Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy

The influence of customer experience on customer loyalty for the mobile telecommunication services

The term customer experience (CE) is used to describe an emerging trend where the sellers and buyers are connected through the past and current product and service experiences. Customer loyalty is an important tool for current and future businesses in telecommunication field. Customer loyalty can help to capture and develop future strategies. Moreover, the effect of customer satisfaction on customer loyalty is also very vital for the sustainability and stability of service provider survival in competitive markets. While customer loyalty and customer satisfaction have been examined widely in different research contexts, the issue of customer loyalty in the context of mobile telecommunication services currently represents a gap in the literature. Therefore, the objective of this study is to identify the influence of customer experience on customers’ loyalty in mobile telecommunication services. This research will gather survey data and applies structural equation modelling SEM to analyse the data. The findings will provide insights for mobile telecommunication service industries in developing strategies for improved implementation of mobile products and services as well as the design of marketing strategies along with improved business models

Design Product-Service Systems by Using a Hybrid Approach: The Fashion Renting Business Model

As is known, sustainability issues represent one of the main challenges companies have to face. Among all, the fashion industry is considered one of the most impactful, both in terms of resource utilization and pollution. Fashion renting is a recent business model for companies to reduce their environmental footprint, following a circular economy approach. The study aims to develop and discuss the proposed hybrid approach to effectively support fashion companies in designing new business models, taking into account both the customer and the company perspective. On the one hand, agent-based modeling (ABM) allow us to represent customers’ behaviour and interaction. On the other hand, discrete event simulation (DES) paradigm is used to model fashion renting processes. Because customers’ attitude to that service reflects its successful implementation, motivators and barriers have been investigated to be included in the model. The practical implication is defining a model to support fashion companies in designing rental business models before implementing them. From a theoretical point of view, it overcomes the literature gap about the definition of a unique model for fashion renting, including processes, customers and interactions between agents. Follow-up research will include the presentation of simulation results

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance

Designing Iranian Customer Expectations Model for Platform Growth and Development with Agent-Based Model Approach (Case Study: Instagram)

One of the most important factors in understanding customer behavior is identifying their expectations. Therefore, this study in order to design customer expectations model for platform with agent-based model approach. At first, for finding customers and servants expectations was used of semi-structure interview and then was done automatic clustering with using meta-heuristic algorithms in order to find different factors. Then, with the factor-based simulation approach, the model was designed in Any Logic software. After designing the model, using design method of Taguchi experiments (Qualitek-4 software), scenarios for the growth and development of the platform based on effective factors (liquidity the quality of communication and trust) designed on four levels and finally simulation was performed and scenarios were examined in the simulation environment. the research results showed that the appropriate level of platform growth and development indicators in the fourth level of liquidity, the fourth level of communication quality and the fourth level of trust. In addition, after implementing the optimal scenario in the simulation environment was determined that the percentage of value created on the Instagram platform due to the implementation of the desired scenario is equal to 0.934

A Data-Driven Approach for Modeling Agents

Agents are commonly created on a set of simple rules driven by theories, hypotheses, and assumptions. Such modeling premise has limited use of real-world data and is challenged when modeling real-world systems due to the lack of empirical grounding. Simultaneously, the last decade has witnessed the production and availability of large-scale data from various sensors that carry behavioral signals. These data sources have the potential to change the way we create agent-based models; from simple rules to driven by data. Despite this opportunity, the literature has neglected to offer a modeling approach to generate granular agent behaviors from data, creating a gap in the literature. This dissertation proposes a novel data-driven approach for modeling agents to bridge the research gap. The approach is composed of four detailed steps including data preparation, attribute model creation, behavior model creation, and integration. The connection between and within each step is established using data flow diagrams. The practicality of the approach is demonstrated with a human mobility model that uses millions of location footprints collected from social media. In this model, the generation of movement behavior is tested with five machine learning/statistical modeling techniques covering a large number of model/data configurations. Results show that Random Forest-based learning is the most effective for the mobility use case. Furthermore, agent attribute values are obtained/generated with machine learning and translational assignment techniques. The proposed approach is evaluated in two ways. First, the use case model is compared to another model which is developed using a state-of-the-art data-driven approach. The model’s prediction performance is comparable to the state-of-the-art model. The plausibility of behaviors and model structure in the use case model is found to be closer to real-world than the state-of-the-art model. This outcome indicates that the proposed approach produces realistic results. Second, a standard mobility dataset is used for driving the mobility model in place of social media data. Despite its small size, the data and model resembled the results gathered from the primary use case indicating the possibility of using different datasets with the proposed approach

INTEGRATING KANO MODEL WITH DATA MINING TECHNIQUES TO ENHANCE CUSTOMER SATISFACTION

The business world is becoming more competitive from time to time; therefore, businesses are forced to improve their strategies in every single aspect. So, determining the elements that contribute to the clients\u27 contentment is one of the critical needs of businesses to develop successful products in the market. The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model focuses on highlighting the most relevant attributes of a product or service along with customers’ estimation of how these attributes can be used to predict satisfaction with specific services or products. This research aims at developing a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to improve the quality of United Arab Emirates University academic support and development services provided to their students by solving the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from United Arab Emirates University. The experiment suggests that Extreme Gradient Boosting Regression can produce the best results for this kind of problem. Based on the integration of the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that either Chi-Square or Analysis of Variance (ANOVA) features selection model’s integration with the Kano model giving higher values of Pearson correlation coefficient and R2. Moreover, the prediction was made using union features between the Kano model\u27s most important features and the most frequent features among 8 clusters. It shows high-performance results

Is competition sufficient to drive observed retail location and revenue patterns? An agent-based case study.

Agent-based models (ABMs) have been widely used to represent and investigate complex systems and are a contemporary modelling approach used in the study of land-use and land-cover change. While many ABMs have been constructed to address research questions associated with residential land development and human choices, agricultural land transition and farmer decision-making, and transportation networks and planning, less attention has been given to improving our understanding about the drivers and agent behaviours associated with commercial and retail competition, which subsequently affects land-use change. Among existing ABMs that represent the retail system, the focus has been on understanding consumer behaviours, but the inclusion of the store competition is lacking, and most retail competition models still use a top-down modelling framework. The thesis herein provides a new contribution to retail competition literature through the development and use of a retail-competition agent-based model (RC-ABM). Utilizing previous empirical research on consumer expenditures and retail location site selection, competition for home-improvement expenditures is simulated within the home-improvement retail system in the Region of Waterloo, Ontario, Canada. Results exhibit a high level of alignment between the RC-ABM and a traditional Location-Allocation Model (LAM) in estimating a market capture and store revenue acquisition. In addition, while modelled competition itself cannot reproduce the observed spatial pattern of home-improvement stores in our study area, results from the model can be used to identify path dependencies associated with retail success generated by competition and factors affecting retail store survival. Lastly, the presented RC-ABM provides the potential to enrich future land-use and land-cover change models by better representing commercial development

Factors influencing customer loyalty in mobile telecommunications products and services in Australia

Consumer loyalty is considered a critical determinant of repetitive mobile telecommunications business. The Australian telecommunications sector has strong competition among numerous mobile telecommunications service providers because of low switching costs for customers and product price value competitiveness, which lead to high customer switching rates. This situation has led mobile telecommunications companies to recognise that maintaining customer loyalty plays an important role in retaining customers and enhancing business revenue. Customer retention is not an easy task for service providers because of the furious price and marketing competition among the telecommunications market leaders. Thus, customer loyalty is vital for mobile telecommunications service providers both from a financial perspective and mobile service angle. As such, it is necessary for service providers to constantly track the factor of customer loyalty through customer feedback, surveys, reports, research studies and marketing business analysis. The mobile telecommunications sector in Australia is expected to grow in the coming years because of continuous technology evolution. Therefore, it is important for mobile businesses and other retail stakeholders to gain insights into the factors influencing consumer loyalty. Thus, the aim of this research was to explore the factors influencing customer loyalty in the context of mobile telecommunications products and services. The research classified the key prototypes and antecedents of customer loyalty, and gained insights regarding the nature of their relationships with consumer loyalty in the presence of moderators (e.g., age, gender and experience). There is no single framework in previous studies which has demonstrated a comprehensive framework for investigation of the factors impacting customer loyalty in mobile telecommunication sector in Australia. The relationship between customer experience and customer loyalty has been investigated in this research. In previous studies more emphasis was on service quality and customer satisfaction. This study has included a new factor customer experience for further investigation. Moreover, the behavioural intention factor from unified theory of acceptance and use of technology (UTAUT2) model has been contexualised into customer loyalty for the investigation of these dependent and independent variables. The relationships between dependent variables such as facilitating conditions, performance expectancy, effort expectancy, social influence, price value, hedonic motivation, habit, customer satisfaction and customer experience, and dependent variable customer loyalty have been hypothesise in this study. The framework is underpinned by unified theory of acceptance and use of technology (UTAUT2) and marketing-mix theory. The research employed a positivist and quantitative approach. To collect research data, this study used a web-based survey distributed to Australian mobile phone customers, with the help of a marketing company, Research Now. The study sample consisted of Australian customers of mobile telecommunications products and services. In total, 384 responses were received, and the survey was distributed to 1,985 Australian mobile telecommunications customers. The response rate was 19.34 per cent. The collected data was analysed using the SmartPLS tool and partial least squares path modelling (PLS-PM) regression method to answer the research questions and test the research hypotheses. Overall, the research findings supported the theoretical framework of this study, which was an extended version of the unified theory of acceptance and use of technology model proposed in this study, with the majority of examined factors found to have a significant effect on consumer loyalty. Specifically, this study found that customer satisfaction strongly affects customer loyalty. Similarly, facilitating conditions, social influence, performance expectancy, hedonic motivation and product price value positively influence customer loyalty. Given that customer loyalty is most strongly affected by customer satisfaction, it seems imperative that mobile telecommunications businesses devote particular attention to ensuring that customers are satisfied with their products and services. These research findings are consistent with those in the existing literature. This study provides insights into aspects of moderators - such as age, gender and experience - that have not previously been fully investigated in the field of mobile telecommunications products and services. The behavioural intention phenomenon has been studied from the perspective of consumer behaviour and usage, yet not in terms of consumer loyalty. Therefore, this study sought to fill this gap and undertake research in the field of customer loyalty in mobile telecommunications products and services settings. The findings of this study provide important managerial and business implications in the field of mobile telecommunications products and services. The research model will help mobile telecommunications service providers allocate their resources - such as network, infrastructure and customer care resources - according to their relative importance. It is anticipated that the proposed framework in this study will improve perceptions of the factors affecting consumer loyalty and the structural relationships between these factors in the presence of moderators. Additionally, this research should act as a guide for mobile telecommunication businesses to help them better meet customers' requirements, which may eventually lead to an increase in mobile companies' overall sales revenue. Consequently, this research may improve the current parameters to measure customer satisfaction and customer loyalty among mobile service provider companies. The results of the quantitative analysis must be understood within the limitations of this study. The sampling population for this research consisted only of Australian mobile telecommunications customers. Moreover, the author of this thesis acknowledges the limitation of limited access to mobile telecommunications companies. Further, implementation of a qualitative research methodology would improve the moderating results. Finally, the population comprised only a subset of all Australian customers; thus, the findings from this research may not be generalisable to all mobile telecommunications users

Data-driven agent-based exploration of customer behavior

Customer retention is a critical concern for mobile network operators because of the increasing competition in the mobile services sector. Such unease has driven companies to exploit data as an avenue to better understand changing customer behavior. Data-mining techniques such as clustering and classification have been widely adopted in the mobile services sector to better understand customer retention. However, the effectiveness of these techniques is debatable due to the constant change and increasing complexity of the mobile market itself. This design study proposes an application of agent-based modeling and simulation (ABMS) as a novel approach to understanding customer behavior through the combination of market and social factors that emerge from data. External forces at play and possible company interventions can then be added to data-derived models. A dataset provided by a mobile network operator is utilized to automate decision-tree analysis and subsequent building of agent-based models. Popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore possible customer churn scenarios. ABMS is used to understand the behavior of customers and detect reasons why customers churned or stayed with their respective mobile network operators. A CART decision-tree method is presented that identifies agents, selects important attributes, and uncovers customer behavior – easily identifying tenure, location, and choice of mobile devices as determinants for the churn-or-stay decision. Word of mouth between customers is also explored as a possible influence factor. Importantly, methods for automating data-driven agent-based simulation model generation will support faster exploration and experimentation – including with those determinants from a wider market or social context. </jats:p