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Ecommerce 2025 chapter (#4350)
* chore(config): enable ecommerce chapter * chore(contributors): add Amandeep Singh * chore(config): update ecommerce publish date * feat(ecommerce): refresh 2025 chapter content * chore(ecommerce): align 2025 metadata * Initial edit * Take images * Optimised images with calibre/image-actions * More edits * Update ecommerce adoption and platform share stats * Refresh ecommerce CWV and Lighthouse data * Update payment provider figures and tables * Ecommerce 2025 SQL queries (#4354) * Add 2025 ecommerce SQL queries * Update to July --------- Co-authored-by: Amandeep Singh <amandeepsinghvirdi@gmail.com> * Fix queries for 2025 * More queries * Update more numbers to July * More table updates * docs: clarify Lighthouse comparison limitations in ecommerce guide * docs: update ecommerce stats to display as percentages instead of absolute numbers * Update date * Add doi * Final edits * Retake images * Add one more query * Optimised images with calibre/image-actions * Update featured stats * Optimised images with calibre/image-actions --------- Co-authored-by: Amandeep Singh <amandeepsinghvirdi@gmail.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎sql/2025/ecommerce/core_web_vitals_by_platform-yoy.sql‎

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#standardSQL
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# CrUX Core Web Vitals performance of Ecommerce vendors by device (fid was upated to inp, and is non optinal now)
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CREATE TEMP FUNCTION IS_GOOD(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good / (good + needs_improvement + poor) >= 0.75
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);
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CREATE TEMP FUNCTION IS_NON_ZERO(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good + needs_improvement + poor > 0
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);
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SELECT
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date,
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client,
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ecomm,
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COUNT(DISTINCT origin) AS origins,
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# Origins with good LCP divided by origins with any LCP.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_lcp, avg_lcp, slow_lcp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp), origin, NULL))
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) AS pct_good_lcp,
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# Origins with good INP divided by origins with any inp.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_inp, avg_inp, slow_inp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_inp, avg_inp, slow_inp), origin, NULL))
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) AS pct_good_inp,
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# Origins with good CLS divided by origins with any CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL))
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) AS pct_good_cls,
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# Origins with good LCP, inp, and CLS divided by origins with any LCP, inp, and CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(
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IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AND
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IS_GOOD(fast_inp, avg_inp, slow_inp) AND
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IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL
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)),
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COUNT(DISTINCT IF(
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IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp) AND
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IS_NON_ZERO(fast_inp, avg_inp, slow_inp) AND
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IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL
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))
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) AS pct_good_cwv
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FROM
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`chrome-ux-report.materialized.device_summary`
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JOIN (
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SELECT DISTINCT
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date,
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client,
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root_page,
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tech.technology AS ecomm
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FROM
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`httparchive.crawl.pages`,
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UNNEST(technologies) AS tech,
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UNNEST(tech.categories) AS category
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WHERE
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date = '2025-07-01' AND
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category = 'Ecommerce' AND
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(
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tech.technology != 'Cart Functionality' AND
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tech.technology != 'Google Analytics Enhanced eCommerce'
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)
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)
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ON
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CONCAT(origin, '/') = root_page AND
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IF(device = 'desktop', 'desktop', 'mobile') = client AND
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date
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WHERE
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date = '2025-07-01'
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GROUP BY
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client,
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ecomm
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ORDER BY
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origins DESC

‎sql/2025/ecommerce/core_web_vitals_by_platform.sql‎

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#standardSQL
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# CrUX Core Web Vitals performance of Ecommerce vendors by device (fid was upated to inp, and is non optinal now)
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CREATE TEMP FUNCTION IS_GOOD(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good / (good + needs_improvement + poor) >= 0.75
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);
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CREATE TEMP FUNCTION IS_NON_ZERO(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good + needs_improvement + poor > 0
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);
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11+
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SELECT
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client,
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ecomm,
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COUNT(DISTINCT origin) AS origins,
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# Origins with good LCP divided by origins with any LCP.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_lcp, avg_lcp, slow_lcp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp), origin, NULL))
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) AS pct_good_lcp,
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# Origins with good INP divided by origins with any inp.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_inp, avg_inp, slow_inp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_inp, avg_inp, slow_inp), origin, NULL))
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) AS pct_good_inp,
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# Origins with good CLS divided by origins with any CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL))
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) AS pct_good_cls,
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# Origins with good LCP, inp, and CLS divided by origins with any LCP, inp, and CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(
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IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AND
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IS_GOOD(fast_inp, avg_inp, slow_inp) AND
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IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL
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)),
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COUNT(DISTINCT IF(
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IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp) AND
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IS_NON_ZERO(fast_inp, avg_inp, slow_inp) AND
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IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL
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))
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) AS pct_good_cwv
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FROM
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`chrome-ux-report.materialized.device_summary`
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JOIN (
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SELECT DISTINCT
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client,
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root_page,
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tech.technology AS ecomm
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FROM
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`httparchive.crawl.pages`,
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UNNEST(technologies) AS tech,
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UNNEST(tech.categories) AS category
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WHERE
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date = '2025-07-01' AND
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category = 'Ecommerce' AND
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(
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tech.technology != 'Cart Functionality' AND
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tech.technology != 'Google Analytics Enhanced eCommerce'
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)
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)
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ON
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CONCAT(origin, '/') = root_page AND
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IF(device = 'desktop', 'desktop', 'mobile') = client
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WHERE
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date = '2025-07-01'
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GROUP BY
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client,
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ecomm
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ORDER BY
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origins DESC

‎sql/2025/ecommerce/core_web_vitals_passingmetrics_byvendor_bydevice.sql‎

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#standardSQL
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# CrUX Core Web Vitals performance of Ecommerce vendors by device (fid was upated to inp, and is non optinal now)
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CREATE TEMP FUNCTION IS_GOOD(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
4+
good / (good + needs_improvement + poor) >= 0.75
5+
);
6+
7+
CREATE TEMP FUNCTION IS_NON_ZERO(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good + needs_improvement + poor > 0
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);
10+
11+
12+
SELECT
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client,
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ecomm,
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COUNT(DISTINCT origin) AS origins,
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# Origins with good LCP divided by origins with any LCP.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_lcp, avg_lcp, slow_lcp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp), origin, NULL))
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) AS pct_good_lcp,
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# Origins with good INP divided by origins with any inp.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_inp, avg_inp, slow_inp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_inp, avg_inp, slow_inp), origin, NULL))
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) AS pct_good_inp,
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# Origins with good CLS divided by origins with any CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL))
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) AS pct_good_cls,
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# Origins with good LCP, inp, and CLS divided by origins with any LCP, inp, and CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(
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IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AND
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IS_GOOD(fast_inp, avg_inp, slow_inp) AND
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IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL
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)),
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COUNT(DISTINCT IF(
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IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp) AND
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IS_NON_ZERO(fast_inp, avg_inp, slow_inp) AND
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IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL
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))
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) AS pct_good_cwv
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FROM
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`chrome-ux-report.materialized.device_summary`
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JOIN (
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SELECT DISTINCT
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client,
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root_page,
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app AS ecomm
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FROM
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`httparchive.crawl.pages`,
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UNNEST(technologies) AS tech,
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UNNEST(categories) AS category
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WHERE
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date = '2025-07-01' AND
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category = 'Ecommerce' AND
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(
62+
technology != 'Cart Functionality' AND
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technology != 'Google Analytics Enhanced eCommerce'
64+
)
65+
)
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ON
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CONCAT(origin, '/') = root_page AND
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IF(device = 'desktop', 'desktop', 'mobile') = client
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WHERE
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date = '2025-07-01'
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GROUP BY
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client,
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ecomm
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ORDER BY
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origins DESC

‎sql/2025/ecommerce/counts.sql‎

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SELECT
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client,
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date,
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EXTRACT(YEAR FROM date) AS year,
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rank_grouping,
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CASE
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WHEN rank_grouping = 100000000 THEN 'all'
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ELSE FORMAT("%'d", rank_grouping)
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END AS ranking,
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COUNT(DISTINCT root_page) AS ecommerce_sites,
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total,
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COUNT(DISTINCT root_page) / total AS pct_ecommerce
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FROM
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`httparchive.crawl.pages`,
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UNNEST(technologies) AS technologies,
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UNNEST(technologies.categories) AS cats,
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UNNEST([1000, 10000, 100000, 1000000, 10000000, 100000000]) AS rank_grouping
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JOIN (
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SELECT
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date,
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client,
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rank_grouping,
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COUNT(DISTINCT root_page) AS total
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FROM
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`httparchive.crawl.pages`,
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UNNEST([1000, 10000, 100000, 1000000, 10000000, 100000000]) AS rank_grouping
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WHERE
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date IN ('2025-07-01', '2024-06-01', '2023-07-01', '2022-06-01', '2025-07-01') AND
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rank <= rank_grouping
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GROUP BY
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date,
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client,
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rank_grouping
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)
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USING (date, client, rank_grouping)
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WHERE
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date IN ('2025-07-01', '2024-06-01', '2023-07-01', '2022-06-01', '2025-07-01') AND
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rank <= rank_grouping AND
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cats = 'Ecommerce' AND
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technologies.technology NOT IN ('Cart Functionality', 'Google Analytics Enhanced eCommerce')
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GROUP BY
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date,
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client,
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rank_grouping,
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total
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ORDER BY
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date DESC,
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client,
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rank_grouping

‎sql/2025/ecommerce/median_lighthouse_score_ecommsites.sql‎

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WITH totals AS (
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SELECT
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client,
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COUNT(DISTINCT page) AS total_webpages,
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COUNT(DISTINCT root_page) AS total_websites
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FROM
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`httparchive.crawl.pages`
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WHERE
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date = '2025-07-01'
10+
GROUP BY
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client
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)
13+
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SELECT
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client,
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technology,
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ARRAY_AGG(DISTINCT category) AS categories,
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APPROX_QUANTILES(CAST(JSON_VALUE(lighthouse.categories.performance.score) AS NUMERIC) * 100, 1000)[OFFSET(500)] AS median_performance,
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APPROX_QUANTILES(CAST(JSON_VALUE(lighthouse.categories.accessibility.score) AS NUMERIC) * 100, 1000)[OFFSET(500)] AS median_accessibility,
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APPROX_QUANTILES(CAST(JSON_VALUE(lighthouse.categories.seo.score) AS NUMERIC) * 100, 1000)[OFFSET(500)] AS median_seo,
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APPROX_QUANTILES(CAST(JSON_VALUE(lighthouse.categories.`best-practices`.score) AS NUMERIC) * 100, 1000)[OFFSET(500)] AS median_best_practices,
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ANY_VALUE(total_websites) AS total_websites,
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COUNT(DISTINCT root_page) AS number_of_websites,
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COUNT(DISTINCT root_page) / ANY_VALUE(total_websites) AS percent_of_websites
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FROM
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`httparchive.crawl.pages`,
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UNNEST(technologies) AS tech,
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UNNEST(categories) AS category
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INNER JOIN
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totals
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USING (client)
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WHERE
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date = '2025-07-01' AND
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category = 'Ecommerce' AND
35+
(
36+
technology != 'Cart Functionality' AND
37+
technology != 'Google Analytics Enhanced eCommerce'
38+
)
39+
GROUP BY
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client,
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technology
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ORDER BY
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client,
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number_of_websites DESC

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