Performance Marketing Audit for Fashion E-commerce: 32-Point Checklist

New Arrivals, Seasonal Collections, Core Collection, Carryover, Sale, and Outlet play different commercial roles. We check how products are separated by life cycle, arrival and end-of-sale dates, Advertising Spend, Revenue, ROAS, and Contribution Margin by collection, Full-Price Sell-Through, the share of old collections in the budget, and automatic updating of Product Sets. For example, PMax can keep optimizing toward last year's products that have historically converted, leaving new collections without enough traffic. Audit result: a Collection Lifecycle Map with recommended promotion priorities and budget limits.

Total stock does not reflect product availability for the core audience. A dress can be listed as In Stock even though sizes M and L have sold out. We analyze sales and stock by size/color for each style, Size Curve by country, popular variants, Inventory Data updates, ad traffic to incomplete size ranges, and Back-in-Stock Demand. We separately check US/UK/EU Size Conversion. Half of the remaining sizes does not equal half of the potential sales: the structure of demand matters. Result: a Size Availability Report and Inventory-Aware Advertising that accounts for customer demand.

We check units received and sold, Sell-Through Rate by SKU/style/color, Stock Aging, Weeks of Supply, sales after a collection launch, stock before the end of the season, advertising Spend, and Sell-Through. A good ROAS does not guarantee that the planned assortment will sell through by the end of the season. Sometimes a moderate ROAS for a Clearance campaign is commercially justified if the alternative is storage and a deeper discount. Result: a Stock Aging & Sell-Through Report with separate priorities for New Arrivals, Core, and Clearance.

We assess Discount Depth by collection, promo codes, Full-Price/Discounted Sales, Gross Margin after discounts, Stackable Discounts, Merchant Center Promotions, Promotional Creatives on Meta/TikTok/Pinterest, and Contribution Profit. A 30% discount can raise the Conversion Rate but reduce profit per order more than the growth in volume makes up for. We analyze not only ROAS but also product cost, advertising, and promotional terms. Result: a Markdown Performance Report and a Promotional Calendar.

We check Return Rate by style, category, size, and color, return reasons, Refund Amount, Exchange Rate, return shipping costs, time to return, the link to the traffic source, and Net Revenue after Refunds. Sales with a high advertising ROAS can turn out to be unprofitable after returns. Return reasons need to be compared against size charts, Product Data, photos, and the promises made in ads. Result: a Return-Adjusted Profitability Report and a list of categories to fix.

In Fashion, demand changes by season, style, cut, material, and color. We check Google Trends, search queries, sales history, collection launch dates, seasonal CPC/CPA, geography, fabric and silhouette trends, and Black Friday preparation. For example, demand for linen dresses and winter outerwear develops on different cycles. Social interest in a trend does not equal guaranteed sales. Result: a Fashion Demand Calendar linked to purchasing and ad launches.

In search, a user may specify a brand, cut, color, material, fit, and occasion: Women's Blazer, Oversized Black Blazer, Linen Blazer, Wedding Guest Outfit. We check Brand/Non-Brand, Category, Style, Material/Color/Fit, Occasion-Based, and Competitor Queries, Ad Copy, Landing Page Relevance, and Negative Keywords. For the query Linen Summer Dress, it is better to send traffic to a relevant selection rather than the general Dresses Category, if possible. For PMax, we analyze the available search insights and Landing Pages. Result: a Fashion Search Intent Map.

Competitors differ in how quickly they refresh their assortment, Drops, discounts, and positioning. We check new collections, the size and frequency of discounts, Free Shipping/Return Offers, exclusive styles, Creative Angles, and Promotional Landing Pages. Semrush/SpyFu are suitable for Paid Search; Foreplay, BigSpy, and PiPiADS for Meta/TikTok; Similarweb, Pathmatics, and AdClarity for the media mix. How long competitors' ads have been running does not confirm their ROAS. Result: a Competitive Fashion Merchandising & Advertising Matrix.

A style with six sizes and four colors can have 24 Product Variants. We check id, item_group_id, color, size, gender, age_group, size_system, size_type (where applicable), Brand/GTIN, Titles, Material/Pattern, Images, Price/Availability, and the consistency of the selected variant on the Landing Page. We pay attention to Petite, Tall, Plus Size, Oversized, and local size systems. Channable, DataFeedWatch, Feedonomics, and Productsup help with Feed Mapping and Channel-Specific Rules. Result: Apparel Feed Diagnostics.

We check Search/Shopping/PMax, Brand and Non-Brand, Product Filters, Listing Groups, Asset Groups, budgets, tROAS, New Customer Acquisition Goals, Promotion Assets, and Search Query Relevance. We look at whether an old sale is getting excess budget at the expense of a new collection. Creating a separate campaign for each collection without considering Conversion Volume, budget, and automated learning is not advisable. Result: a Google Ads Structure & Collection Prioritization Plan.

We assess the main image, color accuracy, on-model photos, product shots, additional angles, Title/Description, variant consistency, and the Product Page. Changes should be checked for compliance with Merchant Center policies, including restrictions on promotional overlays in main images. Feed Management Systems help normalize images and manage Channel-Specific Image URLs. Result: a Product Image & Feed Optimization Backlog.

We check Spend by collection/SKU, Stock Depth, Size Availability, Aging, Gross Margin, Sell-Through, Revenue per Item, and Share of Spend on Discounted Products. Two dresses with the same ROAS may require different strategies: a new style with a full size range and leftover stock from an old collection have different goals. Custom Labels and Product Segmentation make it possible to integrate Merchandising Logic into Shopping. Result: a budget management framework that accounts for availability, collection, and profit.

We check Shopify Integration, Catalog Diagnostics, Product Approval, IDs and Variants, Price/Availability, Product Sets, Product Images, Meta Pixel/CAPI, Content IDs, ViewContent/AddToCart/Purchase, and Deduplication. Product Sets can separate New Arrivals, Dresses, Outerwear, and Sale; they should not lose available variants or break the match between events and the catalog. Result: a Meta Commerce Readiness Report and Catalog Segmentation.

We check the TikTok Catalog, product quality, the link between videos and SKUs, Approval, Price/Availability, IDs/Sets, TikTok Pixel/Events API, available Smart+ Catalog Ads, and Creative Performance. A video featuring several outfits can be accompanied by product links. We assess whether Creators fit the brand, ad disclosure labeling, and UGC rights. For feeds, we consider Channable, DataFeedWatch, and Feedonomics where supported by TikTok and the region. Result: a TikTok Catalog & Creative Audit.

Pinterest lets users explore clothing, outfits, and accessories through a visual journey. We check the Pinterest Catalog, Product Groups, Titles/Descriptions, Images, Price/Availability, Shopping Campaigns, Pinterest Tag/CAPI, clicks through to Product Pages, and category Performance. A linen collection can be presented as ready-made summer outfits, not only as photos of individual items. For feeds, Productsup, Channable, Feedonomics, and DataFeedWatch are suitable with the necessary integrations. Result: Pinterest Shopping & Visual Discovery Recommendations.

We check Editorial Photography, Lookbooks, Lifestyle Video, UGC, Creator Content, Try-On, Fabric/Fit Demonstrations, Collection Launch and Promotional Creatives, Creative Fatigue, and testing frequency. A creative should lead to the relevant outfit or product; otherwise the shopper will not find the items shown. We separately check Creator Partnerships, UGC Rights, and permissible Partnership Ads. Foreplay, BigSpy, and PiPiADS are used for market research, not for copying materials. Result: a Creative Testing Matrix by audience, collection, and commercial impact.

We check Category Navigation, Size/Color/Material/Style/Fit Filters, Occasion Collections, On-Site Search, Suggestions, Zero-Result Searches, Visual Search, and Mobile Navigation. For example, an Oversized Linen Shirt should be findable by a combination of cut and material. Contentsquare/VWO help analyze behavior, and Nosto and Bloomreach Discovery help optimize search and recommendations with the appropriate integration. Result: a Fashion Product Discovery Report.

We check Size Guides, differences between brands, US/UK/EU Conversion, Fit Descriptions, Petite/Tall/Plus Size, the model's height and size in photos, materials and stretch, size recommendations, color switching, and returns for Fit Reasons. True Fit and other Size Recommendation Platforms should be evaluated by actual Conversion and Return Rate. Having a Size Finder does not by itself guarantee fewer returns, which underscores the need for an A/B test. Result: a Size & Fit Optimization Plan.

We check the Main Image, on-model photos, angles, Product Video, Fabric Composition, Fit Description, Size Guide, Reviews/Photo Reviews, Care Instructions, Shipping/Returns, and size availability. For a dress, fabric in motion and the model's measurements matter; for outerwear, insulation and conditions of use. Dynamic Yield/Nosto are suitable for recommendations, and VWO, AB Tasty, Kameleoon, and Optimizely for experiments. Result: a Product Detail Page Optimization Backlog.

Shop the Look, Complete the Outfit, and Matching Products build offers from multiple products. We check whether individual items from a look can be purchased, Size/Color Availability, compatibility, Recommendations, Bundles, Cross-Sell Performance, AOV, Conversion, and Contribution Margin. If a featured look contains sold-out sizes, it loses its value. Tools: Nosto, Dynamic Yield, and CMS features. Result: an Outfit-Based Merchandising Plan and AOV hypotheses.

We check Add-to-Cart, Cart-to-Checkout, Completion Rate, Shipping Visibility, Estimates, Return Policy, Exchange Options, Payments, Mobile Checkout, Coupon Fields, Failed Payments, and Friction. We separately check changing size/color directly in the cart without reselecting the product. For diagnostics, Contentsquare/VWO; for A/B Testing, Optimizely, VWO, Convert Experiences, GrowthBook, and AB Tasty. Result: a Checkout & Returns UX Improvement Plan.

We check Welcome/Exit-Intent/Scroll-Triggered Popups, Discount Forms, Collection Notifications, Early Access, Mobile Lead Capture, Consent, Form Conversion, and subscribers' actual purchases. New collections can justify a subscription to new arrivals, and Sale can justify price change notifications. A single universal discount is not always optimal. We use Klaviyo, Omnisend, or On-Site Conversion Tools. Result: a Lead Capture Strategy based on subscriber quality and sales.

We check Wishlist, Save Product/Look, Send Product/Cart to Email, Share Outfit, Back-in-Stock, Price Drop, saving of the selected size/color, and cart recovery. When the user reopens it, prices, discounts, and stock should be updated, while the selected variant itself is preserved. Implementation through Shopify Apps, CRM, and Marketing Automation. Result: a Wishlist & Saved Shopping Journey Plan.

We analyze Viewed Product, Added to Cart, and Started Checkout separately. We check automated Browse/Cart/Checkout Flows, Deduplication, Purchase Suppression, Consent, Frequency Capping, size availability, the current price, and the discount. For a user who browsed several dresses, a flow with saved variants is more useful than a universal discount. Klaviyo, Omnisend, Braze, and Bloomreach are suitable depending on the stack. Result: an Abandonment Recovery Map.

We check New Arrivals, Launch Emails, VIP Early Access, Variant-Level Back-in-Stock, Price Drop, Sale Campaigns, Style Segmentation, frequency, availability at the time of sending, and Revenue per Campaign. A customer who signed up for a dress in size M should not be notified only that size XS is back in stock. For catalog events, Klaviyo, Omnisend, and similar systems are suitable. Result: a Collection Lifecycle Messaging Plan.

We check New/Returning Customers, Repeat Purchase, LTV, RFM, VIP, Loyalty/Referral Programs, Post-Purchase Recommendations, Reviews, Winback, Email/SMS Revenue, and Retention incrementality. Basics and seasonal outerwear have different repeat purchase cycles. Klaviyo, Bloomreach, Braze, and Loyalty Platforms help with automation. Not all sales from recipients of CRM messages are incremental. Result: a Customer Retention & Loyalty Plan.

We check Purchase/Transaction ID, Revenue/Currency, Item/Variant IDs, Size/Color, ViewItem, AddToCart, BeginCheckout, Google Ads Enhanced Conversions, Meta Pixel/CAPI, TikTok Events API, Pinterest CAPI, Event Deduplication, and Returns/Refund Adjustments. A mismatch between Shopify Variant IDs and the Meta Catalog breaks product matching. We reconcile Shopify, GA4, and the ad platforms, taking attribution differences into account. Result: a Conversion Tracking Diagnostics Report.

We count New/Returning Orders, Unique New/Returning Customers, First Purchase Date, Repeat Purchase Rate, New Customer Acquisition Cost, LTV, and Revenue by Cohort separately. A customer who first bought in the spring and bought again in the fall has a Returning Order in the fall but does not become a second new customer. We use the Customer ID and Shopify history/the data warehouse. Result: a report on customers and orders without mixing metrics.

We link Advertising Spend, Revenue, ROAS, Gross Margin, Return-Adjusted Revenue, Contribution Profit, Sell-Through, Markdown, New Customer Acquisition, Creative/Collection Performance, and LTV. A high CTR on an ad video can be accompanied by order returns. Tools: Shopify, GA4, BigQuery, Looker Studio, or BI. Not all platforms reliably link a specific creative view to a purchase. Result: a Fashion Performance Dashboard by collection, category, and profit.

AI assistants can search for clothing by style, material, color, Fit, and occasion: Oversized Linen Blazer for Summer or Black Dress for Wedding Guest. We check Titles, Descriptions, Color, Material, Pattern, Style, Fit, Size, Gender/Age Group where needed, Images, Availability, Price, Reviews, Structured Data, and page accessibility for permitted AI/Search Bots. We separate objective attributes from subjective Style Recommendations. Accurate Product Data does not guarantee mentions in AI. Result: an AI Fashion Discoverability Report.

Google is developing the Universal Commerce Protocol (UCP), and OpenAI the Agentic Commerce Protocol (ACP). We check Product IDs, Size/Color Variants, Feed Updates, Price/Availability, Shipping/Returns, Discounts, selection of a specific variant, Cart/Inventory Validation, Order Confirmation/Status, and availability by country/account. Recommending a dress is not enough without selecting a size and color. Shopify or a correct Feed does not guarantee automatic participation in Agentic Checkout; conditions and phased rollouts apply. Result: an Agentic Fashion Commerce Readiness Checklist.

We distinguish between user visits from AI, AI Crawler activity, and autonomous Agent Interactions. We check AI Referral Traffic, Landing Pages, Engagement, Conversion/Add-to-Cart Rates, Revenue, New/Returning Buyers, available Source Metadata, Order Deduplication, and Agent-Assisted Conversions. GA4 helps analyze referrals, and Contentsquare/VWO help analyze on-site behavior. Result: an AI Shopping Measurement Framework.