Ecommerce 2021 queries

sql/2021/ecommerce/README.md

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   Analysts: if helpful, you can use this README to give additional info about the queries.
 -->
+
+Current list of queries used
+
+* Ecommerce comparison 2020 to 2021. - pct_ecommsites_bydevice_compare20202021.sql
+* Top ecommerce platforms. - top_vendors.sql, top_vendors_crux_rank.sql
+* Enterprise ecommerce platforms (desktop) - top_vendors.sql
+* Enterprise ecommerce platforms - 2019 desktop
+* Enterprise ecommerce platforms - 2020 desktop
+* Ecommerce platform growth Covid-19 impact - ecomm_vendors_covid_growth.sql
+* Page requests distribution. - pagestats_percentiles_bydevice.sql
+* Page weight distribution. - pagestats_percentiles_bydevice.sql
+* Median page requests by type. - pagestats_percentile_bydevice_format.sql
+* Median page kilobytes by type. - pagestats_percentile_bydevice_format.sql
+* Distribution of HTML bytes per ecommerce page - pagestats_html_bydevice.sql
+* Distribution of image requests for ecommerce - pagestats_image_bydevice.sql
+* Distribution of image bytes for ecommerce - pagestats_percentiles_bydevice.sql
+* Popular image formats on ecommerce sites - pagestats_image_bydevice_format.sql
+* Distribution of third-party requests - pct_3pusage_bydevice.sql
+* Distribution of third-party bytes - pct_3pusage_bydevice.sql
+* Real-user Largest Contentful Paint experiences - core_web_vitals_distribution_byvendor_bydevice.sql
+* Real-user First Input Delay experiences - core_web_vitals_distribution_byvendor_bydevice.sql
+* Real-user Cumulative Layout Shift experiences - core_web_vitals_distribution_byvendor_bydevice.sql
+* Real-user Core Web Vitals experiences - core_web_vitals_passingmetrics_byvendor_bydevice.sql
+* Top analytics solutions on ecommerce sites - top_analytics_providers_bydevice_wapp.sql
+* Tag manager usage on ecommerce sites. - percent_of_ecommsites_using_each_tag_managers.sql
+* Consent Management Platform adoption - percent_of_ecommsites_using_cmp.sql, percent_of_ecommsites_using_cmp_aug21.sql, percent_of_ecommsites_using_cmp_sep21.sql
+* AMP usage on ecommerce sites (mobile). - pct_ampusage_bydevice_vendor.sql
+* Web Push Notification acceptance rates - webpushstats_ecommsites.sql
+* Top "JavaScript frameworks" - top_jsframework_providers_by_device.sql
+* Top "JavaScript libraries" category - top_jslibs_by_device.sql
+* Top CMS technology category - top_cms_by_device.sql
+* Top "Page Builders” technology category - top_pagebuilders_bydevice.sql
+* Top “A/B testing” technology category. - top_abtesting_bydevice.sql
+* Top “Personalisation” technology category - top_personalisation_bydevice.sql
+* Top “Loyalty & Rewards” technology category - top_loyaltyandrewards_bydevice.sql
+* Median lighthouse scores for ecommerce - median_lighthouse_score_ecommsites.sql
+* Ecommerce sites using hreflang value through headers - percent_of_ecommsites_using_hreflang_value_headers.sql
+* Ecommerce sites using hreflang value through link rel - percent_of_ecommsites_using_hreflang_value_link.sql
+* Presence of `Content-Security-Policy` and `Content-Security-Policy-Report-Only` on `Ecommerce` sites - percent_of_ecommsites_csp.sql
+* App links association - android_ios_app_links_ecomm_sites.sql
+* Ecomm covid growth: 2020-2021 - ecomm_covid_growth.sql
+* A11y usage on Ecomm sites - percent_of_ecommsites_using_a11y_solutions.sql
+* Webpush adoption stats - webpush_adoption_by_ecommsites.sql
+
+Unused queries
+
+* percent_of_ecommsites_using_each_a11y_solutions.sql
+* percent_of_ecommsites_using_each_cmp.sql
+* percent_of_ecommsites_using_each_payment_processors.sql
+* top_adplatform_bydevice_vendor.sql
+* top_adplatform_bydevice_vendor_wapp.sql
+* top_analytics_bydevice_vendor.sql
+* top_cdn_bydevice.sql
+* top_cdn_bydevice_vendor_cdn.sql
+* top_cdn_bydevice_vendor_wapp.sql
+* pct_3pusage_bydevice_vendor.sql
+* pct_3pusage_bydevice_vendor_category.sql
+* pagestats_image_dimensions_bydevice.sql

sql/2021/ecommerce/android_ios_app_links_ecomm_sites.sql

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+#standardSQL
+# This query uses custom metric '_well-known' - https://github.com/HTTPArchive/legacy.httparchive.org/blob/master/custom_metrics/well-known.js
+# Note that in this query, there is a subtle bug where the site could have empty /.well-known/assetlinks.json or /.well-known/apple-app-site-association files which will lead to over counting sites with native app links
+# an example is: https://www.allbirds.com/.well-known/assetlinks.json which has a payload of "[]"
+# To fix this, this would require response body parsing on well-known.js
+
+SELECT
+  client,
+  COUNTIF(android_app_links) AS android_app_links,
+  COUNTIF(ios_universal_links) AS ios_universal_links,
+  COUNT(0) AS total,
+  COUNTIF(android_app_links) / COUNT(0) AS pct_android_app_links,
+  COUNTIF(ios_universal_links) / COUNT(0) AS pct_ios_universal_links
+FROM (
+  SELECT DISTINCT
+    _TABLE_SUFFIX AS client,
+    url
+  FROM
+    `httparchive.technologies.2021_07_01_*`
+  WHERE
+    category = 'Ecommerce' AND
+    (app != 'Cart Functionality' AND
+     app != 'Google Analytics Enhanced eCommerce'))
+JOIN (
+  SELECT
+    _TABLE_SUFFIX AS client,
+    url,
+    JSON_VALUE(JSON_EXTRACT_SCALAR(payload, '$._well-known'), '$."/.well-known/assetlinks.json".found') = 'true' AS android_app_links,
+    JSON_VALUE(JSON_EXTRACT_SCALAR(payload, '$._well-known'), '$."/.well-known/apple-app-site-association".found') = 'true' AS ios_universal_links
+  FROM
+    `httparchive.pages.2021_07_01_*`)
+USING
+  (client, url)
+GROUP BY
+  client
+ORDER BY
+  client
+

sql/2021/ecommerce/core_web_vitals_distribution_byvendor_bydevice.sql

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+#standardSQL
+# Core Web Vitals distribution by Ecommerce vendor
+#
+# Note that this is an unweighted average of all sites per Ecommerce vendor.
+# Performance of sites with millions of visitors as weighted the same as small sites.
+SELECT
+  client,
+  ecomm,
+  COUNT(DISTINCT origin) AS origins,
+  SUM(fast_lcp) / (SUM(fast_lcp) + SUM(avg_lcp) + SUM(slow_lcp)) AS good_lcp,
+  SUM(avg_lcp) / (SUM(fast_lcp) + SUM(avg_lcp) + SUM(slow_lcp)) AS ni_lcp,
+  SUM(slow_lcp) / (SUM(fast_lcp) + SUM(avg_lcp) + SUM(slow_lcp)) AS poor_lcp,
+  SUM(fast_fid) / (SUM(fast_fid) + SUM(avg_fid) + SUM(slow_fid)) AS good_fid,
+  SUM(avg_fid) / (SUM(fast_fid) + SUM(avg_fid) + SUM(slow_fid)) AS ni_fid,
+  SUM(slow_fid) / (SUM(fast_fid) + SUM(avg_fid) + SUM(slow_fid)) AS poor_fid,
+  SUM(small_cls) / (SUM(small_cls) + SUM(medium_cls) + SUM(large_cls)) AS good_cls,
+  SUM(medium_cls) / (SUM(small_cls) + SUM(medium_cls) + SUM(large_cls)) AS ni_cls,
+  SUM(large_cls) / (SUM(small_cls) + SUM(medium_cls) + SUM(large_cls)) AS poor_cls
+FROM
+  `chrome-ux-report.materialized.device_summary`
+JOIN (
+  SELECT DISTINCT
+    _TABLE_SUFFIX AS client,
+    url,
+    app AS ecomm
+  FROM
+    `httparchive.technologies.2021_07_01_*`
+  WHERE
+    category = 'Ecommerce' AND
+    (app != 'Cart Functionality' AND
+     app != 'Google Analytics Enhanced eCommerce'))
+ON
+  CONCAT(origin, '/') = url AND
+  IF(device = 'desktop', 'desktop', 'mobile') = client
+WHERE
+  date = '2021-07-01'
+GROUP BY
+  client,
+  ecomm
+ORDER BY
+  origins DESC

sql/2021/ecommerce/core_web_vitals_passingmetrics_byvendor_bydevice.sql

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+#standardSQL
+# CrUX Core Web Vitals performance of Ecommerce vendors by device
+CREATE TEMP FUNCTION IS_GOOD (good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
+  good / (good + needs_improvement + poor) >= 0.75
+);
+
+CREATE TEMP FUNCTION IS_NON_ZERO (good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
+  good + needs_improvement + poor > 0
+);
+
+
+SELECT
+  client,
+  ecomm,
+  COUNT(DISTINCT origin) AS origins,
+  # Origins with good LCP divided by origins with any LCP.
+  SAFE_DIVIDE(
+    COUNT(DISTINCT IF(IS_GOOD(fast_lcp, avg_lcp, slow_lcp), origin, NULL)),
+    COUNT(DISTINCT IF(IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp), origin, NULL))) AS pct_good_lcp,
+
+  # Origins with good FID divided by origins with any FID.
+  SAFE_DIVIDE(
+    COUNT(DISTINCT IF(IS_GOOD(fast_fid, avg_fid, slow_fid), origin, NULL)),
+    COUNT(DISTINCT IF(IS_NON_ZERO(fast_fid, avg_fid, slow_fid), origin, NULL))) AS pct_good_fid,
+
+  # Origins with good CLS divided by origins with any CLS.
+  SAFE_DIVIDE(
+    COUNT(DISTINCT IF(IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL)),
+    COUNT(DISTINCT IF(IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL))) AS pct_good_cls,
+
+  # Origins with good LCP, FID, and CLS dividied by origins with any LCP, FID, and CLS.
+  SAFE_DIVIDE(
+    COUNT(DISTINCT IF(
+      IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AND
+      (NOT IS_NON_ZERO(fast_fid, avg_fid, slow_fid) OR IS_GOOD(fast_fid, avg_fid, slow_fid)) AND
+      IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL)),
+    COUNT(DISTINCT IF(
+      IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp) AND
+      IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL))) AS pct_good_cwv
+FROM
+  `chrome-ux-report.materialized.device_summary`
+JOIN (
+  SELECT
+    _TABLE_SUFFIX AS client,
+    url,
+    app AS ecomm
+  FROM
+    `httparchive.technologies.2021_07_01_*`
+  WHERE
+    category = 'Ecommerce' AND
+    (app != 'Cart Functionality' AND
+      app != 'Google Analytics Enhanced eCommerce'))
+ON
+  CONCAT(origin, '/') = url AND
+  IF(device = 'desktop', 'desktop', 'mobile') = client
+WHERE
+  date = '2021-07-01'
+GROUP BY
+  client,
+  ecomm
+ORDER BY
+  origins DESC
+

sql/2021/ecommerce/ecomm_covid_growth.sql

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+#standardSQL
+# 13_03: Timeseries to show eCommerce growth acceleration due to Covid-19
+# Excluding apps which are not eCommerce platforms/vendors themselves but are used to identify eCommerce sites. These are signals added in Wappalyzer in 2020 to get better idea on % of eCommerce sites but these are not relevant for vendor % market share analysis
+SELECT
+  IF(ENDS_WITH(_TABLE_SUFFIX, '_desktop'), 'desktop', 'mobile') AS client,
+  COUNT(DISTINCT url) AS freq,
+  total,
+  COUNT(DISTINCT url) / total AS pct,
+  2021 AS year,
+  LEFT(_TABLE_SUFFIX, 2) AS month
+FROM
+  `httparchive.technologies.2021_*`
+JOIN
+  (SELECT
+      _TABLE_SUFFIX,
+      COUNT(DISTINCT url) AS total
+    FROM
+      `httparchive.summary_pages.2021_*`
+    GROUP BY
+      _TABLE_SUFFIX)
+USING (_TABLE_SUFFIX)
+WHERE
+  category = 'Ecommerce'
+GROUP BY
+  client,
+  year,
+  month,
+  total
+
+UNION ALL
+
+SELECT
+  IF(ENDS_WITH(_TABLE_SUFFIX, '_desktop'), 'desktop', 'mobile') AS client,
+  COUNT(DISTINCT url) AS freq,
+  total,
+  COUNT(DISTINCT url) / total AS pct,
+  2020 AS year,
+  LEFT(_TABLE_SUFFIX, 2) AS month
+FROM
+  `httparchive.technologies.2020_*`
+JOIN
+  (SELECT
+      _TABLE_SUFFIX,
+      COUNT(DISTINCT url) AS total
+    FROM
+      `httparchive.summary_pages.2020_*`
+    GROUP BY
+      _TABLE_SUFFIX)
+USING (_TABLE_SUFFIX)
+WHERE
+  category = 'Ecommerce'
+GROUP BY
+  client,
+  year,
+  month,
+  total
+
+ORDER BY
+  year DESC,
+  month DESC

sql/2021/ecommerce/ecomm_vendors_covid_growth.sql

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+#standardSQL
+# 13_04: Timeseries to show eCommerce vendors growth acceleration due to Covid-19
+# Excluding apps which are not eCommerce platforms/vendors themselves but are used to identify eCommerce sites. These are signals added in Wappalyzer in 2020 to get better idea on % of eCommerce sites but these are not relevant for vendor % market share analysis
+# Limiting to top 5000 records to continue further analysis in Google Sheets. Using HAVING clauses based on 'pct' results in missing data for certain months
+SELECT
+  IF(ENDS_WITH(_TABLE_SUFFIX, '_desktop'), 'desktop', 'mobile') AS client,
+  app,
+  COUNT(DISTINCT url) AS freq,
+  total,
+  COUNT(DISTINCT url) / total AS pct,
+  LEFT(_TABLE_SUFFIX, 4) AS year,
+  SUBSTR(_TABLE_SUFFIX, 6, 2) AS month
+FROM
+  `httparchive.technologies.*`
+JOIN
+  (SELECT
+      _TABLE_SUFFIX,
+      COUNT(DISTINCT url) AS total
+    FROM
+      `httparchive.summary_pages.*`
+    GROUP BY
+      _TABLE_SUFFIX)
+USING (_TABLE_SUFFIX)
+WHERE
+  category = 'Ecommerce' AND
+  (app != 'Cart Functionality' AND
+   app != 'Google Analytics Enhanced eCommerce')
+GROUP BY
+  client,
+  app,
+  year,
+  month,
+  total
+ORDER BY
+  pct DESC,
+  client DESC,
+  app DESC
+LIMIT 5000

sql/2021/ecommerce/median_lighthouse_score_ecommsites.sql

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+#standardSQL
+# 13_20: Lighthouse category scores per eCommerce plaforms. Web Almanac run LightHouse only in mobile mode and hence references to mobile tables
+SELECT
+  app AS ecommVendor,
+  COUNT(DISTINCT url) AS freq,
+  APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(report, '$.categories.performance.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_performance,
+  APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(report, '$.categories.accessibility.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_accessibility,
+  APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(report, '$.categories.pwa.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_pwa,
+  APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(report, '$.categories.seo.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_seo,
+  APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(report, '$.categories.best-practices.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_best_practices
+FROM
+  `httparchive.lighthouse.2021_07_01_mobile`
+JOIN
+  `httparchive.technologies.2021_07_01_mobile`
+USING
+  (url)
+WHERE
+  category = 'Ecommerce' AND
+  (app != 'Cart Functionality' AND
+   app != 'Google Analytics Enhanced eCommerce')
+GROUP BY
+  ecommVendor
+ORDER BY
+  freq DESC

sql/2021/ecommerce/pagestats_html_bydevice.sql

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+#standardSQL
+# 13_07: Distribution of HTML kilobytes per page
+SELECT
+  _TABLE_SUFFIX AS client,
+  percentile,
+  APPROX_QUANTILES(bytesHtml, 1000)[OFFSET(percentile * 10)] / 1024 AS requests
+FROM
+  `httparchive.summary_pages.2021_07_01_*`
+JOIN
+  `httparchive.technologies.2021_07_01_*`
+USING (_TABLE_SUFFIX, url),
+  UNNEST([10, 25, 50, 75, 90, 100]) AS percentile
+WHERE
+  category = 'Ecommerce' AND
+  app != 'Cart Functionality' AND
+  app != 'Google Analytics Enhanced eCommerce'
+GROUP BY
+  percentile,
+  client
+ORDER BY
+  percentile,
+  client

sql/2021/ecommerce/pagestats_image_bydevice.sql

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+#standardSQL
+# 13_06: Distribution of image stats for 2021
+SELECT
+  percentile,
+  _TABLE_SUFFIX AS client,
+  APPROX_QUANTILES(reqImg, 1000)[OFFSET(percentile * 10)] AS image_count,
+  APPROX_QUANTILES(bytesImg, 1000)[OFFSET(percentile * 10)] / 1024 AS image_kbytes
+FROM
+  `httparchive.summary_pages.2021_07_01_*`
+JOIN (
+  SELECT DISTINCT
+    _TABLE_SUFFIX,
+    url
+  FROM `httparchive.technologies.2021_07_01_*`
+  WHERE category = 'Ecommerce' AND
+    (app != 'Cart Functionality' AND
+     app != 'Google Analytics Enhanced eCommerce'))
+USING (_TABLE_SUFFIX, url),
+  UNNEST([10, 25, 50, 75, 90, 100]) AS percentile
+GROUP BY
+  percentile,
+  client
+ORDER BY
+  percentile,
+  client