Subscriber Behavior Research Articles

Modelling revenue generation in a dynamically priced mobile telephony service

Dynamic pricing has been used extensively in specific markets for many years but recent years have seen an interest in the utilization of this approach for the deployment of novel and attractive tariff structures for mobile communication services. This paper describes the development and operation of an agent based model (ABM) for subscriber behavior in a dynamically priced mobile telephony network. The design of the ABM was based on an analysis of real call detail records recorded in a Uganda mobile telephony network in which dynamic pricing was deployed. The ABM includes components which simulate subscriber calling behavior, mobility within the network and social linkages. Using this model, this paper reports on an investigation of a number of alternative strategies for the dynamic pricing algorithm which indicate that the network operator will likely experience revenue losses ranging from a 5 %, when the pricing algorithm is based on offering high value subscriber cohort enhanced random discounts compared to a lower value subscriber cohort, to 30 %, when the priding algorithm results in the discount on offer in a cell being inversely proportional to the contemporary cell load. Additionally, the model appears to suggest that the use of optimization algorithms to control the level of discount offered in cells would likely result in discount simply converging to a no-discount scenario. Finally, commentary is offered on additional factors which need to be considered when interpreting the results of this work such as the impact of subscriber churn on the size of the subscriber base and the technical and marketing challenges of deploying the various dynamic pricing algorithms which have been investigated.

Enhancing Telco Service Quality with Big Data Enabled Churn Analysis: Infrastructure, Model, and Deployment

The penetration of mobile phones is nearly saturated in both developing and developed regions. In such a circumstance, how to prevent subscriber churn has become an important issue for today’s telecom operators, as the cost to acquire a new subscriber is much higher than that to retain an existing subscriber. In this paper, we propose to leverage the power of big data to mitigate the problem of subscriber churn and enhance the service quality of telecom operators. As the information hub, telecom operators have accumulated a huge volume of valuable data on subscriber behaviors, service usage, and network operations. To enable efficient big data processing, we first build a dedicated distributed cloud infrastructure that integrates both online and offline processing capabilities. Second, we develop a complete churn analysis model based on deep data mining techniques, and utilize inter-subscriber influence to improve prediction accuracy. Finally, we use real datasets obtained from a large telecom operator in China to verify the accuracy of our churn analysis models. The dataset contains the information of over 3.5 million subscribers, which generate over 600 million call detail records (CDRs) per month. The empirical results demonstrate that our proposed method can achieve around 90% accuracy for T + 1 testing periods and identify subscribers with high negative influence successfully.

Influence of Demography, Religiosity and Porting Behaviour on Switching Behaviour of Mobile Subscribers: Evidence from A Developing Country

The paper examines the extent to which consumer demography, religiosity and porting behaviour predict consumer switching behaviour (CSB) in the context of mobile subscribers from an emerging market perspective. Drawing from relevant literature, a predictive model was developed to test the influence of demography, religiosity and porting behaviour on CSB. Empirical data were collected through a cross-sectional survey design involving 736 mobile subscribers from six telecoms in Ghana. Data were analysed using binary logistics regression to test the proposed model. The results indicate that the effect of some demographics (age, income and length of relationship) and porting behaviour were statistically significant in predicting mobile subscribers’ switching behaviour, while other demographics (gender, education, marital status and phone use experience) and religiosity did not contribute to predicting switching behaviour of mobile subscribers in Ghana. The theoretical and managerial implications of this study are discussed. The limitations of this study offer avenues for future research.

Managing the Process of Segmentation on the Mobile Phone Subscribers

Most telecommunications providers possess a remarkable amount of data about their subscribers.The knowledge that we would discover in the database of telecommunications providers is vital to understanding the behavior of subscribers. We talk about subscribers segmentation. The segmentation will identify and select the subscribers most likely to respond favorably to offers. Our paper proposes a set of techniques to analyze and design tools that manages the process of data acquisition, data cleaning and selection of the segmentation algorithm.

Quality Expectations of Mobile Subscribers

Mobile systems, by nature, have finite resources. Radio spectrum is limited, expensive and shared between many users and services. Mobile broadband networks must support multiple applications of voice, video and data on a single IP-based infrastructure. These converged services each have unique traffic holding and quality requirements. A positive user experience must be obtained through efficient partitioning of the available wireless network resources. The 3 rd Generation Partnership Project (3GPP) has developed a comprehensive Quality of Service (QoS) parameter to address this problem. The regular control of service quality is critical for operators to ensure user Quality of Experience (QoE), establish new business models and monetize services. It enables operators to employ fair-use resource policies and maintain network performance during peak traffic times. Wireless mobile communication is tending towards an integrated system of Internet and telecommunication technologies, where mobile users move freely anytime and everywhere. They desire to communicate with any device using the best service available. In this paper QoS management issues in mobile communication are described. The authors present an insight into subscriber behavior and related factors that affect the QoE of mobile data services.

Analyzing the Indian Subscriber Behavior Towards Mobile Social Media - A Data Monetization & Customer Engagement Perspective

The Indian Mobile Services Industry is going through a paradigm shift in terms of their business model-moving away from a Voice centric business to a Data focused business. Mobile Operators are looking at new and innovative avenues to increase the data penetration or the contribution of non-voice revenue (currently hovering around 11%) with respect to their overall service revenue. A similar paradigm shift is also being witnessed in the manner in which communication and social interaction happens-with Social Media emerging as a front runner when compared to hitherto popular communication mediums: email and instant messaging service. The immense popularity of social media has attracted advertisers and enterprise users to effectively engage their prospective as well as existing customers using social media. In emerging markets like India, Social Media has been used for customer engagement and monetization. Every day innovation in the mobile social media space is defining a win-win monetizing model for all the players in the value chain-device manufacturers, technology providers, telecom operators, and social media players. Operators are recognizing the need to investigate the subscriber behavior and aspirations relating to Mobile based Social Media. The extra-large investments in next generation high-bandwidth technologies like 3G & Broadband Wireless Access (BWA) will provide the required momentum to the mobile operators as subscribers resort to user generated content over social media. The paper analyzes the subscriber behavior to ascertain the avenues for customer engagement and monetization using this channel.

Traffic Network and Optimization a Future Subscriber's Mobile Telecom Operator in Train

In this work, we studied the behavior and mobility of telecom subscribers into a train for now and predict a future telecom movement subscriber and have a perfect resources data signal of an operator mobile telecom, we used a deterministic and a probabilistic method. the train in our model example travels through a cellular network and passed into four zones (Z1, Z2, Z3 and Z4), each one is characterized by: topography, numbers of the cellular network, types of network (GSM, GPRS, UMTS ...), numbers of subscribers, types of subscribers(staffs, students, workers and others), numbers of operators (Operator 1, Operator 2 and Operator 3). We have studied statistics in deterministic and probabilistic vision of traffic telecom; the model used is approach to vehicular traffic model.

Detecting GSM churners by using Euclidean Indexing HDMR

Preventing GSM subscribers to move to another operator is an important and crucial issue for the GSM operators. Churn management is of essential importance in detecting loyal and hopeless subscribers. Keeping current GSM number when changing the GSM operator also facilitates these subscribers to switch to another operator. Euclidean Indexing High Dimensional Model Representation (HDMR) method is a polynomial based modeling method which is used to predict the churner behavior of the GSM subscribers. An up-to-date data set consists of demographic information and call details records with the related churn behavior is used to model the churner detection problem. The proposed method uses 640 randomly selected training nodes for the modeling process while 316 nodes are used to examine the performance of the proposed method and to make comparisons with the data mining techniques.

Comparing Rural and Urban Social and Economic Behavior in Uganda: Insights from Mobile Voice Service Usage

The analysis of Call Detail Record (CDR) data sets generated by mobile telephony networks has generated much interest in recent years, particularly as an easily accessed source of large volumes of data capable of reflecting the dynamic behavior of humans across a range of temporal and spatial scales. This paper presents a study focused on examining human social and economic behavioral patterns in Uganda through the analysis of a CDR data set generated in a Ugandan mobile telephone network in 2010. By examining the response of subscribers to a service incentivizing higher mobile phone call rates through the offering of discounts, economically motivated differences in subscriber behavior in poorer versus wealthier regions of the country are identified. The paper also presents an analysis which suggests a high degree of social insularity within the regions of Uganda which is most likely related to regionally economic development levels in addition to the high levels of ethnic homogeneity within those regions. A methodology for identifying centers of economic activity using the data set alone is also presented and the accuracy and implications of the resultant regional patterns are discussed. Finally, measures of human mobility, and its relationship with economic and social regional characteristics, are examined through the use of graph theoretic based analysis techniques.

An innovative price plan monitoring and advisory system: A case study of mobile telecom service in Thailand

Article history: Received October 23,2013 Received in revised format November 25, 2013 Accepted November 27, 2013 Available online November 27 2013 An innovative price plan monitoring and advisory system simulates subscriber usage consumption for offering suitable price plan. The aim of this paper is to develop the decision support system by using Statistical Process Control (SPC) to identify subscriber usage behavior and provide critical visibility into subscriber consumption to detect their inappropriate usage especially in exceeding usage. To explore subscriber usage behavior, a forecasting model and a regression is employed to identify related factors and predictive usage model. The innovative price plan monitoring and advisory system has been verified and validated with one of the largest telecommunication company in Thailand. Using decision support system with effective control chart and real subscriber behavior pattern help mobile network operator grow their revenues and profits by offering an appropriate price plan as well as improve subscriber experience with more flexible choice to meet their individual usage consumption needs. © 2014 Growing Science Ltd. All rights reserved.

Preserving Facebook Records: Subscriber Expectations and Behavior

AbstractThe number of social networking services and the number of subscribers to those services have grown at an exponential rate over the last decade. This study examines how long Facebook subscribers expect their records to persist and how these expectations affect subscriber’s use of Facebook. I use an online survey (n = 144) and a focus group (n = 6) to determine Facebook subscribers’ preservation expectations and to what extent these expectations affect the type of information subscribers choose to share. I found that respondents expect records to persist for less than ten years, and that respondents alter their sharing behavior based on their preservation expectations. A better understanding of subscribers’ attitudes toward preservation, and the effect of these attitudes on the information subscribers choose to share, is an important step in developing a workable preservation strategy for these records.

Gini in A Bottle: A Case Study of Paretos Principle in the Wild

This paper presents a case study of socio-economic disparities - human factors - having tremendous impact on the performance and behavior of a cloud-based software system. Failures to take such factors into account lead to serious design, implementation, and operational problems. A detailed empirical analysis of a commercial mobile network address book web application, serving over 8.3 million subscribers, was conducted for the joint purposes of building realistic subscriber behavior and data models and to explain certain performance characteristics and expectations. Extensive analysis of anonymized production data revealed that many aspects of users data and activity exhibited strongly-heavy-tailed characteristics, particularly characteristics affecting database performance and interactive request latencies, which could be ameliorated by traditional techniques such as caching or multi-threading. Several performance-critical aspects of users data were found to be well-described by the Log-Normal probability distribution, were heavily-skewed to the right, and exhibited Gini coefficients consistent with income inequalities in the Western world. The analytical model was translated into enhanced simulation and performance tooling, enabling more realistic performance and capacity testing of the product. Our deeper understanding also lead to changes in monitoring and system performance evaluation and quality-of-service parameters, statements of performance and capacity ratings, and architecture that would not otherwise have been justifiable

A Data Driven Exploration of Broadband Traffic Issues: Growth, Management, and Policy

This paper uses a novel dataset from multiple ISPs to characterize important trends in broadband traffic growth, develop a better understanding of usage-related costs, and consider the implications of this growth for both network operators (e.g. subscription plans and traffic management policies) and policy makers (e.g. interconnection policies and market structure). We begin by carefully considering the components of overall traffic growth. These include growth from additional users and devices attached to the network as well as growth from the changing behaviors of users and devices. Teasing apart these different types of growth is particularly important as markets for new subscribers saturate. Second we consider temporal properties of traffic at various time-scales which informs the tradeoffs inherent in alternative ways for defining peak traffic periods. These peak periods are important as they are primary drivers of network provisioning and service design decisions, and thus factor into cost estimation, investment planning, and policy analyses. Further, since no standard exists for defining “peak traffic periods,” the term is often employed overly causally in both technical and policy discussions. While a variety of definitions are all reasonable, understanding the differences is worthwhile. We next explore the connection between traffic, traffic during peak periods, and costs to the network providers of carrying traffic. We consider the economic, business, and policy tradeoffs for how these costs are shared. Central to this paper is a unique pool of data that a set of broadband access providers have shared with our research group. The data-set that informs this report is a large set of anonymous per-subscriber byte counts over different time intervals. Some data sets allow us to look at behavior at a 15 minute granularity. (The only per-subscriber information received was the equivalent of interface byte counters associated with anonymized subscriber identifiers so that we could track individual subscribers over multiple months. No personally identifiable information of any nature was received from providers.) This data allows us to ask and answer some key, and sometimes contentious questions, about traffic on broadband networks, such as: What is the distribution of usage across the subscriber population? What is the distribution of usage during peak periods across the subscriber population? What do daily traffic patterns look like? What is the long-term behavior of subscribers that occasionally appear in the tails of the usage distribution? How good are various heuristics (such as total traffic over a month) at approximating contributions to peak time periods? How much is per-subscriber traffic growing yearly? This rich data set provides more detailed insights than studies that are based only on long-term usage data or on aggregates of many users. Because we have per-subscriber traffic we can reconstruct the actual aggregate and peak traffic, whereas if all we had were the peak and aggregate traffic statistics (or worse only one or the other), we could only make inferences about per subscriber behavior that would depend on a number of strong simplifying assumptions. Our analysis of fat-tailed user behavior highlights just how misleading such inferences might be.

A subscriber classification approach for mobile cellular networks

The classification of subscriber types in mobile cellular networks is valuable for network service providers since it provides a mechanism to plan network services by better understanding subscriber behaviour in a network. Mobile networks contain vast repositories of data that store valuable information regarding subscriber behaviour. In this paper, a new approach for subscriber classification in mobile cellular networks is proposed. The proposed approach considers network traffic generated from a mobile cellular network operator in South Africa. The proposed approach makes use of a difference histogram approach for feature extraction and a fuzzy c-means clustering algorithm to classify traffic data into subscriber classes. To validate the proposed approach, a comparative analysis of two different multi-resolution feature extraction approaches, the empirical mode decomposition (EMD) approach and the discrete wavelet packet transform (DWPT) are compared with results obtained with the difference histogram (DH) approach. It is shown that the difference histogram provides better clustering results when compared to the two multi-resolution approaches demonstrating the potential of the difference histogram approach.

Subscriber behavior in adopting 3G value-added services

Third generation (3G) mobile services have not performed as much as they were expected to do a decade ago due to limited exemplary handset capacity, overlooking subscriber behavior and so on. This paper examined customers’ behavioral intention of adopting 3G value-added services, particularly, the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) models. Structural Equation Modeling (SEM) was developed to analyze the data set which included 233 interviews on customer perception of Secured Handset Enabler (SHE) application. Important factors such as perceived usefulness and perceived ease-of-use are incorporated into the models. Some useful findings were presented which will help an Application Service Provider (ASP) to correctly analyze and understand subscribers’ intentions and service demand, and the needed strategies to promote their services. Key words: 3G Services, secured handset enabler (SHE), value-added service, theory of planned behavior (TPB), technology acceptance model (TAM).

Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study

As the profit margins of 3G mobile network operators gradually decline, and market competition becomes increasingly intensive, they must develop rich and diverse varieties of brand new application services to attract new subscribers and retain old ones. Understanding the customer’s purchasing behavior is a key issue in this process. The operator must accurately grasp movements in the market based on analysis of the behavior of 3G subscribers. This study proposes a comprehensive customer relationship management strategy framework to furnish a beneficial plan to overcome such challenges. First, we propose a new model to identify who are the high-value customers related to the characteristics of new telecommunication services. After segmenting the customers, we propose a procedure to provide different kinds of usage analysis, including inter-cluster analysis and intra-cluster analysis. The experimental results are determined based on rules extracted from a large number of call detail records generated by the mobile subscribers of leading 3G mobile system operators in Taiwan. The dependency network demonstrates the relationship between voice services, data communications, message services, micropayments and entertainment. Finally, we propose some marketing recommendations for 3G system operators based on these interesting rules.

Transit traffic service in communications networks

Servicing of the traffic in the transit operator network is discussed. The conceptual scheme of servicing of requests in the transit operator network is proposed. This scheme makes it possible to estimate both the subscriber behavior and the actions of the equipment employed by a transit operator and other operators participating in connection. The points of generation and the causes repeated passage of calls (signaling messages) over communications channels are shown. Recommendations for organizing measurements and interpreting the indicators of quality of service are presented.

Privatisation and transparency: A marketing perspective

The healthcare problem is to provide access to high-quality services to the overall population for an affordable cost. Among industrial nations, only the USA has taken the private market approach, covering most people through employers. Since 2001 the USA has sought to move the coverage or the costs of coverage more directly to the individual. The US approach utilises transparency of price and quality, insurance initiatives such as consumer-driven healthcare and pay for performance. What may other nations learn from these efforts — something to emulate or to avoid? Contrasted to individuals, competition and the purchasing power of large employers give them the edge in taming the healthcare market. However, the data suggest they have been remiss in using the available information to influence provider or employee behaviour. Internationally, many employers are using healthcare as a recruiting and retention tool. Transparency and effective education and motivation of employees hold great promise, but the US model suggests much more aggressive action is needed from employers before the system will bear fruit. A variety of strategies for using information in a more patient-centred way and as a means to influence provider and subscriber behaviour are shown.

Revealed reader preference for marketing journals

In this paper we assess the reading preference for marketing journals by deriving a journal utility scale based on click responses to table of contents postings to the ELMAR virtual community. Use of ELMAR data provides a unique window into journal importance, allowing us to look at table of contents reading behavior rather than citing behavior or attitudinal constructs as has been the case in previous studies. At the time of the study, there were more than 5,100 ELMAR subscribers, easily making this the largest journal ranking study ever undertaken. The external validity of our sample is high since ELMAR subscribers include a high percentage of the population of those interested in scholarly research in marketing. Our results are based on actual choice behavior of subscribers as they simply decide what tables of contents to read. The list of journals included in the study contains 165 different titles posted during the study period, which spanned a year. During that period there were multiple issues posted of these 165 journals, resulting in a replicated field study with high reliability. In fact, our journal reading utility measurement yielded a model with an R2 value of .95. We use this model to suggest ways that researchers might come to a richer understanding of the journal consumption process.

Modeling and Analysis of Mobile Terminal Power on/off-State Management Considering User Behavior

Mobile terminals (MTs) in real personal communication service (PCS) environments are turned off due to limited battery lifetime, avoidance of unwanted incoming calls, and undesirable subscriber behaviors. Since the probability that an MT is in the off-state may be significant, MT power on/off-state transition behavior and the effect of state management on mobile communication networks require analysis. Two MT power on/off-state management schemes are introduced based on the level of location registers (LRs) for managing MT state information. Effects are investigated from the aspects of a radio interface, a network, and a database based on the modeling of the state transition behavior of an MT for varying values of MT and mobile communication network parameters. These values include the MT on/off duration and the incoming call arrival rate. Although additional database processing overhead exists, the management of MT power on/off-states provides a significant reduction in overhead from the aspects of the radio interface and the network. There is a tradeoff between the two management schemes as the MT on/off ratio varies.