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Retail Analytics

Retail Analytics

Elevating retail practice through advanced analytics

The retail value chain, all the way from supplier to end-customer, has seen a huge influx of new technologies such as IoT, robotics, and augmented reality. At the same time new business models, such as omnichannel retail and platforms, have made their entry. These developments have resulted in large amounts of data becoming available throughout the retail supply chain. These big retail data have increased the need for advanced analytics to arrive at actionable insights in many decision areas such as warehousing, transport, assortment planning, and inventory management.

The Retail Analytics Expert Practice advances research on retail analytics and initiates an ongoing dialogue with practice on this topic. Our focus is on retail operations which includes “all activities involved in the selling of physical goods to ultimate customers intended for consumption”. The restaurant business is also part of our focus. Our members actively conduct research in nearly all domains of the retail supply chain, including, but not limited to, warehousing, transportation, store operations, online stores, and omnichannel retail. Our methodological expertise is wide ranging, among others including machine learning, Bayesian statistics, causal inference, choice modeling, mathematical programming, text mining, and much more.

Our research is regularly published in top journals in business, economics, and management. We actively work together with companies for inspiration, for insights into business practice, to obtain data used for our research, and for the dissemination of the insights and techniques resulting from our research.

Research Projects

Omnichannel retailing

Online retail/last-mile logistics

Retail analytics

  • Retail Analytics – ongoing study that surveys the academic literature on retail analytics and takes stock of the most recent developments in terms of technology, data, and analytics in practice.
  • Optimizing Retail Assortments – Methodology for optimizing store-level category assortments.

Restaurant analytics

  • Worth the wait? How restaurant waiting time influences customer behavior and revenue – Article combining empirical analyses and simulations to demonstrate the impact of waiting time on customer behavior, and to estimate the long-term revenue implications of making customers wait.
  • Restaurant analytics – ongoing study on the current applications and vast future potential of analytics applications in all decision domains related to restaurants, ranging from strategic issues (e.g. managing the food supply chain) to operational decisions (e.g. queue management and table allocation).

Inventory management

  • Decision Biases of Empirical Newsvendor Decisions: Target Service Levels are Achieved Effectively, but Inefficiently – An empirical analysis of inventory decisions of bakery products at a large German retail chain. The article identifies different decision biases of managers how these affect company’s performance.
  • Which decision support do empirical newsvendors need? How to use local knowledge best – In a field test we analyze how different decision support tools affect inventory decisions and performance of store managers. This article discusses how manger’s local market knowledge can be used effectively.

Retail distribution – store delivery


Competitive intelligence – analytics in the service sector

  • Optimal location for competing retail service facilities – Analytical approach to solve the location problem for retail service facilities, consumer-facing storefronts, specifically restaurants, that provide a service and compete with other retailers.
  • Pricing under limited competitor data – ongoing study to present a new econometric method for demand estimation based on—widely available—competitor intelligence data, addressing two important and difficult gaps in this stream of research: (1) model estimation with competitor effects (2) model estimation when the firm sells a single product. We use the constructed estimation model to decide on how to set the daily prices for a hotel.
robert rooderkerk

Dr. Robert Rooderkerk

Associate Professor of Operations Management at RSM & Expert Practice Director of Retail Analytics


  • Robert Rooderkerk, Associate Professor and Director // retail analytics, omnichannel retail, marketing-operations interface, assortment planning, new product development, store analytics
  • René de Koster, Professor // warehousing, robotics, material handling, container terminal operations, behavioral operations, retail operations, and sustainable logistics
  • Niels Agatz, Associate Professor // last-mile logistics, ridesharing, drone delivery, on-demand delivery, omnichannel retail, sustainable logistics
  • Debjit Roy, Associate Professor // restaurant analytics, warehousing
  • Remy Spliet, Associate Professor // last-mile logistics, retail transport, sustainable logistics
  • Michael Becker-Peth, Assistant Professor // inventory management, behavioral operations management
  • Jelle de Vries, Assistant Professor // restaurant analytics, warehousing, behavioral operations management, behavior in truck transportation
  • Müge Tekin, Assistant Professor // competitive intelligence, location analytics, restaurant analytics