This case study talks about the Pricing solution for a giant European retailer with the goal of revolutionising the pricing. Its vision is to provide the right price at the right time and the right place to the right customer.

We partnered on enabling the retailer to price more than 40,000 articles; for 25 countries to build better customer price perception, engage more customers and generate uplift on margin and revenue.We also focused on price discrimination strategies guided by customer centricity and zonal difference.

This retailer, by virtue of its size and geographic spread, collects huge amount of sales and customer data in all countries it is present in. However, with the heterogeneity of pricing processes across countries, there is significant variance in quality (vs. quantity) of data. Combined with its business models of both B2B and B2C, and the presence of established competitors in every country, made Pricing a very interesting challenge.

The vast assortment of retailers necessitates employing multiple pricing strategies depending upon certain attributes of the products. The pricing solution was aimed at providing a uniform yet flexible way which would enable the retailer to do price optimization and provide a seamless and transparent way to price their products with confidence across geography, product assortment and model of operations. This talk will explore the pricing landscape which is an integration of 3 domains viz. Retail, Data Science & Economics. We will highlight the type of models in each domain relevant to pricing & dwell deeper into some of the important ones which will have maximum impact on a company's pricing strategy. I will discuss the typical data issues faced, data based identification of various product groups, such as KVIs (Key Value Items), the type of models used - machine learning & statistical models, inferences, Key Performance Indicators & processes involved in the validation of the solution. We were also able to show the reason and impact of the prices recommended by the algorithm which differentiated it from competitors in the market.

I will walk through how the solution evolved on tech stack for the data pipelines to accommodate multiple evolving models, as well to scale up (more products) and scale out ( multiple geographies).