  With the rapid growth in the fashion e-commerce industry, it is becoming
extremely challenging for the E-tailers to set an optimal price point for all
the products on the platform. By establishing an optimal price point, they can
maximize overall revenue and profit for the platform. In this paper, we propose
a novel machine learning and optimization technique to find the optimal price
point at an individual product level. It comprises three major components.
Firstly, we use a demand prediction model to predict the next day demand for
each product at a certain discount percentage. Next step, we use the concept of
price elasticity of demand to get the multiple demand values by varying the
discount percentage. Thus we obtain multiple price demand pairs for each
product and we have to choose one of them for the live platform. Typically
fashion e-commerce has millions of products, so there can be many permutations.
Each permutation will assign a unique price point for all the products, which
will sum up to a unique revenue number. To choose the best permutation which
gives maximum revenue, a linear programming optimization technique is used. We
have deployed the above methods in the live production environment and
conducted several AB tests. According to the AB test result, our model is
improving the revenue by 1 percent and gross margin by 0.81 percent.
