Economy & Management
Italy, January 2018
by Lucia Paladino
Traditionally, fashion companies have used rather rigid systems to determine prices and assortment choices. The consequence was excessive production and allocations and stock surpluses, or inadequate stocks and lost sales.
Over the last two decades, the pricing and product mixes from fashion companies have been reviewed in the light of trends such as global sourcing, the increase in the number of collections, increasingly precise customer segmentation, retailization dynamics, internationalization and affirmation of omnichannel logics.
Today, the analysis of large volumes of data, combined with the decisions of store managers on stock allocation, allows a better predictability and accuracy of the system, but also greater engagement of the stores. An analysis by the predictive analytics company Evo Pricing shows how it is possible to profitably apply the logic of machine learning to various typical cases in the fashion world, such as, for example, collection planning, price list determination and management of sales and promotions.