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Marketing

Online shop conversion optimization

Audits an e-shop and gives concrete conversion-boosting and UX improvement actions.

e-commerceconversion optimizationUXsalescheckout
Prompt
You are a conversion rate optimization (CRO) expert working with online shops. Your task is to find the friction points losing sales and propose concrete fixes.

CONTEXT:
- Shop / niche description: [e.g. clothing e-shop]
- Main products and price range: [e.g. €20–80]
- Current purchase process: [describe steps from product page to payment]
- Payment and delivery methods: [e.g. card, parcel lockers]
- Known problems: [e.g. many abandoned carts, few repeat purchases]
- Main goal: [conversion / average order value / retention]

TASK: run a structured audit from the buyer's perspective.

FORMAT:
1. Product page friction points and fixes
2. Cart and checkout friction points and fixes
3. Missing trust elements (reviews, return policy, contacts)
4. Aspects important to local buyers (parcel-locker delivery, local payment methods, shipping cost and time)
5. 3 A/B test ideas
6. Priority list: top 5 actions ranked by impact and ease of implementation

Justify each recommendation with a short "why". Be concrete and practical, no generic phrases.

Why it matters

Many online shops attract visitors but lose them at the cart or checkout due to small friction. This prompt looks at the shop through the buyer's eyes and delivers a prioritized action list that genuinely lifts conversion.

How to use it

Describe the purchase process, payment and delivery methods in as much detail as possible for sharper recommendations. You can add screenshot descriptions or a link if your AI tool supports it.

Where to use it

  • A clothing shop reduces abandoned carts by improving checkout steps.
  • A small business adds trust elements to lift first-purchase conversion.
  • A shop adapts delivery options to local parcel-locker habits.
  • An owner gets 3 A/B test ideas they can run without a developer.

FAQ

  • Not necessarily. The audit relies on UX logic and buyer behavior, but adding data (e.g. cart abandonment %) makes recommendations sharper.

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