Customer Retention & Lifetime-Value (LTV) Program Design

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 KSA/Gulf store owners over-reliant on costly acquisition ads: a framework to build a retention program that lifts repeat purchase and lifetime value instead of constant re-acquisition.

Pro Model: claude-sonnet-4-7 2,400 previews 0 uses Knowledge: 2026-Q2
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Use case

Building a loyalty program for a specialty coffee store; reactivating dormant customers for a Salla fashion store; lifting repeat purchase before Ramadan without raising ad spend.

#الاحتفاظ بالعملاء#القيمة الدائمة#RFM#برنامج ولاء#تكرار الشراء

Built-in quality guards

Anti-hallucination

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

Halal-safe

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

KSA/Gulf Context

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

Audience Fit

Messaging and offers fit the defined customer segment and buying behaviour

Completeness

Cover all required stages and channels without skipping an operational step

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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