Ecommerce — CMO & Ecommerce Manager
Prompts crafted for online stores that need to grow revenue, defend margin, and understand customer behavior with clean, unmodeled data.
MCPs required: SealMetrics MCP Best for: CMOs and Ecommerce Managers
SEAL-056 — Cart-to-purchase ratio by category
Using SealMetrics MCP for site {site_id}, for the last 30 days:
For every product category (property `category` on add_to_cart and on conversion_items):
- Add-to-cart events, purchases, cart-to-purchase ratio, % vs site average.
Rank categories by absolute leak. Highlight the top 5 worst.
For each, suggest 2 likely causes (price, shipping, stock, payment friction) and what data point I should pull next to confirm.
Output: 1 main table + a "deep dive next" section.
SEAL-057 — Discount code attribution
For site {site_id}, query SealMetrics MCP for the last 90 days.
For every utm_campaign that includes a discount code (property `discount_code` present on conversion):
- Conversions with discount, conversions without discount on same campaign (if applicable), revenue with vs without, average discount, estimated margin impact.
Highlight campaigns where discount usage does NOT correlate with higher CR — those are margin killers.
End with a 3-bullet recommendation: which discounts to keep, which to retire, which to test smaller.
SEAL-058 — Worst product detail pages
Using SealMetrics MCP for site {site_id}, for the last 30 days:
Filter to product detail pages (URL pattern: {pdp_url_pattern} or content group `product_detail`) with more than 500 visits per month.
For each: URL, pageviews, entrances, add_to_cart, purchases, CR, bounce rate, average time on page.
Identify the 10 worst by combined low CR + low add_to_cart. For each, propose a likely cause (price, photos, stock, copy, reviews).
Output: 1 ranking table + 10 hypothesis cards.
SEAL-059 — Returning vs new customer revenue
For site {site_id}, query SealMetrics MCP for the last 90 days.
Use the micro-conversion `login` or `account_returning` (or whichever property identifies a returning customer in this site) to split sessions into new vs returning.
Per segment: sessions, conversions, CR, revenue, AOV, % share of revenue.
Compare also by traffic source: which channels bring more returning customers vs new ones.
End with a 3-bullet recommendation: how to allocate budget between acquisition and retention based on margin contribution.
SEAL-060 — Top categories by revenue and CR
Using SealMetrics MCP for site {site_id}, for the last 90 days, cross content groups with conversion_items by category.
Per category: entrances to category landing pages, conversions, CR, revenue, AOV, % share of total revenue.
Output: 1 main table sorted by revenue. Highlight the top 5 by revenue and the top 5 by CR (those lists may not overlap).
End with 3 takeaways: which category to push organically, which to push paid, which to deprioritize.