The rapidly changing landscape of technology is creating new opportunities and challenges for retailers. New data sources coupled with traditional retail data unleash the potential for innovative solutions in the retail industry.
Broadly speaking, retailers consider problems across two key domains: 1) Merchandising and Operations and 2) Marketing. Whereas the former focuses largely on product assortments, pricing and mass promotional decisions, and inventory and supply chain management, the latter focuses on promoting awareness and improving overall customer experience. Data mining and statistics-driven decision making have been the keys to success in both these domains.
However, retail data has increased exponentially in volume, variety, and velocity with every passing year. This includes both traditional retail data (e.g. transactional sales, inventory and logistics, and customer loyalty, etc.), as well as “newer” data sources from online, mobile (e.g. apps, IoT, etc.) and other external sources such as social and real-time data (e.g. weather, satellite imaging etc.).
Coupled with advancements in data and computing systems, the application of big data tools and machine learning techniques to this plethora of retail data offers exciting new opportunities to develop competitive solutions for innovative retailers.
This Workshop aims to provide a forum for academic researchers and industry professionals to share their latest findings on problems relating to the analysis and exploration of retail data. While there are very important retail problems that have been solved with data mining over the past decade, we want to place emphasis on new problems and methods that arise from the combination of new data sources such as social, IoT, mobile browse, online competitive information, big data technologies, or recent advances in deep learning and data mining.
Submissions are invited to address the need for developing new methods to mine, model, summarize and integrate the huge volume of the structured and unstructured retail data that can potentially lead to significant advances in the field.
Authors are invited to submit a full paper by clicking here. Each submission should be regarded as an undertaking that, if the paper is accepted, at least one of the authors must register and attend the conference to present the work. No-show papers will not be included in the proceedings.
Papers must be submitted electronically, in PDF, and should be limited to a maximum of 8 pages in the standard IEEE 2-column format, following the IEEE ICDM format requirements. Please find more information here: