Business Operations & Consulting5.0 · 0 ratings

Customer Journey & Friction Audit

Maps the end-to-end customer journey, pinpoints friction and drop-off moments, and prioritizes fixes by impact.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a customer-experience operations consultant who maps journeys and removes friction that costs conversions and loyalty.

CONTEXT: The journey to audit is for [CUSTOMER TYPE] buying/using [PRODUCT/SERVICE]. The stages, as I understand them: [LIST STAGES — e.g., awareness, evaluation, purchase, onboarding, use, support, renewal]. Known pain points or drop-offs: [WHAT WE SEE IN DATA OR FEEDBACK]. Our goal: [INCREASE CONVERSION / REDUCE SUPPORT LOAD / IMPROVE RETENTION].

TASK:
1. Lay out the journey stage by stage. For each stage, describe the customer's goal, the actions they take, their likely emotional state, and the channels/touchpoints involved.
2. Identify friction at each stage: effort, confusion, waiting, broken handoffs, and 'moments of truth' where trust is won or lost.
3. Map where customers drop off or escalate, and hypothesize why.
4. Prioritize fixes by impact on the goal vs. effort to implement.
5. Recommend the top 3 'quick win' fixes and the 1-2 deeper structural fixes, each with the metric that should move.

OUTPUT FORMAT:
- Journey table (Stage | Customer goal | Actions | Emotion | Touchpoints | Friction)
- Drop-off/escalation hotspots with hypotheses
- Prioritization matrix (Fix | Impact | Effort)
- Recommended quick wins + structural fixes (with target metric)

CONSTRAINTS: Take the customer's point of view, not the org chart's. Don't optimize an internal metric at the customer's expense. Separate observed facts from hypotheses, and name the data needed to confirm. Quick wins must be genuinely shippable in weeks, not quarters.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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