Data Mining Techniques with Electronic Customer Relationship Management for Telecommunication Company

Organizations must improve decisional quality, and the continuous usage of data mining techniques is a crucial issue for management. This issue mostly involves an individual's motivation to engage in the behavior. This could perhaps be characterized in terms of the working regimen. technology utilization and employee activity are the two main difficulties that this dilemma revolves around. This study aims to address the aspect associated with data mining and E-CRM in the telecom industry. The methods that are used in the current study,  analysis studies of the data mining techniques are applied to E-CRM that has been identified. Moreover, PHP with the update of the DeLone and McLean methods has been used in the current study. The results show the significance in affecting the continuance used intention of data mining techniques. User satisfaction, technology, and data mining are critical predictors of employment intentions

Tweet-based Target Market Classification Using Ensemble Method

Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting). To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree) algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras) and three categories of sentiments (positive, negative and neutral) were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%). On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter

Graduates employment classification using data mining approach

Data Mining is a platform to extract hidden knowledge in a collection of data.This study investigates the suitable classification model to classify graduates employment for one of the MARA Professional College (KPM) in Malaysia.The aim is to classify the graduates into either as employed, unemployed or further study.Five data mining algorithms offered in WEKA were used; Naïve Bayes, Logistic regression, Multilayer perceptron, k-nearest neighbor and Decision tree J48.Based on the obtained result, it is learned that the Logistic regression produces the highest classification accuracy which is at 92.5%. Such result was obtained while using 80% data for training and 20% for testing.The produced classification model will benefit the management of the college as it provides insight to the quality of graduates that they produce and how their curriculum can be improved to cater the needs from the industry

On the innovation mechanisms of fintech start-ups: insights from Swift's innotribe competition

The emergence of nascent forms of financial technology around the globe is driven by efforts to deconstruct and reimagine business models historically embedded within financial services. Entrepreneurial endeavors to this end are diverse. Indeed, the propensity towards complexity across the fintech landscape is considerable. Bridging as it does a diverse range of financial ser-vices, markets, innovations, industry participants, infrastructures and technologies. This study aims to improve the comprehension of the global fintech landscape. It is based on the analysis of start-ups who participated in SWIFT’s Innotribe competition over a three-year period. We used cluster analysis to group 402 fintech start-up firms, and then selected representative cases to create a foundational understanding of the structure of the fintech landscape. We found that six clusters capture the variety of firms and their activities. The main findings of this work are: (1) the development of fintech clusters to classify core services, business infrastructures and underlying component technologies, which characterize the fintech landscape; (2) an analysis of how fintechs synthesize different technologies to restructure and coordinate flows of financial information through competitive and cooperative mechanisms of disintermediation, extension of access, financialization, hybridization and personalization; (3) an analysis of related strate-gies for value creation connected with the competitive and cooperative mechanisms that were identified. Collectively, our results offer new insights into the diversity and range of emergent innovations and technologies which are transforming the financial services industry worldwide

Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models

Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use of ensemble learning models to analyze and forecast customer churn in the telecommunications business. Accurate churn forecasting is essential for successful client retention initiatives to combat regular customer churn. We used innovative and improved machine learning methods, including Decision Trees, Boosted Trees, and Random Forests, to enhance model interpretability and prediction accuracy. The models were trained and evaluated systematically by using a large dataset. The Random Forest model performed best, with 91.66% predictive accuracy, 82.2% precision, and 81.8% recall. Our results highlight how well the model can identify possible churners with the help of explainable AI (XAI) techniques, allowing for focused and timely intervention strategies. To improve the transparency of the decisions made by the classifier, this study also employs explainable artificial intelligence methods such as LIME and SHAP to illustrate the results of the customer churn prediction model. Our results demonstrate how crucial it is for customer relationship managers to implement strong analytical tools to reduce attrition and promote long-term economic viability in fiercely competitive marketplaces. This study indicates that ensemble learning models have strategic implications for improving consumer loyalty and organizational profitability in addition to confirming their performance

Antecedents of business intelligence system use

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees. This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwait’s telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis. The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI user’s self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture. The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employees’ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUT’s facilitating conditions and the limited variance of respondent demographics

Using the dynamic capabilities perspective to analyse CRM adoption: a multiple case study in portuguese organisations

Doutoramento em GestãoCustomer Relationship Management (CRM) adoption is both a relevant research topic in academia and a challenge for practitioners. We understand CRM as a complex concept that includes technology, strategy and philosophy. In this research, we propose an analysis of CRM organisational competences and capabilities. The main goals are: to observe organisational competences and capabilities in order to find ways in which companies obtain success with their CRM initiatives; and to apply a dynamic capabilities perspective as the main theoretical point to analyse how companies can improve their competences related to customer relationship management. In order to achieve the purpose of this study a qualitative, interpretative, case-based research strategy was implemented. We first conducted an exploratory case study in a Brazilian Telecommunications company in order to define the focus of the research and research questions. Afterwards, we conducted a main case study in a Portuguese Telecommunications company for one year, and finally, we conducted four more case studies in Portuguese organisations to develop the research findings. These multiple case studies were based on semi-structured interviews and document analysis. We used qualitative techniques to analyse the collected data and ground our interpretation in two theoretical approaches: value focus thinking and dynamic capabilities. We propose a theoretical framework related to CRM dynamic capability that is corroborated with empirical evidence. We believe that because organisations which adopt a CRM strategy are in a competitive environment, a dynamic model needs to be used to analyse and explain how they can improve their CRM strategy in order to achieve success. As a second contribution we identify a set of competences that need to be developed in order to manage customer relationships effectively.A adopção de Customer Relationship Management (CRM) é um tema considerado relevante para as investigações académicas e um desafio para os praticantes. CRM neste trabalho é entendido como um conceito complexo que envolve tecnologia, estratégia e filosofia. Esta investigação propõe uma análise sobre as competências e as capacidades organizacionais relacionadas ao CRM. As principais motivações deste estudo referem-se às problemáticas observadas nas adopções de CRM, sendo que as lentes da teoria das Capacidades Dinâmicas mostram-se relevante na análise das capacidades e competências organizacionais necessárias ao sucesso da iniciativa de CRM. A fim de alcançar o propósito deste estudo, foi realizada uma investigação qualitativa, interpretativa e baseada em estudos de caso. Primeiramente foi conduzido um estudo de caso exploratório numa empresa Brasileira de telecomunicações com o intuito de melhor definir o foco da investigação e das questões de investigação. Após foi conduzido o estudo de caso principal em uma empresa de telecomunicações Portuguesa ao longo de um ano. Finalmente foram conduzidos outros quatro estudos de caso em organizações Portuguesas com o intuito de aprofundar a discussão dos resultados da investigação. Foram realizadas entrevistas semi-estruturadas e análise de dados secundários. Para a análise dos dados foram utilizadas técnicas qualitativas e duas teorias ajudaram a suportar as interpretações realizadas: value focus thinking e dynamic capabilities. Como contribuições desta investigação tem-se a proposição de um framework teórico sobre a capacidade dinâmica CRM que foi corroborado com evidências empíricas. As organizações que adoptam CRM estão inseridas em ambientes de grande competitividade e um modelo dinâmico precisa ser utilizado para analisar e explicar como elas aprimoram suas estratégias de CRM para ter sucesso. Como segunda contribuição foi identificado um conjunto de competências organizacionais que são necessárias para a gestão do relacionamento com o cliente

A pricing optimization modelling for assisted decision making in telecommunication product-service bundling

Product service bundle (PSB) is a marketing strategy that offers attractive product-service packages with competitive pricing to ensure sustained profitability. However, designing suitable pricing for PSB is a non-trivial task that involves complex decision-making. This paper explores the significance of pricing optimization in the telecommunication industry, focusing on product-service bundling (PSB). It delves into the challenges associated with pricing PSB and highlights the transformative impact of big data analytics on decision-making for PSB strategies. The study presents a data-driven pricing optimization model tailored for designing appropriate pricing structures for product-service bundles within the telecommunication services domain. This model integrates customer preference knowledge and involves intricate decision-making processes. To demonstrate the feasibility of the proposed approach, the paper conducts a case study encompassing two design scenarios, wherein the results reveal that the model offers competitive pricing compared to existing telecommunication service providers, facilitating PSB design and decision-making. The findings from the case study indicate that the data-driven pricing optimization model can significantly aid PSB design and decision-making, leading to competitive pricing strategies that open avenues for new market exploration and ensure business sustainability. By considering both product and service features concurrently, the proposed model provides a pricing reference for optimal decision-making. The case study validates the feasibility and effectiveness of the approach within the telecommunication industry and highlights its potential for broader applications. The model's capability to generate competitive pricing strategies offers opportunities for new market exploration, ensuring business growth and adaptability

Customer Relationship Management : Concept, Strategy, and Tools -3/E

Customer relationship management (CRM) as a strategy and as a technology has gone through an amazing evolutionary journey. After the initial technological approaches, this process has matured considerably – both from a conceptual and from an applications point of view. Of course this evolution continues, especially in the light of the digital transformation. Today, CRM refers to a strategy, a set of tactics, and a technology that has become indispensable in the modern economy. Based on both authors’ rich academic and managerial experience, this book gives a unified treatment of the strategic and tactical aspects of customer relationship management as we know it today. It stresses developing an understanding of economic customer value as the guiding concept for marketing decisions. The goal of this book is to be a comprehensive and up-to-date learning companion for advanced undergraduate students, master students, and executives who want a detailed and conceptually sound insight into the field of CRM

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges