Subscription & Recurring-Revenue Model Design for E-commerce

This is a ready-made prompt from the Zayenha Prompt library in E-Commerce, written for claude-sonnet-4-7. It has a main prompt to copy into the model, built-in quality guards, and a verification prompt to run in a second window to review the answer. The full text needs the Pro plan; the description is open to all.

For repeat-consumable stores (coffee, care, supplements) in KSA: design a Shariah-fair subscription model that grows recurring revenue without gharar or dark patterns in renewal and cancellation.

Pro Model: claude-sonnet-4-7 2,324 previews 0 uses Knowledge: 2026-Q2
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Use case

Launching a monthly coffee subscription; converting a supplements store's customers to auto-renewal; stabilizing cash flow for a care brand via fair commitment plans.

#الاشتراك#الإيراد المتكرّر#الدفع المتكرّر#ضوابط شرعيّة#الاستبقاء

Built-in quality guards

Halal-safe

Avoid riba-based pricing or gharar, and avoid dark patterns in offers and subscriptions

Anti-hallucination

No fabricated sales numbers, conversion rates, or market stats; every figure comes from the user or is flagged as an assumption

KSA/Gulf Context

Account for Salla, Zid, Noon, Amazon.sa, Tabby/Tamara, COD, ZATCA, and PDPL

Completeness

Cover all required stages and channels without skipping an operational step

Audience Fit

Messaging and offers fit the defined customer segment and buying behaviour

Frequently asked questions

How do I use this prompt?

Copy the main prompt and paste it into claude-sonnet-4-7 or the model you use, replace what is in square brackets with your details, then run the verification prompt in a second window with the answer pasted in, and review it before relying on it.

Is this prompt free?

Its full text requires the Pro plan or higher. Free prompts you can copy at once without an account are on the ready-made Arabic prompts page.

Which model was this prompt written for?

It was written for claude-sonnet-4-7 and follows how that model works; you can run it on other models too, reviewing the result, since no single wording guarantees the best result on every model.

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