BigQuery의 가격 벤치마크 데이터를 통해 판매자는 다른 판매자가 동일한 제품의 가격 책정 방식을 이해할 수 있습니다. Google 판매자 센터 보고 데이터가 BigQuery로 전송되면 Products_PriceBenchmarks_ 테이블의 형식은 국가 및 제품별로 일별 가격 벤치마크를 제공합니다.
개별 판매자 ID를 사용하는 경우에는 데이터가 Products_PriceBenchmarks_MERCHANT_ID 테이블에 기록되고, MCA 계정을 사용하는 경우에는 Products_PriceBenchmarks_AGGREGATOR_ID 테이블에 기록됩니다.
스키마
Products_PriceBenchmarks 테이블에는 다음과 같은 스키마가 있습니다.
열
BigQuery 데이터 유형
설명
product_id
STRING
channel:content_language:feed_label:offer_id 형식으로 된 제품의 Content API REST ID입니다. 제품 테이블 스키마에 정의된 방식과 유사합니다. 이 필드는 기본 키입니다.
merchant_id
INTEGER
판매자 계정 ID
aggregator_id
INTEGER
멀티 클라이언트 계정의 애그리게이터 계정 ID
country_of_sale
STRING
사용자가 Google에서 쿼리를 수행한 국가
price_benchmark_value
FLOAT
쇼핑 광고에서 동일한 제품을 광고하는 모든 판매자를 통틀어 클릭수 가중치를 적용한 특정 제품의 평균 가격입니다. 제품 일치 여부는 GTIN을 기준으로 합니다. 자세한 내용은 고객센터 자료를 참조하세요.
price_benchmark_currency
STRING
벤치마크 값의 통화
price_benchmark_timestamp
DATETIME
벤치마크의 타임스탬프
예: 제품 가격을 벤치마크와 비교
다음 SQL 쿼리는 Products 및 Price Benchmarks 데이터를 조인하여 제품 목록 및 관련 벤치마크를 반환합니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[[["\u003cp\u003eThe Google Merchant Center Price Benchmarks report in BigQuery will be discontinued on September 1, 2025, and users are advised to migrate to the Price Competitiveness report.\u003c/p\u003e\n"],["\u003cp\u003ePrice Benchmarks data allows merchants to analyze how their product pricing compares to other merchants selling the same items, providing a daily benchmark per product and country.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eProducts_PriceBenchmarks_\u003c/code\u003e table contains detailed information including product ID, merchant ID, country of sale, price benchmark value, currency, and timestamp.\u003c/p\u003e\n"],["\u003cp\u003eProducts are matched to one another using their GTIN, and the price benchmark is the average click-weighted price across all merchants advertising the same product on Shopping ads.\u003c/p\u003e\n"],["\u003cp\u003eThe provided SQL example shows how to join \u003ccode\u003eProducts\u003c/code\u003e and \u003ccode\u003ePrice Benchmarks\u003c/code\u003e tables to get a list of products and their associated benchmarks, with the caveat that inner or left joins must be used as not all products will have a benchmark.\u003c/p\u003e\n"]]],[],null,["# Google Merchant Center price benchmarks table\n=============================================\n\nOverview\n--------\n\n| **Caution:** BigQuery will no longer support the Price Benchmarks report on September 1, 2025. We recommend that you migrate to use the [Price Competitiveness report](/bigquery/docs/merchant-center-price-competitiveness-schema) instead. For more information about migrating to the new report, see [Migrate the price competitiveness report](/bigquery/docs/merchant-center-price-competitiveness-migration).\n\nPrice Benchmarks data in BigQuery helps merchants understand how\nother merchants are pricing the same product. When your Google Merchant Center\nreporting data is transferred to BigQuery, the format of the\n`Products_PriceBenchmarks_` table provides a daily price benchmark per country\nand per product.\n\nThe data is written to a table named\n`Products_PriceBenchmarks_`\u003cvar translate=\"no\"\u003eMERCHANT_ID\u003c/var\u003e if you are\nusing an individual Merchant ID, or\n`Products_PriceBenchmarks_`\u003cvar translate=\"no\"\u003eAGGREGATOR_ID\u003c/var\u003e if you're\nusing an MCA account.\n\nSchema\n------\n\nThe `Products_PriceBenchmarks` table has the following schema:\n\nExample: compare product prices to benchmarks\n---------------------------------------------\n\nThe following SQL query joins `Products` and `Price Benchmarks` data to return\nthe list of products and associated benchmarks. \n\n```googlesql\nWITH products AS\n(\n SELECT\n _PARTITIONDATE AS date,\n *\n FROM\n dataset.Products_\u003cvar translate=\"no\"\u003emerchant_id\u003c/var\u003e\n WHERE\n _PARTITIONDATE \u003e= '\u003cvar translate=\"no\"\u003eYYYY-MM-DD\u003c/var\u003e'\n),\nbenchmarks AS\n(\n SELECT\n _PARTITIONDATE AS date,\n *\n FROM\n dataset.Products_PriceBenchmarks_\u003cvar translate=\"no\"\u003emerchant_id\u003c/var\u003e\n WHERE\n _PARTITIONDATE \u003e= '\u003cvar translate=\"no\"\u003eYYYY-MM-DD\u003c/var\u003e'\n)\nSELECT\n products.date,\n products.product_id,\n products.merchant_id,\n products.aggregator_id,\n products.price,\n products.sale_price,\n benchmarks.price_benchmark_value,\n benchmarks.price_benchmark_currency,\n benchmarks.country_of_sale\nFROM\n products\nINNER JOIN\n benchmarks\nON products.product_id = benchmarks.product_id AND\n products.merchant_id = benchmarks.merchant_id AND\n products.date = benchmarks.date\n```\n| Notes on joining the data: \n| 1. Not all products have benchmarks, so use INNER JOIN or LEFT JOIN accordingly. \n| 2. Each product may have multiple benchmarks (one per country)."]]