01
Sync Shopify product data with ad catalogs
Start from the catalog surfaces that feed paid channels, not just the storefront product page alone.
Investigate Shopify Product Catalog & Ad Sync Errors
Use Spark to review catalog and product signals alongside ad performance. Identify the records that need attention before deciding how to fix a Facebook catalog error or other paid-ad feed issue.
Product
Spark: AI Store Assistant
Category
Catalog
If the current pain looks more like "Catalog errors are blocking product ads from serving cleanly; Products are being rejected or showing field mismatches across platforms", this workflow is designed to turn those scattered signals into one repeatable operating sequence. It moves through Sync Shopify product data with ad catalogs -> Review feed errors and rejected items -> Prioritize the fixes by business impact so different team members are not improvising the same job in different ways.
By the end, the team should have deliverables such as A catalog health view, A prioritized fix list and use them to drive outcomes like Catch catalog issues earlier, Reduce revenue leakage from rejected or broken products, Help the team fix feed quality based on business priority.
How it works
See how this workflow runs from signal to action so the team can actually reach the intended outcome.
02
Review feed errors and rejected items
Identify missing images, field mismatches, pricing issues, rejected products, and other catalog health problems.
03
Prioritize the fixes by business impact
Use Spark to connect product issues to ad performance so the team fixes what matters most first.
Related product and workflows
Product
Spark: AI Store Assistant
A Shopify AI agent and ecommerce assistant that plans and executes supported store tasks using connected data and tools.
Best AppsAnalytics
Best Shopify Analytics Apps
Help CenterStore tasks
How to use the Spark AI assistant
Set a task goal and review the supported steps using connected store data.
Frequently asked questions
Frequently asked questions
Is this just a feed error list?
No. The stronger use case is that Spark helps connect feed problems to actual product and ad performance, so the team knows what to repair first.
Why is this a Spark use case instead of a generic feed app page?
Because the real operator job is not only fixing fields. It is understanding how catalog quality, ad delivery, and product performance connect.