How to make AI judge the severity of after-sales issues

AI can automate the assessment of after-sales issue severity by analyzing customer input and operational data. This enables rapid categorization and prioritization.

Key principles involve applying machine learning algorithms to specific data inputs. Necessary inputs include customer complaint text, issue category, return/repair logs, and historical resolution success rates. Models are trained on labeled datasets where human experts previously defined severity levels. Essential conditions include high-quality, relevant historical data and clear business rules for severity classification. Integration with CRM and ticketing systems is crucial for operationalization.

Implementation involves collecting and labeling historical data, then training and deploying ML models. Steps include defining severity tiers, feature engineering (like sentiment from text), model selection/validation, and integration with workflows. This brings business value by improving prioritization efficiency, reducing handling time for critical issues, enhancing resource allocation, and boosting customer satisfaction through faster resolution of urgent cases.