  In retail vertical industries, businesses are dealing with human limitation
of quickly understanding and adapting to new purchasing behaviors. Moreover,
retail businesses need to overcome the human limitation of properly managing a
massive selection of products/brands/categories. These limitations lead to
deficiencies from both commercial (e.g. loss of sales, decrease in customer
satisfaction) and operational perspective (e.g. out-of-stock, over-stock). In
this paper, we propose a pipeline approach based on Natural Language
Understanding, for recommending the most suitable replacements for products
that are out-of-stock. Moreover, we will propose a solution for managing
products that were newly introduced in a retailer's portfolio with almost no
transactional history. This solution will help businesses: automatically assign
the new products to the right category; recommend complementary products for
cross-sell from day 1; perform sales predictions even with almost no
transactional history. Finally, the vector space model resulted by applying the
pipeline presented in this paper is directly used as semantic information in
deep learning-based demand forecasting solutions, leading to more accurate
predictions. The whole research and experimentation process have been done
using real-life private transactional data, however the source code is
available on https://github.com/Lummetry/ProVe

Computation and Language;Machine Learning