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.
Authors: Piotr Przymus, Youssef Hmamouche, Alain Casali, and Lotfi Lakhal