Workshop on Big Data & Data Science in Retail


ICDM 2017: IEEE International

Conference on DATA MINING 2017


New Orleans, Louisiana, USA

November 18-21, 2017

Important Dates:

All Deadlines are at 11:59PM Pacific Daylight Time.

Note: Workshop Paper Submission Extension to August 14th

Workshop Paper


Workshop Paper


Camera-Ready Final

of Accepted Papers



Big Data and Data Science in Retail

Half Day Workshop

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.

Call For Papers

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.

Paper Format

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:

Topics of interest include but are not limited to:

Machine Learning & Infrastructure

  • Acquisition, representation, indexing, storage, and management of retail data
  • Models, algorithms, and methods for retail data mining and understanding
  • Knowledge discovery from retail data
  • Ontology extraction from retail data
  • Big data tools for retail data science
  • Econometric time series forecasting in retail



  • Reinforcement learning and Markov decision processes for purchase behaviour
  • Deep learning algorithms for personalization/purchase intent
  • External data enrichment: Impact of social media, weather, or legal/health policies on retail
  • Pricing, price elasticity, and promotion optimization
  • Personalization and recommender

Merchandising &


  • Digital marketing attribution
  • Cross-channel marketing across retail (store) & Digital (web and mobile)
  • Forecasting, replenishment, and inventory optimization
  • Customer loyalty segmentation insights
  • Marketing science
  • Cross-functional analytics - Leverage customer insights to forecast demand

Keynote Speakers

Philippe Beaudoin, PhD

Element AI (SVP Research Group)

Philippe cofounded Element AI in 2016 and currently leads its applied and fundamental research groups. His team has helped tackle some of the biggest and most interesting business challenges using machine learning. Philippe's prior explorations included multidimensional time-series analysis during his PhD at Université de Montréal, and bipedal walking control as a postdoc at UBC. He also worked five years at Google as a Senior Developer and Technical Lead Manager, partly with the Chrome Machine Learning team. When he has some free time, Philippe likes to invent new boardgames, and sometimes get them published ;).


Ed Kim

VP Insights, Indigo

Waleed Ayoub

Chief Product Officer, Rubikloud

Brian Keng

Chief Data Scientist, Rubikloud

Kanchana Padmanabhan

Senior Data Scientist, Rubikloud 

Program Committee

Dr. Ayse Bener

Ryerson University

Dr. Zhengzhang Chen

NEC Laboratories America, Inc

Dr. Andreas Veneris

University of Toronto

Dr. Amir Nabatchian


Dr. Andriy Miranskyy

Ryerson University

Dr. Arthur Ryman

Ryerson University

Dr. Tamer Abdou

Ryerson University

Dr. Uzair Ahmad

Ryerson University

Dr. Ceni Babaoglu

Ryerson University

Dr. Pablo Hennings


Dr. Sebnem Kuzulugil

Ryerson University

Dr. Koushik Pal


The workshop is free for all who register to attend ICDM 2017


The Roosevelt New Orleans

130 Roosevelt Way,

New Orleans,

LA 70112, USA