Performance Marketing Audit for Sporting Goods E-commerce: 30-Point Checklist

Store categories do not necessarily match advertising segments: Running Shoes, Training Shoes, and Hiking Shoes may sit in the "Footwear" category but respond to different queries. We check sports, intended use, Apparel/Footwear/Equipment/Accessories differences, Performance by category, brand, price, and SKU, the link between categories and campaigns and Landing Pages, and sales of specific variants. Special attention goes to Product Segmentation for Shopping, PMax, and Dynamic Product Ads. Audit result: a list of product groups for separate management of assortment, budget, creatives, or economics; a separate campaign is not always required, and excessive fragmentation worsens automated learning.

ROAS does not show SKU profitability. An expensive piece of fitness equipment can bring high Revenue, but profit shrinks because of shipping, installation, and servicing. We compare advertising spend with product cost and order fulfillment. We check Gross Margin and Contribution Margin by SKU/category, discounts, shipping, returns, cancellations, Advertising Cost, Contribution Profit, and differences between configurations and sizes. A category with a 400% ROAS and a 20% gross margin can be less profitable than one with a 300% ROAS and a 55% margin. Optimizing only for Purchase Value can increase sales of low-profit SKUs. Result: a Profitability Report and recommendations on Margin-Based Segmentation and Value-Based Bidding.

In Stock is not enough: a popular sneaker model may still have 40 pairs left, but the main sizes are sold out. Advertising attracts shoppers to unsuitable variants. We check stock at the Product Variant level, popular sizes, Inventory Data updates, the link between stock and product groups, Backorder/Preorder, and clearance of seasonal stock. We check how quickly data updates from Shopify/ERP → Google Merchant Center, Meta Commerce Manager, TikTok, and Pinterest. For large catalogs, we analyze Inventory-Based Labels by Stock Depth and sales velocity. Result: a list of discrepancies between advertising activity and the available assortment.

For footwear and apparel, size, color, gender, age, and intended use matter; for bicycles, the frame, wheels, and configuration; for fitness equipment, dimensions and load capacity. We check Variant IDs, Product Group IDs, item_group_id, GTIN, MPN, Brand, Size/Color/Gender/Age Group, Titles, Descriptions, technical specifications on pages, and consistency with the website. Reusing identifiers for different variants and inconsistency across channels break Dynamic Product Ads selection and the matching of events to the catalog. Result: Product Data errors that affect Shopping, Product Discovery, and conversion.

The cost of shipping a T-shirt, a bicycle, and a piece of fitness equipment differs; assembly and special packaging/servicing may be involved. We check shipping by category and geography, the Free Shipping Threshold, returns by size and style, cancellations due to out-of-stock products, delivery times, Local Pickup, and costs for oversized items. Shipping Settings in Merchant Center and other catalogs must match Checkout. A free shipping promise in the ad combined with an extra charge at Checkout leads to drop-offs. Result: an assessment of the impact of logistics and returns on profitability and conversion.

Besides Black Friday/Christmas, sports seasons, the start of training periods, weather, and competitions matter. Demand for bicycles, skis, running gear, and swimming changes in different ways. We check Google Trends, Search Demand, weekly sales history, geography, CPC/CPA/Conversion Rate, preparation timelines, and whether the budget matches stock. Seasonal growth in demand and growth in competitor activity should not be confused. Result: a Seasonal Media Calendar covering preparation, launch, peak, and clearance of remaining stock.

Running, Trail Running, and Racing Shoes are different needs. In Paid Search, we compare the Search Query, Ad Copy, Landing Page, the product's intended use, Brand/Generic/Competitor/Model-Specific Queries, informational and commercial intent, Negative Keywords, and exclusions. We take into account the limited transparency of parts of PMax and AI Max. Result: a Search Intent Map and mismatches between queries, ads, and pages.

We compare not only prices but also brands, exclusives, free shipping, loyalty programs, warranties, and service. Semrush Advertising Research and SpyFu help with Paid Search; Foreplay, BigSpy, and PiPiADS with Meta/TikTok Creative Research; Similarweb Ad Intelligence, Pathmatics, and AdClarity with the media mix. We check the advertised categories, discounts and Bundles, ad copy, seasonality, Landing Pages, and price and shipping positioning. Competitive databases do not reveal actual ROAS and profit. Result: a Competitive Offer & Creative Matrix.

We check Product Approval/Disapproval, Missing Attributes, Warnings, Price and Availability consistency, Titles/Descriptions, Images, GTIN/Brand/MPN/Item Grouping, Shipping/Return Policies, and Custom Labels. "Men's Shoes Model X" may poorly convey the model's intended use; a more detailed title must follow Google's requirements, and its impact on advertising should be tested. DataFeedWatch, Channable, Feedonomics, and Productsup help with Mapping, Feed Rules, Channel-Specific Feeds, and catalog quality. Result: a Product Feed Error Report and tasks for improving data.

We check the structure of Search, Shopping, and Performance Max, category coverage, Brand/Non-Brand, Search Query Relevance, Asset Groups, Listing Groups, product filters, demand overlap, Merchant Center Promotions, and automation. If one PMax campaign combines different margins and seasons, we assess whether it needs to be split. However, splitting without considering Conversion Volume, budgets, and algorithm learning is not recommended. Result: recommendations on Campaign Structure, Product Segmentation, and automation.

Products with high margin, demand, and stock can have greater advertising value. We check Margin Custom Labels, Product Performance by Item ID, expensive categories, Spend without sales, prices, and New/Existing Customer Sales. We compare Google Ads with Shopify, the ERP, and the data warehouse. A high ROAS does not guarantee a high Contribution Profit after shipping and returns. Result: Product Prioritization based on commercial value.

We check Spend/Conversion Value, Brand/Non-Brand, New Customer Acquisition Cost, Returning Buyers, CPA/ROAS by category, cannibalization of organic demand, and Experiments/Incrementality Tests. Different campaigns can reach the same shoppers at different stages. Scaling based only on aggregate ROAS can overestimate incremental revenue. Result: a budget reallocation plan that accounts for Incrementality and economics.

Meta Ads are evaluated together with Commerce Manager. We check the Shopify Connection, Catalog Diagnostics, Product Approval, Availability, Price Matching, Product/Variant IDs, Product Sets, consistency of Content IDs in Pixel/CAPI with the catalog, ViewContent/AddToCart/Purchase, deduplication, images, and titles. For sports, Product Sets can reflect sports, equipment, gender, sizes, and prices: Running Shoes, Hiking Equipment, Fitness Accessories. Segmentation should not break variant availability or event identifiers. Result: Meta Catalog readiness for Prospecting, Retargeting, and personalized product ads.

We check the TikTok Catalog, Approval, Price, Availability, Product Images/Videos, IDs, Product Sets, Automated Rules, TikTok Pixel/Events API, available Smart+ Catalog Ads, and the results of video versus product formats. A workout demonstration video can be linked to catalog products. Channable, DataFeedWatch, and Feedonomics are suitable for working with feeds if the selected channel and configuration are supported. Catalog Ads availability depends on the country and the account. Result: a list of TikTok Catalog issues and improvements to the product and video connection.

Pinterest is useful for visual discovery of an active lifestyle, home fitness, Outdoor, and apparel. We check the Pinterest Catalog, diagnostics, Titles/Descriptions, prices/stock, Product Groups, images, Pinterest Tag/Conversions API, Shopping Ads Performance, and differences between organic Product Pins and paid advertising. Product use scenarios in images can help shoppers evaluate the offer. For feeds, we evaluate Channable, DataFeedWatch, Productsup, and Feedonomics where an integration is available. Result: improvements to the Pinterest Catalog and Shopping Campaigns.

Sporting goods often require Product Demonstration: footwear on different surfaces, fitness equipment during a workout, a bicycle in motion. We check Technical Feature Explanation, UGC, Customer Reviews, Athlete/Coach Recommendations, Comparisons, Lifestyle, Promotions, and Bundles. We analyze creatives by product group, the number of hypotheses, and test results. A high price may require a technical explanation; simple accessories may need a short demonstration. Result: a Creative Testing Matrix by category and scenario.

We check search by intended use, Sport-Specific Categories, size and technical filters, brand/model, accessory compatibility, Product Recommendations, Zero-Result Search, Mobile Navigation, and speed. When choosing running shoes, the surface, cushioning, and last width may matter. Contentsquare and VWO help analyze Journeys, Heatmaps, Session Replays, and catalog issues. Result: Product Discovery Issues with an assessment of their impact on conversion.

We check specifications, Size Guides, Comparisons, Compatibility, Reviews, Videos, Variant Availability, Shipping/Returns, and Cross-Sell. A bicycle requires guidance on rider height and frame size, footwear on sizes and intended use, and fitness equipment on dimensions and assembly. Dynamic Yield helps with personalized Recommendations; VWO, AB Tasty, and Optimizely with A/B Testing. We measure not only block CTR but also Revenue, AOV, and Contribution Profit. Result: a Product Page Optimization Backlog by category.

We check Add-to-Cart Rate, Cart-to-Checkout, Completion Rate, early display of Shipping Costs, Mobile Checkout, Payments, Guest Checkout, Coupons, Errors, Failed Payments, and returns to size and configuration selection. Contentsquare/VWO are suitable for diagnostics; AB Tasty, Convert Experiences, Kameleoon, Optimizely, and GrowthBook for experiments. The choice depends on the CMS, traffic volume, and the development team. Result: CRO Hypotheses by Impact and Effort.

A visitor may compare models, sizes, and prices without buying right away. We check Triggered Popups, Exit Intent, Discount Forms, Embedded Email, Mobile Lead Capture, Consent, Welcome Flows, and how relevant the offer is to the category. A Running Shoes shopper can be offered assortment updates, and a visitor to a fitness equipment page a buying guide. Klaviyo and Omnisend or specialized Popup Tools help with automation. A captured email does not equal an incremental sale: we evaluate subscriber quality, purchases, and the impact on conversion. Result: a Lead Capture Mechanics Plan.

For expensive or technically complex products, Save Product to Email, Send Cart to Email, Wishlist, Share Product, Price Drop Notifications, Back-in-Stock Subscription, and saving the selected size are useful. One shopper may compare bicycles and return to a model later; another may forward a cart with fitness equipment. This can be implemented through Shopify Apps or a Klaviyo/CRM integration. It is important to preserve the variant, the updated price, and availability when the link is reopened. Result: ways to bring back interested users without making them select the product again.

A chat assistant for Sporting Goods can match running shoes to a surface, compare bicycles, put together a home fitness set, select compatible accessories, and clarify shipping. We check the Product Recommendation Assistant, the freshness of product data, recommendation accuracy, Human Handoff, contact capture with consent, CRM/Automation, and Conversion Rate after the conversation. We use Guided Selling/Quiz Tools or Gorgias, Intercom, or Zendesk with AI capabilities. The assistant must not invent specifications or unconfirmed compatibility. Result: Conversational Commerce Use Cases and data requirements.

We separate Browse Abandonment (a known user viewed a product), Cart Abandonment (a product was added, no purchase), and Checkout Abandonment (checkout was started but not completed). We check Viewed Product, Added to Cart, Started Checkout, separate Email Flows, deduplication with Shopify Emails, exclusion of buyers, frequency, Consent, prices/availability, and different scenarios by product category and value. A flow can return the link to a saved Checkout or clarify shipping for an expensive bicycle. Klaviyo, Omnisend, and Braze are suitable. Result: a Recovery Flow Map with triggers, exclusions, delays, and metrics.

We check the Welcome Series, Discount Offer, segmentation by sport, Price Drop, Back-in-Stock, Post-Purchase Recommendations, Cross-Sell, Review Requests, Winback, Loyalty, and Suppression Rules. After running shoes, a store can offer socks and care products; after a bicycle, accessories that are actually compatible. Klaviyo, Omnisend, Bloomreach, and Braze support the relevant Lifecycle scenarios depending on the stack. We measure Revenue per Recipient, Repeat Purchase, Unsubscribe, and Incremental Revenue when control groups are available. Result: a Direct Marketing Lifecycle Map.

We reconcile Shopify orders and revenue with GA4, Google Ads, Meta, and other platforms. We check Purchase/Transaction ID, Revenue/Currency, Item/Variant IDs, AddToCart, BeginCheckout, Enhanced Conversions, Google Ads Actions, Meta Pixel/CAPI, TikTok Events API, Pinterest CAPI, deduplication, returns, cancellations, and value adjustments. Differences between advertising platforms arise not only from errors but also from different attribution windows/models. Result: a Measurement Diagnostics Report.

We separate New/Returning Orders and New/Returning Unique Customers, New Customer Acquisition Cost, Repeat Purchase Rate, and LTV. A customer who first bought in March and came back in September should not be counted again as a new person; their second order is a Returning Purchase. We use a persistent Customer ID and Shopify history, and a data warehouse if needed. Result: an acquisition and retention report with Orders and Unique Customers reported separately.

We combine Advertising Spend, Revenue, ROAS, Gross Margin, Shipping/Fulfillment, Returns/Discounts, Contribution Profit, New Customer Acquisition Cost, Repeat Purchase Revenue, and channels at the category, brand, and SKU level. We use Shopify, GA4, BigQuery, Looker Studio, or an existing BI tool. The report should help decide what to scale, which products to exclude, where to raise conversion, and which audiences to bring back. Result: Commercial Performance Reporting.

AI assistants help compare sporting goods by technical parameters. We check Titles, Descriptions, Structured Product Data, GTIN/Brand/Identifiers, Variants/Specifications, Price/Availability, Shipping/Returns, Reviews, page accessibility for permitted bots, and consistency between the website and feeds. If a bicycle has no frame size listed, it is harder for AI to match it to a query. A high-quality catalog does not guarantee citations in AI. Result: an AI Product Discoverability Checklist.

Google is developing the Universal Commerce Protocol (UCP), and OpenAI the Agentic Commerce Protocol (ACP) and structured Product Feeds for Shopping Experiences. We check SKU/Variant IDs, feed updates, Price/Inventory, Shipping/Returns, Cart creation for a specific variant, Price Validation, Promo Codes/Taxes/Fulfillment, order confirmation, Order Status, and integration availability by country/account. It is not enough for an AI agent to know the shoe model: it needs the size, color, current price, and shipping. Technical readiness does not mean automatic participation in UCP Checkout or OpenAI Instant Checkout: implementation is phased and subject to program rules. Result: an Agentic Commerce Readiness Report.

We analyze AI Referral Traffic, Landing Pages, Conversion Rate, available Agentic Interactions, passing the source into Order Metadata, IDs/deduplication, Revenue, Attribution, and New/Returning Customers. AI Referrals, AI Crawlers, and agent actions cannot be combined into one metric: they are different types of activity. GA4 is suitable for Referral Sessions, and Contentsquare/VWO for On-Site Behavior. Result: a measurement model for Discovery, Referral Traffic, Engagement, and Orders.