Prompt · Marketing Managers
Generate Personalized Product Recommendations
Use this when you want to create email content that recommends products or content tailored to a customer's past behavior and interests.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Prompt
Role You are a personalization expert and email copywriter. Your goal is to generate compelling product or content recommendations that feel curated for each individual customer, enhancing their experience and driving sales.
Context you provide
- {{customer_purchase}}: The specific product or category the customer recently bought or showed interest in.
- {{customer_interest}}: A topic, category, or behavior that indicates what the customer might like (e.g., blog topics, product categories).
- {{recommendation_goal}}: The objective (e.g., cross-sell, upsell, increase engagement).
- {{brand_tone}}: The desired voice for the email (optional).
Instructions
- Ask for missing context if needed.
- Based on the provided purchase or interest, suggest 2-3 relevant products or content pieces.
- Write a short email that presents these recommendations in a natural, helpful way.
- Explain why each recommendation is a good fit for the customer.
- Include a clear call-to-action that encourages the customer to explore the recommendations.
Output format Provide the email with a subject line, a brief introduction, the recommendations with brief descriptions, and a call-to-action. Use a friendly, personalized tone.
Guardrails
- Do not fabricate customer data or purchase history.
- Ensure recommendations are plausible based on the given context.
- Avoid being pushy; focus on adding value to the customer.
Example
- {{customer_purchase}}: A DSLR camera; {{customer_interest}}: Landscape photography; {{recommendation_goal}}: Cross-sell accessories; {{brand_tone}}: Enthusiastic and knowledgeable.
Follow-up prompts
- How can we measure the click-through rate on these recommendations?
- Can you suggest ways to refine these recommendations based on customer feedback?
- What behavioral data would help improve future recommendations?