilising the specific communication tools that these two generations prefer, whether it is traditional or modern communication tools. The bank should also pay specific attention to the times of the day that these aforementioned communication tools are being used most by the respondents to ensure maximum marketing exposure. This study illustrates that there is no universal rule that dictates that a specific generation will only use a specific communication tool - in this case the Baby Boomer and Generation Y generation. The bank should investigate which modern or traditional communication tools are preferred by their clients the most and then continue with productive two way communication using those tools. This can facilitate the process of making clients more loyal and the process of attracting new clients simpler

The Repellent Effect of Waste: A study of unusual factors affecting Willingness To Purchase

" The Repellent Effect of Waste" Abstract This research poses a theory of the Repelling Effect of Waste in services and perishable goods, explores its principles, and proposes the first moderating factor in the literature that we are aware of. The main ideas of our research and the proposed theory focus on the following three areas of contribution to marketing science: [1] We offer insights about waste aversion in services and actual WTP. We carry out experiments to support our theoretical propositions; [2] We propose insights about the decision-making processes that people go through in terms of complexity and choice (relating to 'waste' and in the context of offers and price design), and how cost disclosure interacts with this; [3] We discuss the importance of key variables such as income (or relative wealth), on those processes, and support our propositions with experimental insights. Through a series of six experiments, this research brings evidence of waste aversion in the context of services, which is the main contribution of the research. This research also looks at Willingness to Purchase (WTP) and proposes that Qualitative Cost cues could be an effective and ethical way to increase consumer's willingness to pay a price premium. Our experimental results also show that there is an ethical, cheap and effective way to communicate a price premium to consumers and convince them to buy a premium product: qualitative cost cues. WTP can be increased by up to 36% in potential consumers. Our experiments further show that less is not always more. In many industries, marketers offer features that build additional value of the product or service being offered. Sweeteners, bonus packs, 2-in-1 deals and similar marketing techniques have become commonplace. However, our experiment shows there could be such a thing as too much value, bordering on waste, in an offering, which eventually could put customers off, rather than entice them into buying the product. Keywords: pricing, bundles, pricing of services, service marketing, waste, sustainability, efficiency

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students

The influence of incentives and survey design on mail survey response rates for mature consumers

The mail survey is still the preferred research tool for the mature consumer population and questions remain about ways of boosting survey response rates. The influence of two incentives were explored, a foil-wrapped tea bag and a $1 donation for each returned questionnaire in the study design. A significant higher response rate was only achieved for the first incentive. The effectiveness of a range of incentives and survey design features were investigated. Respondents indicated that their preferred incentive was a$500 donation to a charity. With the ongoing use of mail surveys almost mandatory for populations like this one, this study shows that incentives and design features such as CEO endorsement are important elements in improving response rates