Understanding the Problem
Every month, thousands of user support tickets contained valuable product insights. However, reviewing them manually was slow and difficult to scale.
Limited Coverage
The research team could only analyze around 5% of incoming tickets, leaving many user issues undiscovered.
Time-Intensive Analysis
Processing and categorizing feedback required nearly four weeks of manual work each month.
Inconsistent Reporting
Insights were collected manually, making it difficult to maintain a consistent structure across monthly reports.
Designing an AI-Assisted Research Workflow
Rather than replacing researchers, AI was introduced to automate repetitive tasks while keeping human review at every critical stage.
Automating Data Classification
An AI workflow was designed to organize thousands of support tickets into consistent categories and identify recurring themes.
Human Validation
Every AI-generated output was reviewed and refined before being included in research findings, ensuring accuracy and reducing the risk of incorrect classifications.
Standardizing Reports
The categorized insights were transformed into a clear monthly reporting format that made trends easier to understand and compare over time.
Supporting Product Decisions
The reports highlighted recurring user pain points, helping product, engineering, and business teams prioritize improvements based on real customer feedback.
Business Impact
The new workflow significantly reduced research effort while increasing the amount of feedback that could be analyzed.
- Consistent Monthly Reports Created a repeatable reporting process that provided stakeholders with structured insights and recurring user trends.
- Faster Decision Making Enabled product and engineering teams to identify user problems earlier and respond more quickly with data-informed decisions.
