Karl Lagerfeld







B2C Sales Forecast

Karl Lagerfeld, an iconic fashion house, sought to enhance and simplify their sales forecasting process. By replacing the current manual forecasting methods, they aimed to improve accuracy and frequency while freeing up valuable time for their team members. The Data Cooks stepped in with an innovative solution based on machine learning (ML).

The challenge

Sales forecasting at Karl Lagerfeld was traditionally carried out quarterly by a dedicated team. This process was not only time-consuming but also prone to human error and limited in frequency. The fashion house needed a way to increase forecasting accuracy and perform these forecasts more frequently without additional burden on their team.

The Solution/Added Value

The Data Cooks introduced a Proof of Concept (POC) utilizing machine learning to automate the sales forecasting process. By analyzing historical sales data and relevant market information, the ML model was able to generate accurate monthly forecasts. This solution provided several key benefits: Improved Frequency and Accuracy: Instead of quarterly updates, sales forecasts could now be generated monthly, allowing for quicker and more responsive planning. Increased Efficiency: Team members who previously conducted manual forecasts were now able to focus on strategic and creative tasks, significantly boosting overall productivity. Data-Driven Decision Making: The automated forecasts provided Karl Lagerfeld with deeper insights and more confidence in their sales strategies, enabling better-informed decisions. This POC demonstrated not only the value of machine learning in business operations but also The Data Cooks’ ability to transform complex data challenges into practical and efficient solutions.

This case study highlights the added value The Data Cooks brings through advanced technologies like machine learning and how these can contribute to more efficient and effective business processes.
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