A customer segmentation framework for targeted marketing in telecommunication

© 2017 IEEE. Telecommunication industry is highly competitive, and mass marketing is not applicable anymore. Moreover, Mobile customers have different behaviors that urge telecom industries to differentiate their strategies to meet customers' needs. At the same time, mobile operators have an enormous amount of customer records, and data-driven approaches can help them to draw insights from this huge amount of data. Therefore, a data-driven segmentation approach can support marketing strategies to tailor their marketing plans. In this research, we adopt behavior and beneficial segmentation in a two-dimensional framework to segment customers. The results indicate that our method has an outstanding performance for customer segmentation. Moreover, we have recommended some marketing strategies based on each segment's behavior with the aim of increasing in Average Revenue Per User (ARPU) and decreasing in marketing expenses

Understanding Mobile Banking Success Through User Segmentation

Mobile banking (MB) which involves the use of mobile devices to access bank accounts for conducting financial transactions has grown rapidly but unevenly with users. Banks realizes the strategic role of user’s satisfaction and the importance of MB systems in their business models. Yet, the diversity of users and disparity of system usage behaviors make difficult to measure MB success. This study segments the MB users on system use behavior of 4,478 users with objective measures by analyzing the MB system log files on various system usage metrics. Then, a subjective measures study surveys the same users on the system success factors of the information systems (IS) success model by using 445 responses. Results indicate that the influence of success factors significantly varies among user segments for intention to use, which makes an important contribution to enhance interpretation of the IS success model

Direct Marketing Based on Fuzzy Clustering of Customers (Case Study: on one Mobile Company)

Objective There is a general tendency toward direct marketing these days. Therefore, instead of designing advertisement and marketing strategies for all the customers in the market, it is recommended to classify the customers based on clustering techniques and then design specific strategies accordingly. This will reduce marketing and advertisement expenses, increase sale department efficiently, build closer and quicker relationships with different customers and etc. There are a variety of clustering methods. Provided that clustering means classifying customers in different groups with maximum similarities within the groups and maximum difference among the groups, it may not be appropriate to apply such a rule in clustering customers (people) due to their nature. Hence, fuzzy clustering technique seems more appropriate for customers because there are no absolute borders considered among different groups just as the market suggests. This study, then, aims to emphasize on this concept in order to apply fuzzy clustering on market.   Methodology This practical research is descriptive-exploratory in nature of data collection. The statistical population includes all the customers of a mobile company, but due to availability issues only a part of their customers would be involved in the present study. A questionnaire including 6 questions was distributed among those customers and only 760 were correctly responded. Finally, EXCEL and S-PLUS were used to analyze the data.    Findings The data in this study include three different parts of information. The first part includes some indexes selected for analysis of the clustering. Second part concerns with the customers service usage such as distant phone calls, free calls and wireless services. The third part refers to other mobile services provided for each customer. This part is presented in a binary fashion deciding whether a customer has received a specific service or not. Such services include activating more than one mobile line at the moment, using voicemail, paging, internet and other services. This algorithm was used to conduct fuzzy clustering in the present study. Following applying fuzzy clustering, only 2 clusters were judged appropriate for such data. The first cluster includes customers with lower income, job stability and lower loyalty to the mobile company, while the second cluster includes customers with higher income, higher job stability and more loyalty to the mobile company. The customers in the first cluster used services like free calls, wireless networks and pay phones. On the other hand, the customer in the second cluster mainly used services like distant calls and rarely used wireless services. In general, we can claim that paging services were the highest requested and then there are voicemail services, internet, and e-pay services respectively. The two clusters reported to have a similar tendency in using services such as voicemail, multi-lines, conferencing; yet, they were different in services like paging, internet, call forwarding (diverting), call waiting and e-pay services. At the end, mobile companies can set marketing strategies based on such findings.   Conclusion It is suggested that mobile companies focus on general advertisements and distant call services, but only a little focus on wireless services. They can also put more thought on services like paging, voicemail, internet and e-pay services respectively. It is also recommended that, for female customers (mostly within the first cluster), the companies should focus on pay phone services, distant calls, and free calls as well as voicemail and internet. On the other hand, for male customer with higher job stability, it is suggested to focus the most on distant call services and provision of special discounts with this regard, but the least on wireless and pay phone services. Besides, voicemail services, paging, call waiting, call forwarding and e-pay services should be the mobile company’s priority for male customers

An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry

Background: Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas. Motivations: With the rapid increase of transactional data, the RFM model can accurately segment customers and provide deeper insights into customers’ purchasing behaviour. However, the traditional RFM model is limited to 3 variables, Recency, Frequency and Monetary, without revealing segments based on demographic features. Meanwhile, the contribution of demographic characteristics to marketing strategies is extremely important. Methods/Approach: The article proposed an extended RFMD model (D-Demographic) with a combination of behavioural and demographic variables. Customer segmentation can be performed effectively using the RFMD model, K-Means, and K-Prototype algorithms. Results: The extended model is applied to the retail dataset, and the experimental result shows 5 clusters with different features. The effectiveness of the new model is measured by the Adjusted Rand Index and Adjusted Mutual Information. Furthermore, we use Cohort analysis to analyse customer retention rates and recommend marketing strategies for each segment. Conclusions: According to the evaluation, the proposed RMFD model was deployed with stable results created by two clustering algorithms. Businesses can apply this model to deeply understand customer behaviour with their demographics and launch efficient campaigns

Assessing industrial ecosystem vulnerability in the coal mining area under economic fluctuations

In the context of the depth adjustment of the global economy and wild fluctuations in energy prices, the vulnerability issue of the coal mining industrial ecosystem (CMIES) has seriously affected the sustainable development of the regional economy. Comparisons of CMIES health status at a regional level are worthy of being conducted. This not only contributes to understanding a particular coal mining area's situation in regards to CMIES vulnerability, but also helps to discover a meaningful benchmark to learn the experiences in terms of action programmes formulation. In this study, based on the analysis of the vulnerability response mechanism of CMIES to economic fluctuations, an initial indicator system for vulnerability assessment of CMIES was constructed. Ultimately, 14 vulnerability-evaluating indicators and their weights were obtained using rough set attribute reduction. Based on a composite CMIES Vulnerability Index (CVI), the Rough Set-Technique for Order Preference by Similarity to Ideal Solution-Rank-sum Ratio (RS-TOPSIS-RSR) methodology is proposed to conduct the CMIES vulnerability assessment process from an overall perspective. Using this methodology, 33 coal mining areas in China are ranked as well as grouped into three specific groups based on the CVI score. The results demonstrate the feasibility of the proposed method as a valuable tool for decision making and performance evaluation with multiple alternatives and criteria

Mining Extremes through Fuzzy Clustering

Archetypes are extreme points that synthesize data representing "pure" individual types. Archetypes are assigned by the most discriminating features of data points, and are almost always useful in applications when one is interested in extremes and not on commonalities. Recent applications include talent analysis in sports and science, fraud detection, profiling of users and products in recommendation systems, climate extremes, as well as other machine learning applications. The furthest-sum Archetypal Analysis (FS-AA) (Mørup and Hansen, 2012) and the Fuzzy Clustering with Proportional Membership (FCPM) (Nascimento, 2005) propose distinct models to find clusters with extreme prototypes. Even though the FCPM model does not impose its prototypes to lie in the convex hull of data, it belongs to the framework of data recovery from clustering (Mirkin, 2005), a powerful property for unsupervised cluster analysis. The baseline version of FCPM, FCPM-0, provides central prototypes whereas its smooth version, FCPM-2 provides extreme prototypes as AA archetypes. The comparative study between FS-AA and FCPM algorithms conducted in this dissertation covers the following aspects. First, the analysis of FS-AA on data recovery from clustering using a collection of 100 data sets of diverse dimensionalities, generated with a proper data generator (FCPM-DG) as well as 14 real world data. Second, testing the robustness of the clustering algorithms in the presence of outliers, with the peculiar behaviour of FCPM-0 on removing the proper number of prototypes from data. Third, a collection of five popular fuzzy validation indices are explored on accessing the quality of clustering results. Forth, the algorithms undergo a study to evaluate how different initializations affect their convergence as well as the quality of the clustering partitions. The Iterative Anomalous Pattern (IAP) algorithm allows to improve the convergence of FCPM algorithm as well as to fine-tune the level of resolution to look at clustering results, which is an advantage from FS-AA. Proper visualization functionalities for FS-AA and FCPM support the easy interpretation of the clustering results

besolidary! : Influencia de factores económicos y sociales en la acción voluntaria y su aplicación en el desarrollo de una herramienta informática para el fomento del voluntariado

El auge y democratización de las nuevas tecnologías ha provocado que casi cualquier ciudadano tenga al alcance de su mano un dispositivo con conexión a internet, teniendo así disponible todo el conocimiento humano en la palma de su mano. Sin embargo, el interés cada vez más creciente por obtener un rédito económico de la explotación de las TIC ha dejado en un segundo plano la aplicación de estas tecnologías a ámbitos solidarios y sin ánimo de lucro. Por ello, en este proyecto se propone la creación de una fundación -cuyo fin último será la promoción y mejora de la solidaridad con ayuda de la tecnología- y la elaboración de una aplicación informática capaz de fomentar el número de acciones solidarias realizadas por los ciudadanos. No obstante, llevar a cabo un proyecto de estas características requiere conocimientos amplios en cuanto a la solidaridad, el voluntariado y la sociedad se refiere. Por ello, el proyecto se realiza de forma conjunta con una estudiante de Sociología de la Universidad Carlos III de Madrid (Paula Pascual Zamora). De esta forma, se consiguen analizar y comprender todas las aristas del proyecto: sociales, económicas y tecnológicas. Para ello, en primer lugar se realiza un análisis de entorno y una evaluación económica del proyecto para corroborar la viabilidad del mismo. Si bien no existe ánimo de lucro, se busca garantizar la supervivencia de la fundación y la aplicación. Elaborar el Plan de Negocio en primer lugar es imprescindible, pues permite estudiar la viabilidad del proyecto y, además, muchas de las características fundamentales de la aplicación informática -que son, a su vez, una ventaja competitivasurgen del estudio pormenorizado de la sociedad y la colaboración y de las necesidades de los usuarios y entidades sin ánimo de lucro que harán uso de la misma. Por otra parte se realiza un análisis cuantitativo de qué factores demográficos y socio-económicos son los que más afectan al voluntariado y cuál es el perfil de las personas que realizan acciones solidarias, de forma que estos estudios sirven para complementar la investigación sociológica llevada a cabo por Paula Pascual Zamora y las conclusiones extraídas del Plan de Negocio. Como resultado de estos análisis cuantitativos se deduce qué factores, como la tasa de paro, la edad media y el número de asociaciones de una región, influyen directamente en el número de habitantes de esa región que realizan voluntariado. Además, se concluye que es posible realizar perfiles fiables de las personas que realizan acciones solidarias. Fruto de todos estos procesos previos surgen los requisitos de usuario para el diseño y desarrollo de la aplicación informática, que se lleva a cabo bajo una metodología RUP adaptada a procesos ágiles. El objetivo principal de la misma es poner en contacto a las organizaciones que realizan algún tipo de ayuda con los usuarios que realizan acciones solidarias. Para ello, se contemplan todo tipo de acciones de ayuda mutua (voluntariado, donaciones económicas y en especie y prestación de servicios no remunerados), sin incluir ningún tipo de limitación como sí hacen plataormas ya existentes. Así mismo, se elabora un buscador y un meta-buscador propios para poner a disposición del usuario no sólo las peticiones de ayuda de las entidades sin ánimo de lucro inscritas en la plataforma que aquí se desarrolla, sino también las peticiones de otras entidades externas a la plataforma. Para incrementar la diferenciación de esta aplicación con respecto a las ya existentes -y, por ende, crear una nueva ventaja competitiva- se implementa un algoritmo de aprendizaje automático capaz de analizar los tipos de perfiles de personas solidarias existentes y así poder recomendarles contenidos personalizados, fomentando así, aún más si cabe, la realización de acciones de ayuda mutua. Durante todo este proceso se trata, por tanto, de conocer y comprender mejor los factores del entorno e individuales que afectan a la cantidad y el tipo de acciones solidarias que llevan a cabo los individuos, y de aplicar estos conocimientos al desarrollo de una aplicación informática capaz de fomentar el voluntariado y la solidaridad. Para garantizar la supervivencia de este proyecto sin ánimo de lucro se lleva a cabo un análisis económico y un Plan de Negocio, que marca los pasos a seguir para la consecución satisfactoria del proyecto. Todo ello realizado, en un primer momento, como proyecto piloto en la ciudad de Madrid y con el apoyo de diferentes fundaciones, asociaciones y organismos gubernamentales que han decidido hacer un seguimiento del proyecto como Entidades Promotoras Observadoras. VIIIDoble Grado en Ingeniería Informática y Administración de Empresa