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Proceedings ArticleDOI

Data warehouse based analysis on CDR to retain and acquire customers by targeted marketing

01 Sep 2016-pp 221-227
TL;DR: A mechanism is developed to store CDR data in a suitable Data Warehouse (DW) schema and analytically process these using OLAP tools to understand the prepaid customers usage, spending and propensity to marketing offers.
Abstract: Retaining an existing customer than acquiring a new one is less expensive in terms of marketing cost, bonus, and incentives offered etc. Telecommunication industry across the globe is stiff competitive environment where market is almost saturated and main focus of customer service becomes retaining existing customers and snatching others' customers to increase market share as well as profit. At the same time telecom industry facing the problem of churn or attrition more than anything especially for prepaid customers as the customers could switch the service providers easily. Telecom operators generate huge volume of call detail records every day for every call, SMS or internet access made by its customers. Telecom operators use these huge operational data for business processing to understand customer behavior. In this paper a mechanism is developed to store CDR data in a suitable Data Warehouse (DW) schema and analytically process these using OLAP tools to understand the prepaid customers usage, spending and propensity to marketing offers. Depending on the usage pattern proper segmentation of the customers has been done and they have been categorized for different types of targeted marketing offers and benefits to retain as well as acquire new customers.
Citations
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Journal ArticleDOI
TL;DR: The aim of this paper is to identify and explore the new paradigm of MCS that is using smartphone for capturing and sharing the sensed data between many nodes and discusses the current challenges facing the collection methodologies of the participants’ data in task management.
Abstract: Mobile crowd-sensing (MCS) is a new sensing paradigm that takes advantage of the extensive use of mobile phones that collect data efficiently and enable several significant applications. MCS paves the way to explore new monitoring applications in different fields such as social networks, lifestyle, healthcare, green applications, and intelligent transportation systems. Hence, MCS applications make use of sensing and wireless communication capabilities provided by billions of smart mobile devices, e.g., Android and iOS-based mobile devices. The aim of this paper is to identify and explore the new paradigm of MCS that is using smartphone for capturing and sharing the sensed data between many nodes. We discuss the main components of the infrastructure required to support the proposed framework. The existing and potential applications leveraging MCS are laid out. Furthermore, this paper discusses the current challenges facing the collection methodologies of the participants’ data in task management. The recent issues in the MCS findings are reviewed as well as the opportunities and challenges in sensing methods are analyzed. Finally, open research issues and future challenges facing MCS are highlighted.

56 citations


Cites background or methods from "Data warehouse based analysis on CD..."

  • ...Maji and Sen [37] developed a mechanism to store CDR data in appropriate data warehouse schematic and analytically process the data using On-Line Analytical Processing (OLAP) server tools to understand the prepaid customer’s usage and spending and provide appropriate marketing offers....

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  • ...receiving terminals as well as an international standardized unique number to identify a mobile subscriber called International Mobile Subscriber Identity (IMSI) [28], [37]....

    [...]

01 Jan 2017

6 citations

Book ChapterDOI
01 Jan 2019
TL;DR: This paper proposes to process these huge transactional data using ETL process and thereafter construction of a data warehouse (DW) which enables the POS provider to employ certain analytical processing for business gain.
Abstract: Point of Sales (POS) terminals are provided by the banking and financial institutes to perform cashless transactions. Over the time due to different conveniences, use of digital money and online card transactions increased many folds. After each successful payment transaction at the POS terminals, a transaction log is sent to the POS terminal provider with payment-related financial data such as date and time, amount, authorization service provider, cardholder’s bank, merchant identifier, store identifier, terminal number, etc. These data are useful for analytical processing which are useful for business. This paper proposes to process these huge transactional data using ETL process and thereafter construction of a data warehouse (DW) which enables the POS provider to employ certain analytical processing for business gain such as knowing own market share as well as position in market with respect to card payments, geographic location-wise business profiling, own as well as competitor’s customer segmentation based on monthly card usage, monthly amount spent, etc.

4 citations

Journal ArticleDOI
TL;DR: The impacts of BI on customer relationship management (CRM) functions (marketing, sales and customer services) in the telecommunications sector in Oman demonstrated mixed impacts, and valuable guidelines for practitioners in the area of CRM, BI, and telecommunications are provided.
Abstract: The application of business intelligence BI techniques for knowledge discovery and decision support empowers organizations in different functions This article examines the impacts of BI on custome

4 citations

Journal ArticleDOI
TL;DR: The main advantage of CDR provides low cost, real-time, noise-free data that captures the evolving dynamics of the movement patterns, and hence any decision taken based on this will be apt and prompt.
Abstract: People are leaving their digital footprint everywhere as they move from one cell tower coverage to another in terms of Call Detail Records (CDR). This huge and variant data can be analyzed to find interesting human mobility patterns for socio-economic development and allow a city administrator to understand the daily commuting patterns of the city dwellers in almost real-time. This paper proposes the techniques and algorithms that use these data to identify (i) optimal transport routes in a city; (ii) Business viable retail outlet location in a city. The aggregated information has been modeled as a network, and graph-theoretic approaches are used to derive a feasible solution. The main advantage of this work is that CDR provides low cost, real-time, noise-free data that captures the evolving dynamics of the movement patterns, and hence any decision taken based on this will be apt and prompt.The only limitation of this study is the unavailability of raw CDR data due to the confidentiality issue for experimental proof of the proposed methods on a real city topology.

3 citations

References
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Book
01 Jan 1992
TL;DR: This Second Edition of Building the Data Warehouse is revised and expanded to include new techniques and applications of data warehouse technology and update existing topics to reflect the latest thinking.
Abstract: From the Publisher: The data warehouse solves the problem of getting information out of legacy systems quickly and efficiently. If designed and built right, data warehouses can provide significant freedom of access to data, thereby delivering enormous benefits to any organization. In this unique handbook, W. H. Inmon, "the father of the data warehouse," provides detailed discussion and analysis of all major issues related to the design and construction of the data warehouse, including granularity of data, partitioning data, metadata, lack of creditability of decision support systems (DSS) data, the system of record, migration and more. This Second Edition of Building the Data Warehouse is revised and expanded to include new techniques and applications of data warehouse technology and update existing topics to reflect the latest thinking. It includes a useful review checklist to help evaluate the effectiveness of the design.

2,820 citations


"Data warehouse based analysis on CD..." refers background in this paper

  • ...A data warehouse is a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management’s decisions [3]....

    [...]

Journal ArticleDOI
TL;DR: Two novel data mining techniques, AntMiner+ and ALBA, are applied to churn prediction modeling, and benchmarked to traditional rule induction techniques such as C4.5 and RIPPER to induce accurate as well as comprehensible classification rule-sets.
Abstract: Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to churn, and to develop an effective retention strategy in accordance with domain knowledge. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It is shown that only limited attention has been paid to the comprehensibility and the intuitiveness of churn prediction models. Therefore, two novel data mining techniques are applied to churn prediction modeling, and benchmarked to traditional rule induction techniques such as C4.5 and RIPPER. Both AntMiner+ and ALBA are shown to induce accurate as well as comprehensible classification rule-sets. AntMiner+ is a high performing data mining technique based on the principles of Ant Colony Optimization that allows to include domain knowledge by imposing monotonicity constraints on the final rule-set. ALBA on the other hand combines the high predictive accuracy of a non-linear support vector machine model with the comprehensibility of the rule-set format. The results of the benchmarking experiments show that ALBA improves learning of classification techniques, resulting in comprehensible models with increased performance. AntMiner+ results in accurate, comprehensible, but most importantly justifiable models, unlike the other modeling techniques included in this study.

307 citations


"Data warehouse based analysis on CD..." refers background in this paper

  • ...Acquiring [8] and retaining a subscriber is the biggest challenge for every telecom operators in modern days of fierce competition....

    [...]

Proceedings ArticleDOI
25 Jun 2012
TL;DR: A novel approach to modeling how large populations move within different metropolitan areas, which takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records from a cellular telephone network, and produces synthetic CDRs for a synthetic population.
Abstract: Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.

286 citations


"Data warehouse based analysis on CD..." refers background in this paper

  • ...start time of a call, and its duration etc [2]....

    [...]

  • ...B[2] are our target for retaining and acquiring new customers....

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Proceedings ArticleDOI
06 Nov 2006
TL;DR: This paper uses the Call Detail Records of a mobile operator from four geographically disparate regions to construct call graphs, and introduces the Treasure-Hunt model to describe the shape of mobile call graphs.
Abstract: With ever growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Toward this end, existing approaches have almost solely focussed on the individual behaviour of customers. Call graphs, that is, graphs induced by people calling each other, can allow telecom operators to better understand the interaction behaviour of their customers, and potentially provide major insights for designing effective incentives.In this paper, we use the Call Detail Records of a mobile operator from four geographically disparate regions to construct call graphs, and analyse their structural properties. Our findings provide business insights and help devise strategies for Mobile Telecom operators. Another goal of this paper is to identify the shape of such graphs. In order to do so, we extend the well-known reachability analysis approach with some of our own techniques to reveal the shape of such massive graphs. Based on our analysis, we introduce the Treasure-Hunt model to describe the shape of mobile call graphs. The proposed techniques are general enough for analysing any large graph. Finally, how well the proposed model captures the shape of other mobile call graphs needs to be the subject of future studies.

184 citations

Journal ArticleDOI
TL;DR: The main finding from the research is that linear models, especially logistic regression, are a very good choice when modelling churn of the prepaid clients, and decision trees are unstable in high percentiles of the lift curve, and the author does not recommend their usage.
Abstract: In this article, we test the usefulness of the popular data mining models to predict churn of the clients of the Polish cellular telecommunication company. When comparing to previous studies on this topic, our research is novel in the following areas: (1) we deal with prepaid clients (previous studies dealt with postpaid clients) who are far more likely to churn, are less stable and much less is known about them (no application, demographical or personal data), (2) we have 1381 potential variables derived from the clients' usage (previous studies dealt with data with at least tens of variables) and (3) we test the stability of models across time for all the percentiles of the lift curve - our test sample is collected six months after the estimation of the model. The main finding from our research is that linear models, especially logistic regression, are a very good choice when modelling churn of the prepaid clients. Decision trees are unstable in high percentiles of the lift curve, and we do not recommend their usage.

100 citations