Automated ETIM Classification

Embracing AI for Enhanced Data Quality
Cebeo, a leading Belgian distributor of electronics to professional installers and companies, is part of the larger French group, Sonepar. With 700 employees in Belgium, Cebeo has always prioritized maintaining high data quality standards. As an organization with an extensive catalogue of technical products, data quality is essential for reporting, product information management (PIM), catalogues, and e-commerce.

Manual, Time-Consuming Data Classification Tasks

Cebeo uses 3,281 of the 5,481 different ETIM (V8) classes for their 1.9 million products, of which 750,000 are active items with sales history. The main challenge faced by Cebeo is the manual, tedious, and time-consuming nature of the product classification task. Product managers have to classify technical products based on limited descriptive information, leading to potential inaccuracies and inconsistencies. Furthermore, confusion arises among some products and ETIM classes, as they sometimes have different representations, resulting in incorrect labeling. For example, the term "Differentieelschakelaar" can refer to two distinct Cebeo product groups. In the last two years, product managers have performed 400 ETIM lookups per month, spending a minimum of 10 minutes per product.


A Scalable Solution for Automated Product Data Classification

To address these challenges, Cebeo requires an effective solution that automates the task of ETIM code classification with high accuracy, minimizes manual workload, and continuously improves the performance of the models over time.


Cebeo and Faktion's Proof of Concept for Data Quality Automation

Cebeo and Faktion initially committed to a proof of concept (POC) to investigate the possibility of automating ETIM classification using Faktion's IDQO toolbox (Intelligent Data Quality Optimization). The focus is on the main ETIM category, with the underlying ETIM features not yet within scope. Faktion received a sample of 54,000 datapoints to test their solution and demonstrate its effectiveness in automating product data classification.



AI-Driven ETIM Classification for Improved Data Quality

Faktion's machine learning models automatically classify ETIM codes for each new article, streamlining Cebeo's product data classification process. By reducing manual effort, this AI-driven approach enhances data quality and accuracy, enabling Cebeo to have detailed business reporting and to better serve their customers.

A Transformative Partnership for Enhanced Product Data Management

Through their collaboration with Faktion, Cebeo has successfully automated their ETIM product data classification process with a minimum accuracy of 97% (F1 score). This has increased efficiency, reduced costs, and allowed product managers to focus on other essential tasks. The improved data quality and product searchability have led to a more positive customer experience, setting new industry standards in electrotechnical product data management.

Expanding the Collaboration to Address New Data Management Challenges

As Cebeo and Faktion continue to build on the success of their partnership, they plan to explore new opportunities for improving data management in the electrotechnical industry. Following the successful classification of the main category, the solution will subsequently be used to predict the underlying ETIM product features. By leveraging Faktion's expertise in AI and machine learning, they aim to tackle other data-related challenges, ensuring that Cebeo remains at the forefront of innovation and continues to deliver exceptional customer experiences.

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