skills/aes-cart-abandonment-analyzer/SKILL.md
Identify reasons for cart abandonment and build multi-touch recovery sequences across email, SMS, and push.
npx skillsauth add leoyeai/openclaw-master-skills aes-cart-abandonment-analyzerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Cart abandonment is one of the most expensive leaks in any ecommerce funnel — average abandonment rates hover around 70%, meaning seven out of ten shoppers who add items to their cart leave without purchasing. This skill diagnoses the likely causes of cart abandonment for your specific store and product mix, then builds tailored multi-touch recovery sequences across email, SMS, and push notification channels to win back lost revenue systematically.
This skill takes your store details, product category, average order value, and current abandonment data to perform a structured root-cause analysis of why shoppers are leaving. It examines pricing friction, shipping cost surprises, checkout complexity, trust gaps, payment method limitations, and mobile experience issues. Based on the diagnosis, it generates a complete multi-channel recovery sequence with specific message copy for each touchpoint, recommended send timing relative to the abandonment event, subject lines and preview text for emails, SMS message templates within character limits, and push notification copy. The sequence includes an incentive escalation ladder that starts with reminders and progressively introduces discounts or free shipping offers.
The output begins with a Root-Cause Diagnosis section that identifies the three to five most likely abandonment drivers for this specific store and product type, with reasoning for each. Next comes the Recovery Sequence Blueprint — a timeline-based plan showing each touchpoint across all channels, with exact timing relative to the cart abandonment event. For each touchpoint, the output provides the channel, send time, subject line or message hook, full message body copy, CTA text and destination, and any incentive offered. The output also includes a Segmentation Guide explaining how to split recovery flows by cart value, product type, and customer status (new versus returning). Finally, a Performance Benchmarks section sets realistic open rate, click rate, and recovery rate targets for each message in the sequence.
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