Enhance customer service by understanding and leveraging agent strengths and capabilities.
Identify pods of strength and agent performance groupings
Understand the key factors driving top-performing agents
Optimize training and development strategies
This describes how far ahead in the future this model can be trained to predict. Ideally, the historical data available for training would be 2-3 times longer than the prediction window.
Boost service quality through personalized agent development.
Maintain agent effectiveness with continuous insights and training.
A typical modeling engagement with GlorifAI lasts 3-4 weeks once we get access to the data and proceeds in three distinct phases.
Understanding the underlying factors of employee, store rep or call center agent performance. Offer trainings, adjust compensation and rewards, location benefits, and office freebies.
A range of algorithms will be tested for the best AUC/outcome like XGBoost/GBM, Neural Network, SVM, ANOVA, KNN, K-Means, etc.
At GlorifAI, we prioritize your data privacy by working exclusively on-prem. Our consultants operate either on hardware you provide, such as company laptops, or within virtual machines that you provision in the cloud—ensuring that your data remains entirely within your ecosystem.
We do not egress, transfer, or copy your data to our private company servers or third-parties, so you can trust that your sensitive information stays secure and under your control at all times.
We will contact you within 2 business days to setup a meeting and set the engagement date. Instructions for the required data set will be provided at this time.
The first model payment is invoiced at the data access date. Model delivery usually occurs 3-4 weeks from the data access date. The final model payment is invoiced at the model delivery date.
Deposit fully refundable before engagement date.
If we cannot detect a pattern for a stable model, the final model invoice will be waived and we will provide you with data assessments, findings, insights and further recommendations.