PhD Position on Process Mining in Customer Behavior Unique collaboration between BrandLoyalty and the Jheronimus Academy of Data Science
Consumers can shop for anything, anywhere, anytime and with anyone. Therefore, retailers need to compete on analytics and must analyze customer data carefully. BrandLoyalty is the global leader in providing innovative, incentive-driven loyalty programs designed to drive immediate improvements in retail performance. BrandLoyalty is working together with JADS (Jheronimus Academy of Data Science) to improve its analytical capabilities. As part of a joint research program we are looking for a PhD student interested in the project "Mining Customer Behavior to Increase the Effectiveness of Loyalty Programs and Promotions (MiCuB)". The goal of this project is to apply existing process mining techniques to BrandLoyalty's data and develop new techniques tailored to the analysis of customer behavior. Context The PhD working on Process Mining in Customer Behavior will be employed by the AIS research group (http://www.win.tue.nl/ais/) which is part of the Data Science Centre Eindhoven (DSC/e, http://www.tue.nl/dsce/). DSC/e and Tilburg University collaborate in the context of JADS (Jheronimus Academy of Data Science). Together with BrandLoyalty (http://www.brandloyalty-int.com/) a joint research program has been created. Within this research program are three PhD positions, all sponsored by BrandLoyalty, that work towards better analytical capabilities taking advantage of the wealth of (typically anonymized) customer data. To improve customer engagement, process mining is used to get a deep understanding of actual customer behavior. In fact, understanding of customer behavior has never been easier than now. With the rise of mobile, social, and big data technologies, customers are always-connected and can find information in seconds. This enables one to gain much more detailed and direct information on customer behavior. The "Mining Customer Behavior to Increase the Effectiveness of Loyalty Programs and Promotions (MiCuB)" PhD position will focus on process mining, i.e., the analysis of timestamped customer data. Process Mining in Customer Behavior The PhD student will focus on capturing customer behavior in process models. These models will allow us to better understand the behavior and to systematically explore ways of influencing this through promotions, loyalty programs, and mobile applications. The MiCuB project will cover a broad range of questions, such as "Did sales increase?", "Was the program successful?", "What has influenced the (un)success?", "Will the targets be reached?", "What is the best that could happen?". Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). The interest in process mining is rapidly rising as it is reflected by the growing numbers of publications, citations and commercial tools (Disco, Celonis, ProcessGold, ARIS PPM, QPR, SNP, minit, myInvenio, Perceptive, etc.). In the academic world, ProM is the de-facto standard (www.processmining.org<http://www.processmining.org>) and research groups all over the world have contributed to the 1500+ ProM plug-ins available. This platform will be used to first test ideas that could later be implemented in BrandLoyalty's core IT systems. Position We are looking for candidates for the "Mining Customer Behavior to Increase the Effectiveness of Loyalty Programs and Promotions (MiCuB)" PhD position. The PhD student will join the Architecture of Information Systems group (AIS) at Eindhoven University of Technology (TU/e), which is part of the DSC/e and JADS. Wil van der Aalst will be the promotor and people from both AIS and BrandLoyalty will be involved in the supervision. The AIS group at TU/e is world leader in process mining research and responsible for ProM and has generated a number of spin-offs. Therefore, the group is well equipped to take on this challenge. The PhD student will closely collaborate and spend one day a week within BrandLoyalty. The PhD will focus on applying, extending, and developing process mining to the requirements elicited together with BrandLoyalty. In particular, the results obtained in the project shall both be implemented in software prototypes for validation and research as well as disseminated in trainings. BrandLoyalty will provide its expertise, engineering capabilities, and data for deriving accurate and realistic requirements and will provide opportunities to quickly validate all ideas in a realistic setting. Function Requirements Requirements We are looking for a candidate that meets the following requirements: * possesses a solid background in Computer Science, Data Science, or Mathematics (demonstrated by a relevant Master); * has a strong background in data mining, process mining, machine learning, and/or BPM; * has a strong interest in data science research and real-life applications of analytics; * has strong skills in software development to be able ability to realize research ideas in terms of software prototype; * is highly motivated, rigorous, and disciplined when developing algorithms and software according to high quality standards; * has excellent communication skills in English, both in speaking and in writing (candidates from non-Dutch or non-English speaking countries should be prepared to prove their English language skills); * is a team worker, able to operate in environment with multiple stakeholders. The PhD student is expected to: * perform scientific research in the domain described; * collaborate with other researchers in this project and be self-propelling; * present results at (international) conferences; * publish results in scientific journals; * participate in activities of the group and department, at both sites; * assist in teaching undergraduate/graduate courses; * participate in doctoral training on relevant topics; * be eager to spend one day per week at the BrandLoyalty premises in 's-Hertogenbosch to discuss requirements and research's findings with company stakeholders; * provide training to internal specialists at BrandLoyalty. Conditions of Employment We offer: * A full time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months; * A gross salary of Euro 2083 per month in the first year increasing up to Euro 2664 in the fourth year; * Support for your personal development and career planning including courses, summer schools, conference visits etc.; * A broad package of fringe benefits (e.g. excellent technical infrastructure, child daycare and excellent sports facilities). Information and Application More information * For more information about this position contact Prof.dr.ir. Wil van der Aalst, Eindhoven University of Technology, Department of Mathematics and Computer Science, E-mail: [email protected]<mailto:[email protected]>. * For more information about the employment conditions contact TU/e personnel department, e-mail: [email protected]<mailto:[email protected]> or by telephone: +31 40 247 2321. Application Please apply by using the 'Apply now' button on http://jobs.tue.nl/en/vacancy/phd-position-on-process-mining-in-customer-behavior-298469.html. The reference number of this position is V32.2799<http://jobs.tue.nl/en/vacancy/phd-position-on-process-mining-in-customer-behavior-298469.html>. The deadline is on February 28th 2017 The application should consist of the following parts: * Cover letter explaining your motivation and qualifications for the position (the letter should also show an understanding of process mining and the work done within AIS, see websites such as www.processmining.org<http://www.processmining.org> and the book " Process Mining: Data Science in Action"); * Detailed Curriculum Vitae; * List of courses taken at the Bachelor and Master level including marks; * List of publications and software artefacts developed (if applicable); * Names of at least three referees. -------------------------------------------------------- Wil van der Aalst Eindhoven University of Technology WWW: vdaalst.com<http://www.vdaalst.com/> --------------------------------------------------------
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