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

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

Improved Customer Churn and Retention Decision Management Using Operations Research Approach

The relevance of operations research cannot be overemphasized, as it provides the best possible results in any given circumstance, through analysis of operations and the use of scientific method thus, this paper explore the combination of two operations research models (analytic hierarchy process and Markov chain) for solving subscribers’ churn and retention problem peculiar to most service firms. A conceptual model for unraveling the problem customer churn and retention decision management was proposed and tested with data on third level analysis of AHP for determining appropriate strategies for customer churn and retention in the Nigeria telecommunication industries. A survey was conducted with 408 subscribers; the sample for the study was selected through multi-stage sampling. Two analytical tools were proposed for the analysis of data. These include: Expert Choice/Excel Solver (using Microsoft Excel) and Windows based Quantitative System for Business (WinQSB). This paper plays important role in understanding various strategies for effective churn and retention management and the ranking of churn and retention drivers in order of importance to stakeholders` decision-making. The study provided a framework for understanding the application of AHP and Markov chain for modeling, analysing and proffering solution to problem of churn and retention. The study recommends organizational strategies (corporate, business and functional) that reverse the churn alternatives with high priority and equally strengthen service delivery on high priority retention alternatives in order to ensure firms sustainable competitive advantage. An erratum to this article has been published as https://doi.org/10.5195/emaj.2017.131

From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

A Survey on Various Techniques in Internet of Things (IoT) Implementation: A Comparative Study

As per the current trends in computing research socialization and Personalization in Internet of Things (IOT) environment are quite trending and they are being widely used. The main aim of research work is to provide socialized and personalized services along with creating awareness of predicting the service. Here various kind of methods are discussed which can be used for predicting user intention in large variety of IOT based applications such as smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. By common consent it is found that the prediction is made usually for finding techniques that can be accessed by the mobile user, predicting the next page that is most likely to be used by web user, predicting favorite and most likely TV program that can be viewed by user, getting a list of browsing usage and need of user and also predicting user navigational patterns, predicting future climate conditions, predicting the health and welfare of user, predicting user intention so that implicit could be made and human-like interactions could be possible by accepting implicit commands, predicting the exact amount of traffic at a particular location, predicting curricular performance of student in schools & colleges, having prediction of frequency of natural calamities and their occurrences such as floods, earthquakes over a long period of time & also the required time in which precautionary measures could be adopted, predicting & detecting the frauds in which false user try to make transaction in the name of genuine user, predicting the steps and work done by the user to improve the business, predicting & detecting the intruder acting in the network, by the help of context history predicting the mood transition information of the user, etc. Here in this topic of discussion, different techniques such as Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms are used for prediction

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