Google Shopping Optimizer

Ecommerce sellers running Google Shopping campaigns often struggle to get their product feeds approved, maintain competitive visibility, and achieve profitable return on ad spend. This skill analyzes your Google Merchant Center product data, identifies feed quality issues, recommends bidding strategy improvements, and generates an actionable optimization plan that increases impressions, clicks, and conversions while reducing wasted spend.

Use when

• A seller says "my Google Shopping ads aren't getting impressions" or "my products keep getting disapproved in Merchant Center"
• An ecommerce operator asks "how do I improve my Google Shopping feed quality score" or needs help fixing feed errors and warnings
• A brand manager wants to compare Smart Shopping vs Standard Shopping campaign structures and decide which approach fits their catalog size and goals
• A performance marketer needs help setting up product group bid segmentation by category, brand, margin tier, or custom labels to maximize ROAS

What this skill does

This skill takes your Google Merchant Center product feed data including titles, descriptions, images, prices, GTINs, product categories, and custom labels and runs a structured quality audit against Google feed specification requirements. It checks for common disapproval triggers such as missing identifiers, price mismatches, policy-violating language, and image quality issues. Beyond the feed audit, it analyzes your campaign structure and bidding approach, recommending whether to use target ROAS, maximize conversions, or manual CPC based on your catalog size, average order value, and competitive landscape. The output is a prioritized action plan with specific feed fixes, bid adjustments, and negative keyword recommendations.

Inputs required

• product_feed_sample (required): A sample of your product feed data including at least title, description, price, image URL, GTIN/MPN, and product category for 5-20 representative products. Paste as CSV rows or structured text.
• campaign_goals (required): Your primary objective such as maximize revenue at 400% ROAS target, drive traffic to new product launches, or clear seasonal inventory within 30 days.
• monthly_budget (optional): Your current or planned monthly Google Shopping budget. Helps calibrate bidding strategy recommendations and identify whether budget is a limiting factor on impression share.
• competitor_context (optional): Names of 2-3 key competitors or links to their product pages. Enables competitive pricing and positioning analysis in the recommendations.

Output format

The output is divided into four structured sections. First, a Feed Health Report that scores each product on title optimization including keyword inclusion, character length, and attribute ordering, description completeness, image compliance, and identifier accuracy, with a pass/warning/fail rating per product and specific fix instructions for each issue found. Second, a Campaign Structure Recommendation section that advises on campaign segmentation by product type, margin tier, or brand, bidding strategy selection, and budget allocation across campaigns. Third, a Priority Action List of 8-15 numbered specific actions ranked by expected impact for example Add color and size to titles for apparel products using the pattern Brand plus Gender plus Product Type plus Color plus Size or Create a separate campaign for products with margins above 40 percent and set target ROAS to 500 percent. Fourth, a Quick Wins Summary table listing the 3-5 changes that can be implemented in under one hour with the highest expected performance lift.

Scope

• Designed for: ecommerce operators, performance marketers, and DTC brand teams running Google Shopping campaigns
• Platform context: Google Merchant Center, Google Ads Shopping campaigns, Performance Max campaigns with shopping feeds
• Language: English

Limitations

• Does not connect directly to Google Merchant Center or Google Ads APIs. Analysis is based on feed data you provide as text input
• Bidding and budget recommendations are directional guidance based on best practices, not predictive modeling with your historical conversion data
• Cannot guarantee Google approval of feed items. Final approval depends on Google automated and manual review processes which may apply policies differently across regions and product categories