Analyzing User Sentiment
Analyzing Sentiment in Ticket Resolutions to Identify Patterns
This prompt helps customer support teams create a sentiment analysis process focused on ticket resolution feedback. It identifies patterns in user satisfaction across different resolution types, support channels, and timeframes, providing actionable insights for improvement.
Responsible:
Customer Support
Accountable, Informed or Consulted:
Customer Success
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Collect post-resolution feedback from recent support tickets across all channels.
Define categories for grouping ticket types and resolution methods.
Set up tools for analyzing sentiment data, such as surveys or text analysis software.
THE PROMPT
Help design a sentiment analysis process for ticket resolution feedback in [specific software product or service]. Focus on:
Feedback Collection: Recommending ways to gather user sentiment post-resolution, such as, ‘Send follow-up surveys asking, “How satisfied are you with the resolution provided?” Include an open-ended feedback option.’
Categorizing Sentiment by Resolution Type: Providing strategies for classifying responses, like, ‘Group feedback by categories such as technical fixes, account issues, or feature guidance to identify sentiment trends.’
Analyzing Support Channels: Suggesting ways to track sentiment differences by channel, such as, ‘Compare satisfaction scores across email, live chat, and phone interactions to determine which channels perform best.’
Timeframe Analysis: Offering methods for evaluating sentiment over time, such as, ‘Monitor sentiment trends monthly or quarterly to assess the impact of recent changes in support workflows.’
Deriving Actionable Insights: Proposing steps to act on findings, like, ‘Identify recurring low-sentiment patterns and develop targeted training for agents or improve specific resolution processes.’
Provide a structured analysis workflow that empowers teams to assess sentiment across resolved tickets, enabling focused improvements. If additional details about ticket types or survey methods are needed, ask clarifying questions to refine the process.
Bonus Add-On Prompts
Propose strategies for linking resolution sentiment to key support metrics, like average resolution time or escalation rates.
Suggest methods for integrating ticket sentiment analysis with CRM tools for deeper customer insights.
Highlight techniques for identifying agent-specific trends in sentiment and providing targeted coaching.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on specific types of tickets, like technical issues or account-related inquiries.
Include recommendations for tracking sentiment for high-priority or enterprise customers.
Propose ways to automate sentiment analysis using AI or NLP tools.
Highlight tools like Tableau or Power BI for visualizing sentiment trends across ticket data.
Add suggestions for benchmarking ticket sentiment against industry standards.
WHEN TO USE
To identify trends in user satisfaction related to resolved tickets.
During quarterly or monthly reviews to assess support team performance.
When improving resolution workflows or identifying training opportunities.
WHEN NOT TO USE
For tickets without resolution or ongoing issues that require further follow-up.
If feedback collection is inconsistent or unavailable across resolution types.