Analyzing User Sentiment
Building a Sentiment Analysis Framework for Post-Support Surveys
This prompt helps customer support teams create a framework for analyzing sentiment from post-support surveys. It focuses on identifying patterns in customer feedback, extracting actionable insights, and aligning results with support improvement initiatives.
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:
Design and distribute post-support surveys to capture actionable feedback.
Collect historical survey responses for analysis.
Identify sentiment analysis tools compatible with survey platforms like SurveyMonkey or Typeform.
THE PROMPT
Help create a sentiment analysis framework for post-support surveys from users of [specific software product or service]. Focus on:
Survey Design: Recommending key questions to capture sentiment, such as, ‘How satisfied were you with the resolution provided?’ or ‘What could we improve about your experience?’
Sentiment Categorization: Outlining methods for classifying feedback, like, ‘Use text analysis tools to tag responses as positive, neutral, or negative, and group comments into categories like resolution time or agent professionalism.’
Tracking Sentiment Trends: Providing strategies for monitoring changes, such as, ‘Create dashboards to visualize sentiment by week, support channel, or agent performance.’
Extracting Insights: Suggesting ways to act on feedback, like, ‘Identify recurring themes in negative sentiment and collaborate with the training team to address them.’
Linking Sentiment to Outcomes: Recommending metrics to measure impact, such as, ‘Correlate sentiment scores with repeat support requests, churn rates, or CSAT scores to assess long-term effects.’
Provide a structured framework that guides teams in analyzing survey sentiment effectively, ensuring results lead to actionable support improvements. If additional details about survey data or customer feedback goals are needed, ask clarifying questions to refine the framework.
Bonus Add-On Prompts
Propose strategies for integrating survey sentiment data with CRM systems for deeper customer insights.
Suggest methods for identifying agents or teams that consistently drive positive sentiment.
Highlight techniques for automating survey analysis to quickly address emerging issues.
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 support channels, such as phone, chat, or email.
Include tips for crafting open-ended questions that encourage detailed responses.
Propose ways to integrate sentiment data into team performance reviews.
Highlight tools for visualizing survey insights, such as Tableau or Google Data Studio.
Add suggestions for incorporating sentiment insights into agent training programs.
WHEN TO USE
To identify patterns in sentiment from post-support surveys.
When aligning survey insights with broader support improvement initiatives.
To track the impact of new support strategies on customer satisfaction.
WHEN NOT TO USE
If survey response rates are too low to provide meaningful insights.
For analyzing feedback from informal or unstructured survey formats.