OKR template to enhance global issue feedback classification accuracy and coverage
The OKR aims at enhancing the classification accuracy and coverage of global issue feedback. The goal is to rectify the problem of misclassification of feedback and minimize the errors by at least 25% while increasing overall efficiency of the process.
The initiatives taken to reach these objectives include equipping staff with best practices in feedback classification and improving an automated system for classification. Moreover, thorough analysis and identification of patterns in previously misclassified cases will be carried out.
Another major goal is improving feedback classification through machine learning model. Steps like introduction of a more complex algorithm, enhancement of training dataset through data augmentation and optimization of hyperparameters will be undertaken. This aims at increasing the classification accuracy by 30%.
The OKR also focuses on expanding feedback coverage to include 20 new globally-relevant issues. This will be achieved by identifying these issues, developing a comprehensive feedback form for each one of them, and then rolling out these feedback tools across all platforms.
The initiatives taken to reach these objectives include equipping staff with best practices in feedback classification and improving an automated system for classification. Moreover, thorough analysis and identification of patterns in previously misclassified cases will be carried out.
Another major goal is improving feedback classification through machine learning model. Steps like introduction of a more complex algorithm, enhancement of training dataset through data augmentation and optimization of hyperparameters will be undertaken. This aims at increasing the classification accuracy by 30%.
The OKR also focuses on expanding feedback coverage to include 20 new globally-relevant issues. This will be achieved by identifying these issues, developing a comprehensive feedback form for each one of them, and then rolling out these feedback tools across all platforms.
Enhance global issue feedback classification accuracy and coverage
Reduce incorrect feedback classification cases by at least 25%
Train staff on best practices in feedback classification- Implement and continuously improve an automated classification system
- Analyze and identify patterns in previous misclassifications
- Improve machine learning model accuracy for feedback classification by 30%
- Introduce a more complex, suitable algorithm or ensemble methods
- Implement data augmentation to enhance the training dataset
- Optimize hyperparameters using GridSearchCV or RandomizedSearchCV
- Expand feedback coverage to include 20 new globally-relevant issues
- Identify 20 globally-relevant issues requiring feedback
- Develop a comprehensive feedback form for each issue
- Roll out feedback tools across all platforms
