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ACP Feed Optimizer

Case Study Template 02

Verification pending

Variant normalization workflow for a Shopify catalog with inconsistent size and color data

Publication-ready structure for a merchant case study focused on broken variant grouping, attribute inconsistency, and cleanup prioritization. Merchant details and measured results are still awaiting approval.

Company snapshot

  • Merchant name pending written approval before publication.
  • Current placeholder scope: Shopify apparel or accessories catalog with large variant depth.
  • Multiple channels depended on consistent color, size, and parent-child variant structure.

Initial feed problem

The catalog used inconsistent option values and incomplete attributes, which made related products hard to group cleanly across shopping and social channels.

Why it mattered commercially

When variants are messy, channels can split product groups, dilute listing quality, and create more manual catalog cleanup for the merchant or agency managing the account.

What ACP found in the audit

  • Color and size values were not standardized across related products.
  • Variant structure and attribute gaps were reducing downstream feed consistency.
  • Product-level recommendations were needed to decide which items to normalize first.

What fixes were prioritized

  • Normalize shared variant values for color, size, and related options.
  • Repair attribute coverage on the products with the highest channel exposure.
  • Review before and after product previews before pushing updates live.

What changed operationally

  • Create one naming standard for high-volume variant fields.
  • Reduce repeated manual cleanup by documenting issue patterns inside the audit workflow.
  • Use the same audit output for merchant review and agency reporting.

Results

Disapproval reduction

TODO: add verified before and after disapproval count.

Use Merchant Center or channel export data with dates.

CTR improvement

TODO: add verified CTR change for the audited product set.

Cite the reporting window and traffic source.

ROAS improvement

TODO: add verified ROAS change only if attribution is defensible.

Separate feed cleanup impact from bidding, budget, and creative changes.

Variant error reduction

TODO: add verified reduction in variant-related feed errors.

Use the same rule set before and after cleanup.

Time saved

TODO: add verified workflow time saved per week or month.

Document who measured it and what tasks were removed.

Quote slot

TODO: add an approved merchant or agency quote with publication consent.

Measurement and attribution note

TODO: replace with the verified normalization window and post-cleanup observation period before publishing this case study.

TODO: confirm how variant errors, sync success, and downstream performance were measured.

TODO: state clearly whether merchandising, pricing, or campaign changes happened in the same period.