ads_suivi_unpivot<\/strong> recalcule les totaux sur des p\u00e9riodes glissantes (7 jours, 14 jours, 28 jours, mois dernier, 6 derniers mois \u2013 les r\u00e9f\u00e9rences pour mes rapports) et restructure en lignes pour que Looker Studio puisse les afficher facilement.<\/li>\n<\/ul>\nChaque vue s’appuie sur la pr\u00e9c\u00e9dente : la cha\u00eene est lin\u00e9aire, ce qui permet de la d\u00e9boguer \u00e9tape par \u00e9tape plut\u00f4t que de tout g\u00e9rer dans une seule requ\u00eate complexe.<\/p>\n
La table brute (ads_raw)<\/h3>\n
C’est la table que Windsor alimente automatiquement \u00e0 chaque synchronisation \u2014 une ligne par jour, par campagne, par plateforme, avec les champs bruts d\u00e9finis \u00e0 la configuration du connecteur.<\/p>\n
Segmentation par canal\/campagne (ads_segmented)<\/h3>\n
Dans cette vue j\u2019enrichis chaque ligne avec la segmentation extraite des labels de campagne.<\/p>\n
J\u2019utilise les labels pour cat\u00e9goriser mes campagnes par langue, type de campagne, mots-cl\u00e9s etc.<\/p>\n
\r\nCREATE VIEW `mon_projet.mon_client.ads_segmented` AS\r\nSELECT\r\n date,\r\n source,\r\n campaign_id,\r\n campaign,\r\n COALESCE(cost, CAST(spend AS FLOAT64)) AS cost,\r\n CAST(impressions AS FLOAT64) AS impressions,\r\n CAST(clicks AS FLOAT64) AS clicks,\r\n CAST(conversions AS FLOAT64) AS conversions,\r\n REGEXP_EXTRACT(campaign_labels, r'lang:([a-z]+)') AS langue,\r\n REGEXP_EXTRACT(campaign_labels, r'type:([a-z]+)') AS type_campagne,\r\n REGEXP_EXTRACT(campaign_labels, r'site:([a-z0-9]+)') AS site\r\nFROM `mon_projet.mon_client.ads_raw`\r\n<\/code><\/pre>\nDeux points de vigilance ici, tir\u00e9s de l’exp\u00e9rience :<\/p>\n
\n- Le co\u00fbt<\/strong> : selon la plateforme, Windsor peut router la d\u00e9pense vers spend plut\u00f4t que cost \u2014 le COALESCE \u00e9vite de perdre des donn\u00e9es.<\/li>\n
- Le typage<\/strong> : les champs import\u00e9s arrivent parfois en BIGNUMERIC, un type que Looker Studio ne reconna\u00eet pas comme m\u00e9trique. Un CAST(… AS FLOAT64) \u00e0 ce niveau r\u00e8gle le probl\u00e8me une bonne fois pour toutes.<\/li>\n<\/ul>\n
Vue des KPIs consolid\u00e9s (ads_kpis)<\/h3>\n
Cette vue agr\u00e8ge ads_segmented par jour, source et segment :<\/p>\n
\r\nCREATE VIEW `mon_projet.mon_client.ads_kpis` AS\r\nSELECT\r\n date,\r\n source,\r\n langue,\r\n type_campagne,\r\n site,\r\n SUM(impressions) AS impressions,\r\n SUM(clicks) AS clicks,\r\n SUM(cost) AS cost,\r\n SUM(conversions) AS conversions\r\nFROM `mon_projet.mon_client.ads_segmented`\r\nGROUP BY date, source, langue, type_campagne, site\r\n<\/code><\/pre>\nLes m\u00e9triques comme le CPC ou le CPA ne sont volontairement pas calcul\u00e9es ici : les calculer directement dans Looker Studio (via SUM(cost)\/SUM(clicks)) \u00e9vite qu’elles soient fauss\u00e9es par l’agr\u00e9gation \u2014 un ratio pr\u00e9-calcul\u00e9 en amont se retrouve additionn\u00e9 plut\u00f4t que recalcul\u00e9 d\u00e8s qu’on l’affiche sur plusieurs lignes.<\/p>\n
Unpivot pour le reporting temporel (ads_suivi_unpivot)<\/h3>\n
Derni\u00e8re \u00e9tape : recalculer les totaux sur des p\u00e9riodes glissantes, puis les transformer en lignes plut\u00f4t qu’en colonnes pour que Looker Studio les exploite facilement.<\/p>\n
\r\nCREATE VIEW `mon_projet.mon_client.ads_suivi_unpivot` AS\r\nWITH base AS (\r\n SELECT\r\n source, langue, type_campagne, site,\r\n SUM(IF(date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY), cost, 0)) AS cost_7d,\r\n SUM(IF(date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY), conversions, 0)) AS conversions_7d,\r\n SUM(IF(date >= DATE_TRUNC(DATE_SUB(CURRENT_DATE(), INTERVAL 1 MONTH), MONTH)\r\n AND date < DATE_TRUNC(CURRENT_DATE(), MONTH), cost, 0)) AS cost_m1, SUM(IF(date >= DATE_TRUNC(DATE_SUB(CURRENT_DATE(), INTERVAL 1 MONTH), MONTH)\r\n AND date < DATE_TRUNC(CURRENT_DATE(), MONTH), conversions, 0)) AS conversions_m1\r\n -- ... m\u00eame logique pour 14j, 28j, 6 mois\r\n FROM `mon_projet.mon_client.ads_kpis`\r\n GROUP BY source, langue, type_campagne, site\r\n)\r\nSELECT source, langue, type_campagne, site, periode, cost, conversions\r\nFROM base\r\nUNPIVOT (\r\n (cost, conversions) FOR periode IN (\r\n (cost_7d, conversions_7d) AS '7 derniers jours',\r\n (cost_m1, conversions_m1) AS 'Mois dernier'\r\n -- ... autres p\u00e9riodes\r\n )\r\n)\r\n<\/code><\/pre>\nPoint important : la p\u00e9riode \u00ab\u00a0mois dernier\u00a0\u00bb exclut volontairement le mois en cours via DATE_TRUNC \u2014 sinon, un mois encore incomplet fausserait la comparaison avec les mois pr\u00e9c\u00e9dents.<\/p>\n
<\/span>Etape 3\u00a0: Cr\u00e9er le dashboard Looker Studio<\/span><\/h2>\nPour importer les donn\u00e9es dans Looker Studio, il suffit d\u2019ajouter une nouvelle source de donn\u00e9es via le connecteur BigQuery natif, puis on de s\u00e9lectionner le projet, le dataset et la vue \u00e0 connecter \u2014 g\u00e9n\u00e9ralement ads_suivi_unpivot, qui contient d\u00e9j\u00e0 les p\u00e9riodes de comparaison pr\u00eates \u00e0 l’emploi.<\/p>\n
![\"Ajouter](\"http:\/\/hgency.be\/wp-content\/uploads\/2026\/08\/connecteur-donnees-BigQuery-final.jpg\")
<\/p>\n
Des p\u00e9riodes d\u00e9j\u00e0 pr\u00eates gr\u00e2ce \u00e0 ads_suivi_unpivot<\/h3>\n
Toute la logique de calcul des p\u00e9riodes \u2014 7 derniers jours, 14 derniers jours, 28 derniers jours, mois dernier, 6 derniers mois \u2014 a d\u00e9j\u00e0 \u00e9t\u00e9 pr\u00e9par\u00e9e \u00e0 l’\u00e9tape 2, dans la vue ads_suivi_unpivot. \u00c7a veut dire qu’il n’y a rien \u00e0 recalculer c\u00f4t\u00e9 Looker Studio : il suffit de filtrer ou de grouper sur le champ periode pour afficher directement la comparaison souhait\u00e9e, sans configurer de plage de dates personnalis\u00e9e \u00e0 chaque graphique.<\/p>\n
C’est ce qui permet, par exemple, de construire un tableau comparant les 7 derniers jours au mois dernier c\u00f4te \u00e0 c\u00f4te, simplement en s\u00e9lectionnant les deux valeurs de periode correspondantes \u2014 sans \u00e9crire de logique de date dans Looker Studio lui-m\u00eame.<\/p>\n
Calculer les m\u00e9triques avec SUM() plut\u00f4t que les pr\u00e9-calculer<\/h3>\n
Les m\u00e9triques comme le CPC, le CPA ou le taux de conversion sont calcul\u00e9es directement dans Looker Studio via des champs calcul\u00e9s, par exemple :<\/p>\n
CPC = SUM(cost) \/ SUM(clicks)<\/em><\/p>\nCe choix n’est pas anodin : une m\u00e9trique pr\u00e9-calcul\u00e9e dans BigQuery (comme un CPA d\u00e9j\u00e0 divis\u00e9) se retrouve additionn\u00e9e plut\u00f4t que recalcul\u00e9e d\u00e8s qu’elle est affich\u00e9e sur plusieurs lignes agr\u00e9g\u00e9es \u2014 le total obtenu n’a alors plus de sens. Utiliser SUM() sur les valeurs brutes (co\u00fbt, clics, conversions) dans le champ calcul\u00e9 garantit que le ratio reste correct, quel que soit le niveau d’agr\u00e9gation affich\u00e9.<\/p>\n
Cr\u00e9ation des tableaux et graphiques<\/h3>\n
\u00c0 partir d’ici, tu as tout en main pour cr\u00e9er tes tableaux et graphiques de suivi pour v\u00e9rifier en temps r\u00e9el les performances de chaque campagne ou segment de campagne. Voici quelques exemples de tableaux que je v\u00e9rifie quotidiennement :<\/p>\n
\n- \u00c9volution du CPA dans le temps<\/strong> \u2014 graphique en courbe sur les diff\u00e9rentes p\u00e9riodes glissantes (7j, 14j, 28j), pour rep\u00e9rer rapidement une d\u00e9rive<\/li>\n
- R\u00e9partition du budget par plateforme<\/strong> \u2014 graphique en secteurs ou barres empil\u00e9es sur source, pour voir en un coup d’\u0153il o\u00f9 va la d\u00e9pense<\/li>\n
- Tableau crois\u00e9 par segment<\/strong> \u2014 langue ou type_campagne en dimension, avec CPC\/CPA\/taux de conversion en m\u00e9triques calcul\u00e9es, pour comparer les segments entre eux<\/li>\n<\/ul>\n
<\/span>R\u00e9sultat : ce que \u00e7a change au quotidien<\/span><\/h2>\nGain de temps sur le reporting client<\/strong>:\u00a0Ce qui prenait auparavant plusieurs exports manuels par plateforme et par client se r\u00e9sume d\u00e9sormais \u00e0 un dashboard toujours \u00e0 jour, sans intervention manuelle. Le temps lib\u00e9r\u00e9 est r\u00e9investi dans l’analyse des campagnes plut\u00f4t que dans leur compilation.<\/p>\nD\u00e9tection plus rapide des anomalies de tracking<\/strong>:\u00a0Avec des donn\u00e9es centralis\u00e9es et compar\u00e9es sur plusieurs p\u00e9riodes glissantes, une anomalie \u2014 une campagne qui d\u00e9croche, un pic de co\u00fbt anormal, une conversion qui dispara\u00eet \u2014 se rep\u00e8re en un coup d’\u0153il, plut\u00f4t qu’au d\u00e9tour d’un export mensuel.<\/p>\nUn rapport sur mesure, accessible directement par le client<\/strong>:\u00a0Cette architecture permet aussi de construire, \u00e0 partir des m\u00eames vues, un rapport Looker Studio d\u00e9di\u00e9 et partag\u00e9 directement avec le client \u2014 avec ses propres filtres, ses propres segments, sans lui donner acc\u00e8s aux comptes publicitaires ou \u00e0 BigQuery. Le client garde une vue autonome et \u00e0 jour sur ses performances, sans d\u00e9pendre d’un envoi manuel de rapport.<\/p>\n<\/span>Combien co\u00fbte un dashboard marketing centralis\u00e9<\/span><\/h2>\nSur cette stack, le seul poste de co\u00fbt r\u00e9el est Windsor.ai \u2014 BigQuery et Looker Studio restent gratuits vu les volumes de donn\u00e9es en jeu ici (quelques millions de lignes par mois, largement dans les quotas gratuits de BigQuery).<\/p>\n
Pour Windsor, le prix d\u00e9pend surtout du nombre de comptes publicitaires \u00e0 connecter. Dans mon cas, avec une quinzaine de clients \u00e0 g\u00e9rer et plusieurs comptes par client, je suis sur un abonnement plus \u00e9lev\u00e9, factur\u00e9 \u00e0 l’ann\u00e9e. Mais pour une seule entreprise qui centralise ses propres campagnes (Google Ads, Meta, Microsoft…), le plan de base \u00e0 25\u20ac suffit largement. Il permet de couvrir 75 comptes \u00e0 partir de 3 sources diff\u00e9rentes, ce qui laisse une bonne marge m\u00eame avec plusieurs plateformes et plusieurs comptes par plateforme.<\/p>\n
<\/p>\n
Voil\u00e0 pour la stack compl\u00e8te \u2014 \u00e0 adapter selon vos plateformes et besoins. Bon reporting !<\/p>\n","protected":false},"excerpt":{"rendered":"
Centraliser ses campagnes Google Ads, Meta et Bing dans Looker Studio avec Windsor.ai et BigQuery L’objectif : un dashboard centralis\u00e9, \u00e0 jour et accessible partout Avec plusieurs clients \u00e0 g\u00e9rer sur Google Ads, Meta et Microsoft, je passais beaucoup de temps \u00e0 jongler entre les interfaces et \u00e0 compiler des exports \u00e0 la main. J’avais…<\/p>\n","protected":false},"author":1,"featured_media":5847,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[129],"tags":[],"class_list":["post-5835","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-web-analytics"],"yoast_head":"\n
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