Difficulty To Execute
Easy
Goal
To analyse and optimise your abandoned cart recovery email sequence so you can recover more lost sales and generate higher revenue. This prompt is specifically for abandoned cart situations, where a customer adds products to their basket but leaves without visiting checkout. It will not analyse abandoned checkout or browse abandonment sequences.
By entering your data into this prompt, the AI will:
- Identify weaknesses in your current abandoned cart email flow
- Recommend a revised sequence structure and timing
- Suggest high-converting subject lines and CTAs
- Improve personalisation tactics
- Advise on incentive strategies (discounts, free shipping, bundles)
- Suggest segmentation tweaks to target the right audience
- Provide actionable A/B testing ideas to lift recovery rates
Full Prompt Description / Instructions
Paste the following prompt into ChatGPT (or another LLM) and replace the placeholders with your own information. This is specifically for abandoned cart recovery, meaning a customer added items to their cart but left without going to checkout. Do not use this for abandoned checkout or browse abandonment analysis.
The Prompt To Use
You are an ecommerce email conversion specialist. I want you to analyse my abandoned cart recovery email sequence and recommend high-impact improvements that could increase recovery rate and revenue. This is not for abandoned checkout or browse abandonment, only for customers who have added items to their cart but have not progressed to checkout.
Here is my data:
- Store Platform: [Shopify / Magento / WooCommerce / etc.]
- Email Platform: [Klaviyo / Mailchimp / Omnisend / etc.]
- Sequence Structure: Number of emails in the abandoned cart series, and timing of each send.
- Email Copy & Creative: Paste or summarise the current copy, subject lines, and images used in each email.
- Offer/Incentives: Any discounts, free shipping offers, or bonuses included (and at which stage).
- Segmentation Rules: Who receives the sequence (e.g., all customers, repeat customers, minimum cart value).
Metrics: For each email in the sequence, provide:
- Open rate (%)
- Click-through rate (%)
- Conversion rate (%) (sales attributed to that email)
- Revenue generated (£)
- Unsubscribe rate (%)
- Lookback Window: How long after cart abandonment the first email is sent, and the total time span of the sequence.
- Top Abandoned Products: Which products or categories most frequently appear in abandoned carts.
- Audience Location(s): Country or region of the majority of customers.
- Brand Tone & Voice: How you would describe your style (e.g., formal, playful, luxury).
- Goals: Your priority (e.g., maximum revenue, higher recovery rate, brand experience, list hygiene).
My request:
Analyse my data, identify weaknesses, and suggest:
- A revised email sequence structure and timing
- High-converting subject lines
- Personalisation tactics
- Creative ideas for images and CTAs
- Stronger incentive strategy (if relevant)
- Segmentation improvements
- Tests I should run over the next 30 days to optimise performance
Example Output
Analysis:
Your current abandoned cart sequence has a slow first touchpoint (first email at 6 hours). Industry best practice is to send the first email within 30–60 minutes to capture urgency. Subject lines are functional but lack curiosity and personalisation. Conversion rates drop sharply after email 1, suggesting later emails aren’t adding new value.
Recommended New Sequence:
- Email 1 – Sent at 45 mins: “Still thinking it over? Your [Product Name] is waiting” — Include cart image, key benefit, and strong CTA.
- Email 2 – Sent at 12 hours: Add social proof (reviews, trust badges) and a subtle urgency hook.
- Email 3 – Sent at 24 hours: Introduce an incentive (e.g., 10% off or free shipping) — clearly state it expires in 24 hours.
- Incentive Strategy: Delay discount until email 3 to maximise margin recovery from early conversions.
Segmentation Improvements: Exclude customers with carts under £15 to reduce wasted sends.
Personalisation Tactics: Add first name to subject line, dynamic product images from their cart, and related item recommendations.
A/B Tests for Next 30 Days:
- Test “benefit-led” vs “curiosity-led” subject lines
- Test hero image with vs without human model
- Test discount type: % off vs £ amount off
- Expected Impact: With improved timing, targeted incentives, and stronger personalisation, recovery rates could rise from your current 7.2% to 11–14%, generating an estimated additional £3,200–£4,800 per month.