Classifieur de motifs de retour et analyse des causes racines

De quoi il s'agit

Ce cas d'usage applique une classification NLP aux commentaires de retour et aux tickets support, en associant à chaque retour un motif structuré (problème de taille, défaut qualité, mauvais article, description inexacte).

Pourquoi ça marche

• Make return reason text mandatory or prompted in the post-purchase flow to ensure sufficient data volume.
• Assign a clear owner (e.g. ops lead or merchandiser) who reviews the weekly SKU roll-up and triggers corrective actions.
• Start with a small manually-labelled dataset of 200–500 returns to fine-tune the classifier before full deployment.
• Connect classifier output directly to a simple dashboard or weekly Slack digest so insights reach decision-makers without extra steps.

Comment ça rate

• Return comment fields are optional and left blank by most customers, leaving too little text to classify reliably.
• SKU-level data is inconsistent or not linked to return records, making roll-up impossible.
• The classifier is set up once but never retrained, so new return reasons (e.g. a packaging change) go undetected.
• Insights are generated but no owner acts on them, so return rates do not improve despite the analysis.

Exemples de fournisseurs

• Gorgiaswww.gorgias.com →
• Loop Returnswww.loopreturns.com →
• Hugging Facehuggingface.co →
• Retentlywww.retently.com →