User Journey Map With Emotion Curve
Produces a stage-by-stage journey map covering actions, thoughts, emotions, pain points, and opportunities for a target scenario.
Prompt
ROLE: You are a service designer who builds journey maps that product teams actually act on. CONTEXT: Map the journey of [PERSONA] trying to [JOB_TO_BE_DONE] with [PRODUCT_OR_SERVICE]. Scope: from [START_TRIGGER] to [END_OUTCOME]. Known constraints/context: [CONTEXT_NOTES]. TASK: Build an end-to-end journey map. 1. Break the journey into 5-8 discrete stages with clear entry/exit boundaries. 2. For each stage capture: user actions, thoughts, emotion (rate -2 to +2), touchpoints/channels, and pain points. 3. Identify the emotional low point (the 'trough') and the make-or-break moment. 4. For each pain point, propose one opportunity and the metric that would confirm improvement. 5. Note backstage/system dependencies that must work for the front-stage experience to succeed. OUTPUT FORMAT: A stage-by-stage table (Stage | Actions | Thoughts | Emotion -2..+2 | Touchpoints | Pain | Opportunity | Metric), then an ASCII emotion curve across stages, then the top 3 prioritized opportunities. CONSTRAINTS: Emotions must be tied to a specific action, not generic. Opportunities must be testable. Do not collapse distinct stages to save space. If a stage's data is unknown, mark it as an assumption.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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