Offline retail channel, Category Management, assortment optimization, clustering, retailing


In this paper we present a practical approach that AB InBev’s Global Capabilities Centre (“GCC or ABI or Ab InBev”) has developed to solve the challenges of Category Management for Retailers. The approach brings technical rigor from the areas of data science, econometrics, and measurement methodologies very close to business context. This has allowed us to create a solution which is highly contextual and relatable to our business stakeholders. The strength of the presented solution lies in it being a semi-automated framework that allows a wide array of disparate data to be modelled and captures the nuances of different markets - such as socio-demographic profiles, consumption behaviours, local preferences towards beer styles. We also present the ABI created 4C framework to arrive at the optimal assortment recommendation for a Retailer.

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  • McFadden, Train, Tye (1978). An Application of Diagnostic Tests for the Independence from Irrelevant Alternatives Property of the Multinomial Logit Model, Transportation Research Record Journal of the Transportation Research Board
  • Guadagni PM, Little JDC (1983). A logit model of brand choice calibrated on scanner data. Marketing Science
  • Wierenga B (2008). Handbook of Marketing Decision Models, International Series in Operations Research and Management Science, Vol. 121 (Springer, New York)
  • Talluri, K. and van Ryzin, G. (2004). ‘Revenue management under a general discrete choice model of consumer behavior’, Management Science
  • ML Fisher, R Vaidyanathan (2009). An algorithm and demand estimation procedure for retail assortment optimization
  • JM Davis, G Gallego, H Topaloglu (2014). Assortment optimization under variants of the nested logit model- Operations Research
  • TL Urban (1998). An inventory-theoretic approach to product assortment and shelf-space allocation - Journal of Retailing
  • H Hwang, B Choi, G Lee (2009). A genetic algorithm approach to an integrated problem of shelf space design and item allocation   Computers & Industrial Engineering
  • R Chen, H Jiang (2017). Capacitated assortment and price optimization for customers with disjoint consideration sets - Operations Research Letters
  • F Bernstein, S Modaresi, D Sauré (2019). A dynamic clustering approach to data-driven assortment personalization- Management Science
  • Feldman, A Paul, H Topaloglu (2019). Assortment optimization with small consideration sets - Operations Research
  • ML Fisher, R Vaidyanathan (2012). Which Products Should You Stock? Harvard Business Review, November 2012
  • Fishburn, Peter C. (1970). Utility Theory for Decision Making. Huntington, NY: Robert E. Krieger