Member Outreach Model to Predict Best Time for Communicating with Members
This ML model is used to predict the best time for member outreach to ensure increased member response rate. It identifies key attributes for predicting best time to reach out for member engagement. It uses demographics, SDoH, clinical data and historical outreach data to determine the appropriate timing for a member/customer when they are most likely to respond to an outreach.It is trained on a dataset contained more than 34000 members data. The models shows Accurcy of more than 89%, F1 score of 87%, Recall is 88% and the Precision is 92%.
Input variables : Demographic, SDoH, Clinical data, Historical outreach data
Output Variables : Best time to connect with member (Weekday/ Weekend - Morning/ Afternoon/ Evening)
Metrics to Monitor
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Statistical
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Somers D |
Accuracy |
Precision and Recall |
Confusion Matrix |
F1 Score |
Roc and Auc |
Prevalence |
Detection Rate |
Balanced Accuracy |
Cohen's Kappa |
Concordance |
Gini Coefficent |
KS Statistic |
Youden's J Index
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Business
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Outreach Campaign Efficacy |
Members Engaged
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Infrastructure
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Log Bytes |
Logging/User/IAMPolicy |
Logging/User/VPN |
CPU Utilization |
Memory Usage |
Error Count |
Prediction Count |
Prediction Latencies |
Private Endpoint Prediction Latencies |
Private Endpoint Response Count
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Visit Model :
medictiv.citiustech.com