Abstract
Clusters of similar customer queries can arise at certain times in the lifecycle of a product or service, e.g., during a software release, during a service outage, etc. At such times, customer support can experience challenges in efficiently escalating emerging issues reported by customers. This disclosure describes techniques that enable rapid and automated categorization of customer issues for a product or service. Using the described techniques, clusters of similar issues can be recognized and the issue escalated as necessary. Machine learning (e.g., large language models) or other categorization techniques are applied to categorize reported customer issues. Categories are ranked into tiers based on respective frequencies of occurrence. For example, categories of relatively rare complaints rank higher than categories of relatively common complaints. Since rare complaints are likely a sign of emerging issues, the rarer a category, the quicker its escalation to higher support levels. Conversely, the more common a category of complaints, the greater the number of complaints needed for escalation to higher support levels.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Heath, Taliver; Zha, Charles; and Wait, Arthur, "Using Baseline Feedback Rates to Dynamically Adjust Customer Service Alert Thresholds", Technical Disclosure Commons, (November 28, 2024)
https://www.tdcommons.org/dpubs_series/7603