Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

Predicting mobile advertising response using consumer colocation networks

Building on results from economics and consumer behavior, the authors theorize that consumers' movement patterns are informative of their product preferences, and this study proposes that marketers monetize this information using dynamic networks that capture colocation events (when consumers appear at the same place at approximately the same time). To support this theory, the authors study mobile advertising response in a panel of 217 subscribers. The data set spans three months during which participants were sent mobile coupons from retailers in various product categories through a smartphone application. The data contain coupon conversions, demographic and psychographic information, and information on the hourly GPS location of participants and on their social ties in the form of referrals. The authors find a significant positive relationship between colocated consumers' response to coupons in the same product category. In addition, they show that incorporating consumers' location information can increase the accuracy of predicting the most likely conversions by 19%. These findings have important practical implications for marketers engaging in the fast-growing location-based mobile advertising industry

Interpreting infrastructure: Defining user value for digital financial intermediaries.

The 3DaRoC project is exploring digital connectivity and peer-to-peer relationships in financial services. In the light of the near collapse of the UK and world financial sector, understanding and innovating new and more sustainable approaches to financial services is now a critical topic. At the same time, the increasing penetration and take-up of robust high-speed networks, dependable peerto- peer architectures and mobile multimedia technologies offer novel platforms for offering financial services over the Internet. These new forms of digital connectivity give rise to opportunities in doing financial transactions in different ways and with radically different business models that offer the possibility of transforming the marketplace. One area in the digital economy that has had such an effect is in the ways that users access and use digital banking and payment services. The impact of the new economic models presented by these digital financial services is yet to be fully determined, but they have huge potential as disruptive innovations, with a potentially transformative effect on the way that services are offered to users. Little is understood about how technical infrastructures impact on the ways that people make sense of the financial services that they use, or on how these might be designed more effectively. 3DaRoC is exploring this space working with our partners and end users to prototype and evaluate new online, mobile, ubiquitous and tangible technologies, exploring how these services might be extended.Executive Summary: Drawing from Studies of Use - the value, use and interpretation of infrastructure in digital intermediaries to their users. The UK economy has a huge dependence on financial services, and this is increasingly based on digital platforms. Innovating new economic models around consumer financial services through the use of digital technologies is seen as increasingly important in developed economies. There are a number of drivers for this, ranging from national economic factors to the prosaic nature of enabling cheap, speedy and timely interactions for users. The potential for these new digital solutions is that they will allay an over-reliance on the traditional banking sector, which has proved itself to be unstable and risky, and we have seen a number of national policy moves to encourage growth in this sector. Partly as a result of the 2008 banking crisis, there has been an explosion in peer-to-peer financial services for non-professional consumers. These organisations act as intermediaries between users looking to trade goods or credit. However, building self-sustaining or profitable financial services within this novel space is itself fraught with commercial, regulatory, technical and social problems. This document reports on the value, use and interpretation of infrastructure in digital intermediaries to their users, describing analysis of contextual field studies carried out in two retail digital financial intermediary organisations: Zopa Limited and the Bristol Pound. It forms the second milestone document in the 3DaRoC project, developing patterns of use that have arisen on the back of the technical infrastructures in the two organisations that form cases for examination. Its purpose is to examine how the two different technical infrastructures that underpin the transactions that they support–composed of the back-office hardware and software, data structures, the networking and communications technologies used, supported consumer devices, and the user interfaces and interaction design–have provided opportunities for users to realise their financial and other needs. While we orient towards the issues of service use (and its problems), we also examine the activities and expectations of their various users. Our research has involved teams from Lancaster University examining Zopa and Brunel University focusing on the Bristol Pound over approximately a one-year period from October 2013 to October 2014. Extensive interviews, document analysis, observation of user interactions, and other methods have been employed to develop the process analyses of the firms presented here. This report comprises of three key sections: descriptions of the user demographics for Zopa and the Bristol Pound, a discussion about the user experience and its role in community, and an examination of the role of usage data in the development of these a products. We conclude with final analytical section drawing preliminary conclusions from the research presented.The 3DaRoC project is funded by the RCUK Digital Economy ‘Research in the Wild’ theme (grant no. EP/K012304/1)

Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai

Unprecedented human mobility has driven the rapid urbanization around the world. In China, the fraction of population dwelling in cities increased from 17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses challenges for policymakers and important questions for researchers. To investigate the process of migrant integration, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. We find systematic differences between locals and migrants in their mobile communication networks and geographical locations. For instance, migrants have more diverse contacts and move around the city with a larger radius than locals after they settle down. By distinguishing new migrants (who recently moved to Shanghai) from settled migrants (who have been in Shanghai for a while), we demonstrate the integration process of new migrants in their first three weeks. Moreover, we formulate classification problems to predict whether a person is a migrant. Our classifier is able to achieve an F1-score of 0.82 when distinguishing settled migrants from locals, but it remains challenging to identify new migrants because of class imbalance. This classification setup holds promise for identifying new migrants who will successfully integrate into locals (new migrants that misclassified as locals).Comment: A modified version. The paper was accepted by AAAI 201

Customer Demographic Segmentation Based On Telecom Behavioral Data

In the modern world, digitalization becomes ubiquitous and covers almost every aspect of the business and daily life. Telecom services providers have a major role in these processes due to their involvement in collecting, storing and processing enormous amounts of customer data. This also includes personal telecom services usage data, which if correctly interpreted, might be used for many different purposes. Using telecom data to predict certain demographic characteristics of the customers is helpful in more than one aspect: 1) It could add the acquired knowledge into customer segmentation to better target different customer groups. 2) Such data could be used in cases where traditional historic data is not available- the potential strength of predicting customer credit worthiness based on behavior data is still not fully explored. 3) Last but definitely not least, is the use of data for verifying customer identification in fraud detection. In this paper, an overview of some successful use of telecom data for non-telecom services is shown, as well as with a set of real telco data, statistical techniques are used to demonstrate the relation between mobile telecom services usage and subscription owners’ age. Use of alternative customer data could have enormous implication both on traditional predictive models and could alter the role of the telecoms, making them one of the most important information sources for financial institutions, which operate with sensitive customer data