A Proposed Analytical Customer Satisfaction Prediction Model for Mobile Internet Networks

Subjective method (such as survey, interview, etc.) has been the most common and reliable method used in analyzing customer satisfaction. However, the subjective method is expensive, time consuming, lacks repeatability in real-time and may not capture the technical aspect of the telecoms network service performance in telecommunication industry. As a result, perceived quality of experience (QoE) has been traditionally used to evaluate the satisfaction of telecommunication services from both Internet service providers and customer’s perspective. However, the result of perceived QoE in relation to mean opinion score found not suitable enough to quantify customer satisfaction, and it eliminates the diversity of customer assessment while quantifying satisfaction. Therefore, this paper proposed an analytical customer satisfaction prediction model based on perceived QoE, perceived QoE influence factors, perceived QoE measurements and perceived QoE estimations to overcome the limitations of the subjective measurement. The paper presents how the mean opinion score can be used to quantify customer satisfaction by ensuring the diversity of customer’s assessment is not eliminated

Implementation of Quality of Experience Prediction Framework through Mobile Network Data

Generally, a reliable method of analyzing the quality of experience is through the subjective method, which is time consuming, lacks usability, lacks repeatability in real-time and near real-time. Another method is the objective measurement that aims at predicting the subjective measurement based on the estimated mean opinion score. Therefore, this study adopted the objective measurement by implementing a quality of experience framework, which employed predictive analytics techniques to analyze the mobile internet user experience dataset gathered through the mobile network. The predictive analytics employed the use of multiple regression, neural network, decision trees, random forest, and decision forest to predict the mobile internet perceived quality of experience. Result from the study shows that decision forests performs better than other algorithms used for the predictive analytics. In addition, the result indicates that the predictive analytics can be used to enhance the allocation of network resources based on location and time constituted in the dataset

A proposed framework for mobile Internet QoS customer satisfaction using big data analytics techniques

In the past few years, the Nigeria telecommunication industry has experienced tremendous growth and changes to the extent that customers find it much easier to access the internet through their mobile phones.However, the growth in mobile telecoms subscribers comes with challenges of quality of service, which lead to fluctuations in customer satisfaction.Therefore, the present study proposed a customer satisfaction prediction model through the Key performance indicators obtained from the objective measurement of the network traffic using extended and exhaustive study of the literature.The proposed framework would guide mobile network operators on strategies to embark on in order to retain their customers within the network

IDENTIFYING A CUSTOMER CENTERED APPROACH FOR URBAN PLANNING: DEFINING A FRAMEWORK AND EVALUATING POTENTIAL IN A LIVABILITY CONTEXT

In transportation planning, public engagement is an essential requirement forinformed decision-making. This is especially true for assessing abstract concepts such aslivability, where it is challenging to define objective measures and to obtain input that canbe used to gauge performance of communities. This dissertation focuses on advancing adata-driven decision-making approach for the transportation planning domain in thecontext of livability. First, a conceptual model for a customer-centric framework fortransportation planning is designed integrating insight from multiple disciplines (chapter1), then a data-mining approach to extracting features important for defining customersatisfaction in a livability context is described (chapter 2), and finally an appraisal of thepotential of social media review mining for enhancing understanding of livability measuresand increasing engagement in the planning process is undertaken (chapter 3). The resultsof this work also include a sentiment analysis and visualization package for interpreting anautomated user-defined translation of qualitative measures of livability. The packageevaluates users satisfaction of neighborhoods through social media and enhances thetraditional approaches to defining livability planning measures. This approach has thepotential to capitalize on residents interests in social media outlets and to increase publicengagement in the planning process by encouraging users to participate in onlineneighborhood satisfaction reporting. The results inform future work for deploying acomprehensive approach to planning that draws the marketing structure of transportationnetwork products with residential nodes as the center of the structure

Aplicación de algoritmos automáticos de aprendizaje supervisado para predecir el abandono de clientes en telefonía móvil

Un aspecto clave para el éxito de una compañía telefónica es mantener la satisfacción de los usuarios que contratan sus servicios, lo que se conoce como fidelización de los clientes. Para ello, es necesario elaborar e implementar estrategias que respondan a las necesidades y preferencias de los clientes, teniendo en cuenta el mercado y la competencia. Para optimizar esas estrategias, es conveniente identificar a qué clientes enfocarse. La empresa objeto de estudio es una compañía de telecomunicaciones que opera en todo el territorio estadounidense, compuesto por 51 estados. El contexto legal de este país se caracteriza por la apertura al mercado y el fomento de la competencia, garantizando al mismo tiempo el acceso universal al servicio. En este escenario, la rivalidad entre las empresas se ha intensificado en los últimos años, lo que ha impulsado la innovación en los departamentos de fidelización, que buscan estrategias para diferenciarse y mantenerse en el mercado. Este trabajo busca disminuir el abandono de clientes de la empresa. Para lograrlo, se utilizan herramientas de aprendizaje automático supervisado que permiten crear un modelo predictivo para detectar a los clientes con mayor riesgo de cancelar su contrato con la empresa, y analizar las variables más relevantes en la decisión de estos de abandonar la compañía. Se utiliza una base de datos que contiene información de 3,333 clientes de una empresa telefónica. El objetivo de este trabajo es identificar a los clientes que tienen más riesgo de abandonar la empresa. Para lograrlo, se han utilizado dos métodos de aprendizaje supervisado de manera consecutiva. El primero permite estimar la probabilidad de que un cliente se dé de baja y el segundo genera una serie de reglas que explican esa probabilidad en función de las características de los clientes. Para predecir si un cliente cancelará o no los servicios que contrató con la compañía, se usa la regresión logística. Esta técnica permite estimar la relación entre una variable binaria (cancelación o no) y varias variables explicativas (categóricas y continuas) que influyen en ella. Se evaluaron diferentes criterios para seleccionar el mejor modelo, como el criterio de información bayesiano, el criterio de Akaike, el pseudo R^2 de McFadenn, la importancia de las variables y el factor de inflación de la varianza. Luego, se aplicó el árbol de decisión, otra técnica que clasifica a los clientes en dos grupos según la variable binaria. Esta técnica tiene la ventaja de ser más fácil de interpretar y comunicar que la regresión logística, además de obtener mejores métricas de rendimiento. Para comparar las técnicas de árbol de decisión y regresión logística en la predicción de la cancelación de servicios de telefonía móvil, se construyen y se prueban varios modelos con diferentes parámetros. Se utiliza la matriz de confusión y otras métricas, como la precisión, la sensibilidad y la especificidad, para evaluar el rendimiento de los modelos. Los resultados muestran que el árbol de decisión alcanza una precisión del 95.65% en la predicción de la cancelación, mientras que la regresión logística logra una precisión del 85.30%. Finalmente con el modelo predictivo obtenido, se diseñan políticas de retención de clientes basadas en las variables más influyentes.A key aspect for the success of a telephone company is to maintain the satisfaction of the users who contract its services, known as customer loyalty. To do this, it is necessary to develop and implement strategies that respond to customer needs and preferences, taking into account the market and the competition. To optimise these strategies, it is advisable to identify which customers to focus on. The company under study is a telecommunications company that operates throughout the 51 states of the United States. The legal context of this country is characterised by openness to the market and the promotion of competition, while guaranteeing universal access to service. In this scenario, rivalry between companies has intensified in recent years, driving innovation in loyalty departments, which are looking for strategies to differentiate themselves and remain in the market. This work aims to reduce customer churn. To achieve this, supervised Machine Learning tools are used to create a predictive model to detect the customers most at risk of cancelling their contract with the company, and to analyse the most relevant variables in their decision to leave the company. A database containing information on 3,333 customers of a telephone company is used. The objective of this work is to identify the customers who are most at risk of leaving the company. To achieve this, two supervised learning methods have been used consecutively. The first method estimates the probability that a customer will churn and the second method generates a set of rules that explain this probability based on customer characteristics. Logistic regression was used to predict whether or not a customer will cancel services contracted with the company. This technique allows estimating the relationship between a binary variable (cancellation or not) and several explanatory variables (categorical and continuous) that influence it. Different criteria were evaluated to select the best model, such as the Bayesian information criterion, the Akaike criterion, McFadenn's pseudo R^2, the importance of the variables and the variance inflation factor. Then, the decision tree, another technique that classifies customers into two groups according to the binary variable, was applied. This technique has the advantage of being easier to interpret and communicate than logistic regression, as well as obtaining better performance metrics. To compare decision tree and logistic regression techniques in predicting mobile churn, several models with different parameters are built and tested. The confusion matrix and other metrics, such as accuracy, sensitivity and specificity, are used to evaluate the performance of the models. The results show that the decision tree achieves an accuracy of 95.65% in predicting cancellation, while the logistic regression achieves an accuracy of 85.30%. Finally, with the predictive model obtained, customer retention policies are designed based on the most influential variables.Universidad de Sevilla. Máster en Organización Industrial y Gestión de Empresa

Aplicación de Machine Learning en las empresas del sector telecomunicaciones del Perú

Esta investigación titulada Aplicación de Machine Learning en las empresas del sector Telecomunicaciones del Perú tuvo un enfoque cualitativo con el objetivo general de describir la aplicación de machine learning en las empresas del sector telecomunicaciones del Perú, se emplearon técnicas de entrevista a profundidad, observación participante y análisis documental con sus respectivos instrumentos guía de preguntas semi estructuradas, ficha de observación y ficha de análisis documental, las entrevistas y observación se realizaron mediante las herramientas meet de Google y teams de Microsoft, para el análisis documental se utilizó documentos públicos, el escenario de estudio fueron las áreas de análisis de datos de las empresas Movistar, Claro y Entel y participaron expertos en análisis de datos, el tipo de investigación fue tecnológica y de diseño investigación acción. Se concluyó que la aplicación de machine learning en estas empresas es fundamental para aumentar la eficiencia de sus procesos y mejorar la satisfacción de sus clientes reduciendo las tasas de abandono, por lo cual están empezando a incorporarlo en sus estrategias de transformación digital, para su aplicación es importante comprender la conceptualización de esta tecnología, los beneficios actuales para las empresas del sector, las barreras que se presentan en la industria, los casos que influyen en su adopción y las tendencias tecnológicas que potenciarán los beneficios y masificarán su uso. Se hace necesario la incorporación de herramientas que faciliten su aplicación, el uso de algoritmos que aumenten la precisión de los resultados, desarrollar soluciones de lenguaje natural para una atención personalizada en cualquier momento, difundir los beneficios a nivel de toda la compañía, extender el ámbito de influencia más allá de la propia compañía y trabajar en políticas que garanticen la ética en el uso de datos y la responsabilidad en las decisiones producto de su uso