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Apolla AI
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Complaint analysis

Analyzes complaint records for root causes and trends, identifies systemic issues and high-risk cases, and produces an improvement report.

Sample input and output

Sample input

Analyze all 2,340 complaints from August and find the issues that need priority attention.

Sample output

August complaints rose 18% month-on-month, with 63% of the increase in “delivery time”; the root cause points to the system switchover at the South China warehouse from August 10. 7 additional high-risk cases show signs of escalating to regulatory complaints and are listed separately.

Details

Complaint data is the most direct signal of product and service problems, yet most of it is handled one case at a time. This template clusters complaint text by topic and root cause, identifies systemic issues that spike together, and flags high-risk cases that could escalate into regulatory complaints or public incidents.

Reports are produced weekly or monthly and cover topic distribution, trend changes, root cause classification and improvement recommendations, with each recommendation linked to the responsible product or process owner.

  • Complaint topic clustering and root cause classification
  • Trend and spike detection
  • High-risk case early warning
  • Recommendations linked to the responsible team

How close is this template to your use case?

Most customers go live after changing 20–40% of a template. Book a workshop and we will assess it together.