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USE CASE

Objective

AI ticket management systems with machine learning capabilities can do wonders when it comes to customer service operations. From providing contextual responses to providing a smooth customer service experience AI and ML can help businesses increase CSAT scores, shoot up revenue flow, increase customer onboarding and retention levels, etc.

Outcome

One of the best features of this solution is the speedy remediation process. From quickly pulling past transactions, to providing customers with the best solutions, the ticket management system can get the work done in no time. The fast NLP algorithms have helped solve many practical problems by analyzing text information. Powerful algorithms have been developed to interpret human language and derive predictions. Ensemble models are also well suited to further improve performances as they are able to explore the latent space better to take advantage of features/weights discovered from a group of trained models.

 

Business Value

Trouble ticket handling is a time consuming and tedious process. The support ticket resolution solution can replace the tedious and time-consuming triaging process with intelligent recommendations. The time to resolution is expected to be significantly reduced without multiple ticket reopening and long triaging. Another best is enhanced efficiency and productivity. From minimizing the number of repeat tickets with tools like self-serve portals to efficiently identifying the root cause of the customer service ticket they tend to be very precise, accurate and unlike traditional workflows, they leave no room for error, this not only leads to better operational excellence but also improves overall customer experiences.

H2O's AI and Data Approaches

This solution is powered by the H2O AI Cloud Driverless AI AutoML, and H2O.ai Wave. The data science approaches include regression, NLP, time series, stacked ensembles, and advanced feature engineering.

 

Resources

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