Sora Prompt for Analyzing User Feedback
Analyzing user feedback is a crucial step to improve a product, service, or customer experience. Sora, OpenAI's video generation model, offers an innovative approach to transform textual user feedback data into impactful visual content. By generating videos that illustrate user journeys, friction points, and moments of satisfaction, Sora enables product and UX teams to communicate their analyses in a much more impactful way than a simple written report. Instead of presenting raw data tables, you can create visual scenarios that concretely show what your users experience, making insights immediately understandable by all stakeholders. This visual approach facilitates decision-making in meetings, team alignment, and prioritization of improvements. Whether you work on a mobile app, e-commerce site, or SaaS service, using Sora to stage user feedback transforms abstract data into concrete and memorable visual narratives.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Generate a 30-second video illustrating the following user journey, based on real collected feedback: [PASTE_FEEDBACK_HERE]. The video should show a typical user interacting with the interface, highlighting the 3 main friction points identified in the feedback. Use a clean, professional style with superimposed text annotations summarizing each encountered problem. Alternate between moments of frustration (signaled by a subtle red tint) and moments of satisfaction (green tint). End the video with a summary screen listing the priority improvement areas extracted from the feedback. Visual tone: modern UI/UX design, neutral colors, readable typography. Format: 16:9, suitable for presentation in a product team meeting.
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Why this prompt works
This prompt works because it structures the request in three layers: context (real feedback), staging (user journey with emotional color coding), and final deliverable (actionable summary). The visual color coding leverages Sora's ability to create coherent narrative transitions, while text annotations anchor the video in concrete data. Specifying the format and tone ensures a result directly usable in a professional context.
Use Cases
Variants
Expected Output
You will get a 30-second video showing a user navigating an interface, with key moments annotated corresponding to the analyzed feedback. Friction points are visually identified through color changes and annotations, creating an immediately understandable presentation support for non-technical stakeholders.
Frequently Asked Questions
How should I prepare my user feedback before submitting it to Sora?
Before using Sora, structure your feedback by grouping it by theme (navigation, performance, design, features). Identify the most representative verbatims and rank them by frequency of occurrence. Summarize each group in a clear sentence describing the issue or satisfaction. The more organized and synthesized your feedback is upfront, the more relevant and true-to-life the generated video will be to the user experience. You can use a tool like ChatGPT to pre-process and categorize your raw feedback before passing it to Sora.
Can Sora replace traditional qualitative feedback analysis?
No, Sora doesn't replace qualitative analysis but powerfully complements it. Textual and statistical analysis remains essential for identifying trends, measuring issue frequency, and prioritizing actions. Sora comes in downstream as a communication and storytelling tool: it transforms your analysis conclusions into visual assets that make insights accessible to everyone. Use NLP analysis tools for data processing, then Sora for bringing the results to life.
What types of feedback work best with Sora for visualization?
Feedback describing concrete interface interactions works particularly well: purchase journeys, sign-up processes, menu navigation, using a specific feature. Strong emotional responses (frustration, confusion, satisfaction) also translate very well visually. In contrast, highly abstract or technical feedback (server performance, backend bugs) is harder to illustrate and requires more visual translation work in the prompt.
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