10 ready-to-use AI prompts for fashion ecommerce directors

An Ecommerce Director’s day moves fast: tracking performance, optimizing campaigns, and keeping operations seamless across every market. And as AI becomes part of our daily workflow, AI powered prompts can help make sense of the details—summarizing metrics, spotting issues, and surfacing trends.

But it works best with structured, well-organized data.

In this post, we’re sharing 10 ready-to-use AI prompts for fashion ecommerce to help summarize key metrics, flag potential issues, and highlight trends–each paired with a mini example of the type of data you’d feed an LLM. These prompts can also be adapted to your specific markets, stores, or customer segments.

Ready? Let’s go.

Prompt 1: Daily sales summary

Purpose: Summarize overall revenue and identify trends in product performance.

“Summarize [date’s] total revenue across [number of markets] markets and [number of languages] languages. Highlight the top [number] best-selling SKUs globally, underperforming SKUs per market, and any deviations from [time period, e.g., last week] for items over [price threshold, e.g., £100].”

Data example:

Prompt 2: Abandoned cart recover

Purpose: Monitor abandoned carts and identify potential recovery opportunities.

“List abandoned carts over [amount, e.g., £200] from the last [time period, e.g., 24 hours], including customer segment (VIP, loyalty, first-time buyer). Include suggestions for recovery actions such as follow-up emails or discount offers.”

Data example:

Prompt 3: Inventory & stock alerts

Purpose: Track low stock items and anticipate potential inventory issues.

“Identify SKUs with stock below [number, e.g., 10 units] across [online store] and [number of retail stores, e.g., 10]. Flag products where low stock could affect campaigns, click-and-collect availability, or high-demand seasonal items ([season/collection]).”

Data example:

Prompt 4: High-value customer monitoring

Purpose: Track activity from top-spending customers and identify follow-up opportunities.

"Highlight [customer segment] customers who made purchases between [date] and [date]. Suggest potential follow-ups, upsells, or loyalty rewards based on past purchase history and current product trends (e.g., seasonal collection or top-selling category).”

Data example:

Prompt 5: Returns and complaints insights

Purpose: Monitor returns and customer complaints to identify recurring patterns.

“Summarize returns or complaints logged [date], broken down by SKU, market [specific markets], and customer segment [VIP, loyalty, wholesale]. Highlight recurring issues such as [sizing, shipping, product quality] and note items that may require operational or website adjustments.”

Data example:

Prompt 6: Checkout funnel analysis

Purpose: Identify friction points in the checkout process.

“Analyze [date’s] checkout funnel for drop-offs, failed payments, or other friction points. Highlight markets [specific markets], devices [desktop, mobile, tablet], or payment methods [e.g., credit card, PayPal, Apple Pay] with the highest abandonment rates. Include suggestions for adjustments such as express checkout, click-and-collect, or ship-from-store.”

Data example:

Prompt 7: Campaign performance review

Purpose: Review marketing campaign effectiveness by channel and segment.

“Report yesterday’s campaign results by channel [email, social, paid ads] and customer segment [VIP, loyalty, B2B wholesale, regional market]. Include traffic, conversion, and revenue metrics, and highlight the top-performing campaigns as well as underperforming segments.”

Data example:

Prompt 8: Trending products & insights

Purpose: Track product interest and emerging trends.

“Identify products with unusually high searches, page views, or add-to-cart activity [date]. Highlight trends by market [specific markets] and suggest possible promotions, bundles, or social media posts targeting relevant segments [VIP, loyalty, B2B buyers]”.

Data example:

Prompt 9: Omnichannel coordination

Purpose: Monitor cross-channel order fulfillment and operational performance.

“Summarize [date’s] click-and-collect and ship-from-store orders by store [list stores] and market [list markets]. Flag delays, unfulfilled orders, or operational bottlenecks that could affect the customer experience.”

Data example:

Prompt 10: Website content & functionality audit

Purpose: Ensure product pages and content are accurate and functional.

“Check product pages and CMS content for missing images, broken links, or incorrect product information across all markets [list markets] and languages [list languages]. Prioritize issues affecting high-value SKUs [list SKUs], seasonal collections [season], or products included in active campaigns [campaign names].”

Data example:

Structuring data for AI prompts

To get the most value from your AI search for ecommerce:

1. Keep it tabular and consistent: Each row represents a single order, customer, SKU, or session; each column holds one type of data.

2. Include unique identifiers: e.g., Order ID, Customer ID, Campaign ID.

3. Segment your data: Add columns for Market, Customer Type, Device, or Campaign Segment.

4. Maintain historical context: Include past sales or previous stock levels to detect trends and deviations.

5. Create a brand brain: The more it knows about your brand, goals, and audience, the better its output. Consider creating reusable notes that guide every AI interaction.

6. Clean data first: Ensure SKUs, product names, and labels are consistent and avoid missing values.

AI prompts for smarter, faster insights

If these 10 prompts gave you a taste of what’s possible, you’ll want to check out the full toolkit. It’s packed with 100+ best AI prompts for ecommerce and advanced AI powered prompts for strategy, creative, UX, and customer retention. It’s all there.