Enhancing the methods of customer behavior analysis to increase customer satisfaction. Case study: Syriatel Telecom Company

The telecommunication industry is in the strongest competition ever, as this sector gets disrupted by new arising competitors with high technical infrastructure as 5G networks. However, the current customer satisfaction measures are based on subjective questionnaires without utilizing the vast amount of objective network KPIs and telecom systems data into account. This work presents a model that tackles this lack of research and provides a high impact solution to survive in the tough competition of the telecom industry. The paper addresses two fundamental questions: 1) To what extent satisfied/dissatisfied customers can be classified based on telecom systems data that was produced during users’ interactions? 2) Can satisfaction indicators be derived from telecom systems data? this study discusses a machine learning problem, and compare 7 classifiers and analyze data for 10,000 real users from the Syrian telecom company Syriatel. 120 extracted features were drawn from the most significant available sources: billing, network, customer service system, and customer demography data. The best result for customer satisfaction classification was 87%, achieved with XGBOOST classifier. Furthermore, the paper identifies the most 9 potential indicators for satisfaction. Our goal was to classify customer satisfaction/dissatisfaction based on the objective data that is generated from every service interaction on the network or customer care centers

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested