Case

Set-up a compelling data workflow for building a global pricing strategy and secure customer loyalty

Background

As a large multinational, it’s a challenge to keep an eye out for pricing inconsistencies between different countries, plants and business units. Differences are typically caused by a mix of different ICT systems with unique processes that are implemented across countries and business units. In areas where “price” is a critical element of the commercial strategy and success, it is key to have a global and consistent view. Only then a global pricing strategy can be implemented and customer loyalty can be secured.

Objectives of the cooperation

CROPLAND implemented result-oriented data wrangling pipes that consolidate pricing data into a global dataset. One of the data wrangling steps was to set-up an automated master data mapping for product information i.e. description, product attributes,… . Specifically for this, our proprietary data matching software, UNITE, was utilised. The product information datasets were then matched and deduplicated with a global product hierarchy master dataset.

Results

As a result of this project, pricing gaps were identified and reduced. Customer loyalty was secured while the company remained profitable.

 

Technology Python I data bricks I data wrangling

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