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After 10 years of using a specific AI set-up and technology, Proximus asked element61 to review and help to select a new AI workbench.

This project supported Proximus in building its 10-year target architecture exercise and define & launch a set of improvement projects on AdTech and MarTech technology including an AI Workbench and a Customer Data Platform.

In order to plan ressources more effectively and efficiently, AZ Alma wanted to look in Machine Learning to predict and estimate the demand for IPT.

Mazda asked element61 to help assess the feasibility and set-up of a propensity model to predict renewal for existing customers.

BNP Paribas Fortis asked element61 to assist them in becoming more data-driven in marketing with a specific focus on the activation and adoption of DMP usage.

Today, the team of AZ Sint-Blasius manually has to label patient files with ICD-10 classification. To automate and gain time, AZ Sint-Blasius has asked element61 to evaluate the use of NLP and machine learning to automatically label patient files.

BRU required that the consolidation process could be performed on a monthly basis. To support these reporting requirements, BRU implemented SAP BPC version 10.0 NW.

In a push to reduce the number of folders sent and to determine the target audiences efficiently, element61 ran a project to induce and leverage machine learning: more specifically, a clustering algorithm to continuously help us divide customers into different target groups.