OKR template to minimize customer impact due to false positives

public-lib · Published 3 months ago

This OKR is aimed at reducing the impact of false positives on customers. To meet this objective, comprehensive training to all customer service staff will be conducted on how to manage such cases. Initiatives for this training include compulsory training sessions, a training module specifically on handling false positives, and tests to gauge the staff's understanding post-training.

A second phase to meet the objective is the implementation of an innovative predictive model purposed for attaining a 90% accuracy rate. To fulfill this, the model will be developed and trained using relevant data, select the most appropriate predictive modeling algorithm, and be constantly tested and fine-tuned to ensure that the specified accuracy is achieved.

The final measure to minimize false positive incidents involves reducing such incidents by 20%. To do this, initiatives include implementing more stringent incident validation protocols, putting an effort to regularly review and revamp the filtering system, and also refining the AI training data for enhanced accuracy.

The success of all these initiatives is quantified through a scoring system, where 100% indicates that the objectives have been completely met. From the inception of these initiatives, the performance is set from a minimum score of 0% to be steadily improved to a maximum of 100%.
  • ObjectiveMinimize customer impact due to false positives
  • Key ResultProvide training to 100% of customer service staff on handling false positives
  • TaskSchedule compulsory training sessions for all customer-service staff
  • TaskDevelop a comprehensive training module on false positives handling
  • TaskDistribute pre-set tests to evaluate understanding post-training
  • Key ResultImplement a new predictive model with 90% accuracy
  • TaskDevelop and train the predictive model using relevant data
  • TaskResearch and select an appropriate predictive modeling algorithm
  • TaskTest and refine the model to achieve 90% accuracy
  • Key ResultDecrease false positive incidents by 20%
  • TaskImplement stricter incident validation protocols
  • TaskRegularly review and update filtering system
  • TaskImprove AI training data for better accuracy
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