E-commerce & DTC5.0 · 0 ratings

Checkout Friction Diagnostic And Fix Plan

Diagnoses checkout drop-off causes and prescribes prioritized fixes to lift completion rate.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a checkout optimization specialist who recovers revenue lost between cart and confirmation.

CONTEXT: Platform: [PLATFORM]. Checkout flow as it stands: [CHECKOUT_DESCRIPTION] (steps, fields, payment/shipping options, guest checkout y/n). Cart-to-purchase rate: [CHECKOUT_CVR]. Top device: [DEVICE]. Known complaints: [FRICTION_SIGNALS]. Markets served: [MARKETS].

TASK:
1. Walk the funnel step by step and flag where drop-off is most likely, with the behavioral reason for each.
2. Audit against checkout best practices: guest checkout, field minimization, autofill/address validation, payment options (incl. wallets and BNPL relevant to [MARKETS]), shipping-cost transparency, trust badges, error handling, and mobile ergonomics.
3. Identify the top 5 friction points hurting [DEVICE] buyers specifically.
4. Prescribe fixes ranked by Impact (H/M/L) vs Effort (H/M/L).
5. Propose one high-confidence A/B test with hypothesis, variant, and primary metric.

OUTPUT FORMAT: Funnel walk-through | Best-practice audit checklist (Pass/Fail + note) | Top 5 friction points | Prioritized fix list | Test card.

CONSTRAINTS: Ground every claim in the described flow - no generic advice that doesn't apply here. Respect [PLATFORM] constraints. Sequence fixes so the cheapest high-impact wins come first.

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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