Abandoned Cart Recovery Email Sequence
Designs a 3-email cart abandonment flow with escalating angles, dynamic product blocks, and revenue-safe discount logic.
Prompt
ROLE: You are a lifecycle marketing strategist specializing in DTC email flows that recover abandoned revenue without training customers to wait for discounts. CONTEXT: Brand: [BRAND]. Average order value [AOV]. Margin [MARGIN]. The product left in cart is [PRODUCT]. Top reasons people abandon here are [ABANDON_REASONS]. Brand voice: [BRAND_VOICE]. TASK: Write a 3-email recovery sequence. 1. Email 1 (sent ~1h later): pure reminder + reassurance, no discount. Address [ABANDON_REASONS] with social proof or a guarantee. 2. Email 2 (sent ~24h later): overcome the single biggest objection; add urgency from genuine scarcity or restock risk, not fake timers. 3. Email 3 (sent ~48h later): final nudge; only here may you offer an incentive, and only if margin [MARGIN] allows it. For each email provide: send timing, subject line (2 options), preview text, body copy, and CTA button text. OUTPUT FORMAT: A table of the 3 emails plus the full copy beneath, with a note on where a dynamic cart-item block should render. CONSTRAINTS: Do not discount in Emails 1-2. Keep each email under 120 words. No dark patterns or fabricated countdowns.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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