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Rank product-title opportunities from search gaps

Mine high-intent terms from a BigQuery catalog, inspect competitor title patterns in a browser, and produce scored rewrite recommendations.

Goal

Deliver a product-operations report with search gaps, scores, and title recommendations.

Steps

  1. Configure read-only BigQuery product data and Vertex AI/ADK credentials; scope the target product group. [src10-google-score-product-title-opportunities-from-search-results]
  2. keyword_finding_agent extracts category/style/attribute/use-case terms; search_results_agent inspects competing retail titles with ComputerUseToolset. [src10-google-score-product-title-opportunities-from-search-results]
  3. Have comparison_root_agent produce TitleOptimizationReport, ranking rewrites by keyword gaps and searchability score. [src10-google-score-product-title-opportunities-from-search-results]

How to verify

Check each report row has a BigQuery product ID, competitor observation, and 0–100 score; title suggestions must not alter factual product attributes.

Troubleshooting

If BigQuery returns no products, inspect ADC project/dataset; if browser search fails, mark competitor data unavailable instead of inventing titles.

Permissions

Prerequisites

Permissions

Limitations

Review competitor titles and search results regularly, then measure title changes against traffic and conversion data.

Pinned upstream source

https://github.com/google/adk-samples/blob/9252494a7aaafca8fe4d81a04f15392345a5779b/contrib/python/brand-search-optimization/README.md

Repository
google/adk-samples
File path
contrib/python/brand-search-optimization/README.md
Pinned commit
9252494a7aaafca8fe4d81a04f15392345a5779b
Source checked
2026-10-14 · Reviewed against the pinned upstream source

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License: Apache-2.0

Main topic

Turn ecommerce campaign materials into a reviewable weekly brief

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