Metrics For Bill Pay Search

To evaluate the effectiveness of the Bill Pay search functionality and ensure that users can find vendors quickly with minimal keystrokes, several key metrics can be employed:

1. Search Response Time:

   • Definition: The time taken from when a user starts typing to when the search results are displayed.

   • Goal: Ensure that search results are displayed almost instantaneously, ideally within milliseconds.

2. Search Accuracy:

   • Definition: The relevance and correctness of the search results based on the user’s input.

   • Goal: The top results should match the intended vendor accurately.

3. Average Keystrokes to Find Vendor:

   • Definition: The average number of keystrokes users need to type before finding the correct vendor.

   • Goal: Minimize the number of keystrokes required, aiming for users to find the correct vendor within 3-5 keystrokes.

4. Search Completion Rate:

   • Definition: The percentage of searches that result in the user successfully finding and selecting the correct vendor.

   • Goal: Achieve a high completion rate, indicating that users can efficiently find their desired payees.

5. Search Abandonment Rate:

   • Definition: The percentage of searches where users abandon the search without selecting a vendor.

   • Goal: Reduce the abandonment rate, suggesting that users are not frustrated by the search process.

6. User Satisfaction:

   • Definition: User feedback and ratings on the ease of finding vendors through the search functionality.

   • Goal: Maintain high satisfaction scores through surveys and feedback forms.

7. Error Rate:

   • Definition: The rate at which users encounter errors or issues during the search process (e.g., no results found, incorrect results).

   • Goal: Keep the error rate to a minimum, ensuring that the system works reliably.

8. Auto-Completion Effectiveness:

   • Definition: The effectiveness and accuracy of the auto-complete suggestions provided as users type.

   • Goal: Ensure that auto-complete suggestions are relevant and reduce the number of keystrokes needed.

9. Search Frequency:

   • Definition: The number of searches performed within a specific timeframe.

   • Goal: Monitor this to understand usage patterns and identify peak times for search activity.

10. Time to First Interaction:

    • Definition: The time it takes for a user to interact with the search results (e.g., clicking on a vendor) after starting the search.

    • Goal: Shorten this time to indicate a more efficient search process.

11. Search Conversion Rate:

    • Definition: The percentage of searches that lead to a completed transaction (e.g., bill payment initiated).

    • Goal: Higher conversion rates indicate that users are finding the correct vendors and proceeding with their transactions.

12. Heatmaps and Click Analysis:

    • Definition: Visual representations of where users click within the search results.

    • Goal: Identify patterns and optimize the layout and positioning of search results to enhance usability.

Technical Metrics for ML Model

To evaluate the performance of the AI model used for search functionality in Bill Pay, several technical metrics should be employed. These metrics help ensure that the model is efficient, accurate, and provides a good user experience. Here are some key technical metrics:

1. Precision:

   • Definition: The ratio of relevant results returned by the model to the total results returned.

   • Goal: High precision indicates that the model is returning mostly relevant vendors.

2. Recall:

   • Definition: The ratio of relevant results returned by the model to the total relevant results available.

   • Goal: High recall ensures that the model is capturing as many relevant vendors as possible.

3. F1 Score:

   • Definition: The harmonic mean of precision and recall.

   • Goal: A balanced F1 score indicates that both precision and recall are high.

4. Mean Reciprocal Rank (MRR):

   • Definition: The average of the reciprocal ranks of the first relevant result for each query.

   • Goal: High MRR indicates that relevant vendors are appearing near the top of the search results.

5. NDCG (Normalized Discounted Cumulative Gain):

   • Definition: A measure of ranking quality that considers the position of relevant results.

   • Goal: High NDCG means that the most relevant results are ranked higher in the search results.

6. Mean Average Precision (MAP):

   • Definition: The mean of the average precision scores for each query.

   • Goal: High MAP indicates that the model consistently returns relevant results across different queries.

7. Latency:

   • Definition: The time taken for the model to return search results after a query is made.

   • Goal: Low latency ensures a fast and responsive user experience.

8. Query Understanding Accuracy:

   • Definition: The accuracy with which the model interprets the user's query and intent.

   • Goal: High accuracy ensures that the model correctly understands and processes user queries.

9. Error Rate:

   • Definition: The percentage of incorrect or failed search results.

   • Goal: Low error rate indicates a reliable and robust search model.

10. Coverage:

    • Definition: The proportion of vendors that the model can accurately identify and return in search results.

    • Goal: High coverage ensures that the model can handle a wide range of vendors.

Other Metrics

11. Learning Rate:

    • Definition: The rate at which the model improves its performance over time through continuous learning and updates.

    • Goal: An optimal learning rate ensures that the model adapts and improves without overfitting or underfitting.

12. A/B Testing Results:

    • Definition: Comparative performance of different versions of the model or algorithm.

    • Goal: Use A/B testing to identify and implement the most effective model version.

13. User Feedback Loop Integration:

    • Definition: The extent to which user feedback is integrated into the model for continuous improvement.

    • Goal: Effective integration of user feedback helps refine and enhance the model over time.

14. Scalability:

    • Definition: The model's ability to handle increasing amounts of data and user queries without degradation in performance.

    • Goal: Ensure the model can scale efficiently to accommodate growth in users and data.

15. Robustness:

    • Definition: The model's ability to maintain performance despite variations and anomalies in input data.

    • Goal: A robust model performs well under diverse and challenging conditions.

By monitoring and optimizing these technical metrics, Chase can ensure that its AI model for Bill Pay search is efficient, accurate, and provides an excellent user experience.