Customer Complaint Ticket Classification
akira.aiFrequent issues can produce a large volume of customer complaint tickets and misroute them to inappropriate teams. This misallocation contributes to higher MTTR (mean time to resolve) and lower FCR (First Call Resolution), reducing overall support efficiency.
The solution addresses these problems by training a multi-factor machine learning model that evaluates attributes such as ticket impact, urgency, priority, issue description, and other relevant features to identify the most suitable group to handle each ticket. It applies a pool of candidate models to historical and ongoing data, selecting the one that generalizes best for the ticket classification task.
LLM mention score The LLM mention score is the total number of mentions of this brand in different LLM chatbots, normalized to the scale from 0 to 100. You can get actual, non-normalized numbers via the LLM Mention API from DataForSEO.
Normalized 0–100 · last 8 weeks
Get LLM mention data of any company via DataForSEO API
Get access to the structured data on keyword, brand, and website mentions in LLMs, including metrics like AI search volume, impressions, and mentions count.
How to get LLM mention data →// Fetch Customer Complaint Ticket Classification mention score POST /v3/content_analysis/summary { "keyword": "Customer Complaint Ticket Classification", "type": "llm_mentions", "date_from": "2025-01-01" }
