Let Windsor create the tables automatically on the first import<\/strong>: once the dataset is set in Windsor’s configuration, the tool generates the raw table itself and populates it on every sync, with no need to define the schema manually.<\/li>\n<\/ol>\n![\"Creation](\"http:\/\/hgency.be\/wp-content\/uploads\/2026\/08\/creation-table-big-query-final-1024x493.jpg\")
<\/p>\n
This raw table, once populated, is what serves as the starting point for structuring the data into views (ads_segmented<\/code>, ads_kpis<\/code>…), covered in detail in the next step.<\/p>\nSetting up the Windsor connectors<\/h3>\n
Once your Windsor.ai account is created, connecting the sources happens platform by platform: you authorize access to the various ad and analytics accounts through standard OAuth authentication. Windsor then lets you choose which fields to import (impressions, clicks, cost, conversions…) and how often to sync.<\/p>\n
For my reports, I import by default: date, source, spend, impressions, clicks, conversions, campaign ID, and campaign labels. With these KPIs, I have what I need for weekly tracking. Calculated metrics (CPC, CPA, and conversion rate) are computed directly in Looker Studio.<\/p>\n
![\"Cr\u00e9ation](\"http:\/\/hgency.be\/wp-content\/uploads\/2026\/08\/import-windsor-final.jpg\")
<\/p>\n
Once the connectors are configured, the data flows automatically into a single table, ready to be used in BigQuery.<\/p>\n
<\/span>Step 2: Structuring the data in BigQuery<\/span><\/h2>\nData imported from Windsor is first stored in ads_raw. I then pass it through three successive views to enrich the data:<\/p>\n
\n- ads_segmented<\/strong> enriches each raw row with business segmentation \u2014 language, campaign type, site \u2014 extracted from campaign labels, without changing the level of detail.<\/li>\n
- ads_kpis<\/strong> aggregates this enriched data by day, source, and segment, moving from row-by-row detail to consolidated totals.<\/li>\n
- ads_suivi_unpivot<\/strong> recalculates the totals over rolling periods (7 days, 14 days, 28 days, last month, last 6 months \u2014 the reference periods for my reports) and restructures them into rows so Looker Studio can display them easily.<\/li>\n<\/ul>\n
Each view builds on the previous one: the chain is linear, which makes it possible to debug it step by step rather than having to manage everything in a single, complex query.<\/p>\n
The raw table (ads_raw)<\/h3>\n
This is the table that Windsor populates automatically on every sync \u2014 one row per day, per campaign, per platform, with the raw fields defined in the connector’s configuration.<\/p>\n
Segmentation by channel\/campaign (ads_segmented)<\/h3>\n
In this view, I enrich each row with segmentation extracted from campaign labels. I use the labels to categorize my campaigns by language, campaign type, keywords, and so on.<\/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>\nTwo points worth watching here, based on experience:<\/p>\n
\n- Cost<\/strong>: depending on the platform, Windsor may route spend to spend rather than cost \u2014 COALESCE prevents data from being lost.<\/li>\n
- Typing<\/strong>: imported fields sometimes come through as BIGNUMERIC, a type Looker Studio doesn’t recognize as a metric. A CAST(… AS FLOAT64) at this stage fixes the problem for good.<\/li>\n<\/ul>\n
Consolidated KPIs view (ads_kpis)<\/h3>\n
This view aggregates ads_segmented by day, source, and 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>\nMetrics like CPC or CPA are deliberately not calculated here: computing them directly in Looker Studio (via SUM(cost)\/SUM(clicks)) prevents them from being skewed by aggregation \u2014 a ratio pre-calculated upstream ends up being summed rather than recalculated once it’s displayed across multiple rows.<\/p>\n
Unpivoting for time-based reporting (ads_suivi_unpivot)<\/h3>\n
Final step: recalculate the totals over rolling periods, then transform them from columns into rows so Looker Studio can work with them easily.<\/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>\nImportant point: the “last month” period deliberately excludes the current month via DATE_TRUNC \u2014 otherwise, an incomplete month would skew the comparison with previous months.<\/p>\n
<\/span>Step 3: Creating the Looker Studio dashboard<\/span><\/h2>\nTo bring the data into Looker Studio, simply add a new data source via the native BigQuery connector, then select the project, dataset, and view to connect \u2014 typically ads_suivi_unpivot, which already contains the comparison periods ready to use.<\/p>\n
![\"Ajouter](\"http:\/\/hgency.be\/wp-content\/uploads\/2026\/08\/connecteur-donnees-BigQuery-final.jpg\")
<\/p>\n
Periods already prepared, thanks to ads_suivi_unpivot<\/h3>\n
All the period calculation logic \u2014 last 7 days, last 14 days, last 28 days, last month, last 6 months \u2014 has already been prepared in step 2, in the ads_suivi_unpivot view. That means there’s nothing left to calculate on the Looker Studio side: you just need to filter or group by the periode field to display the desired comparison, without setting up a custom date range for every chart.<\/p>\n
This is what makes it possible, for example, to build a table comparing the last 7 days to last month side by side, simply by selecting the two corresponding periode values \u2014 without writing any date logic in Looker Studio itself.<\/p>\n
Calculating metrics with SUM() instead of pre-calculating them<\/h3>\n
Metrics like CPC, CPA, or conversion rate are calculated directly in Looker Studio using calculated fields, for example:<\/p>\n
CPC = SUM(cost) \/ SUM(clicks)<\/em><\/p>\nThis choice isn’t trivial: a metric pre-calculated in BigQuery (like an already-divided CPA) ends up being summed rather than recalculated once it’s displayed across multiple aggregated rows \u2014 and the resulting total no longer makes sense. Using SUM() on the raw values (cost, clicks, conversions) inside the calculated field guarantees the ratio stays correct, no matter what level of aggregation is displayed.<\/p>\n
Building tables and charts<\/h3>\n
From here, you have everything you need to build your tracking tables and charts to check the performance of each campaign or campaign segment in real time. Here are a few examples of tables I check daily:<\/p>\n
\n- CPA trend over time<\/strong> \u2014 a line chart across the different rolling periods (7d, 14d, 28d), to quickly spot a drift<\/li>\n
- Budget split by platform<\/strong> \u2014 a pie chart or stacked bar chart on source, to see at a glance where the spend is going<\/li>\n
- Cross-tab by segment<\/strong> \u2014 langue or type_campagne as the dimension, with CPC\/CPA\/conversion rate as calculated metrics, to compare segments against each other<\/li>\n<\/ul>\n
<\/span>Results: what this changes day to day<\/span><\/h2>\nTime saved on client reporting<\/h3>\n
What used to require multiple manual exports per platform and per client now comes down to a dashboard that’s always up to date, with no manual work involved. The time freed up gets reinvested in analyzing campaigns rather than compiling them.<\/p>\n
Faster detection of tracking anomalies<\/h3>\n
With data centralized and compared across several rolling periods, an anomaly \u2014 a campaign that drops off, an unusual cost spike, a conversion that disappears \u2014 stands out at a glance, rather than being discovered in a monthly export.<\/p>\n
A custom report, accessible directly by the client<\/h3>\n
This architecture also makes it possible to build, from the same views, a dedicated Looker Studio report shared directly with the client \u2014 with their own filters, their own segments, without giving them access to the ad accounts or to BigQuery. The client keeps an autonomous, up-to-date view of their performance, without depending on a manually sent report.<\/p>\n
<\/span>How much does a centralized marketing dashboard cost<\/span><\/h2>\nOn this stack, the only real cost is Windsor.ai \u2014 BigQuery and Looker Studio stay free given the data volumes involved here (a few million rows per month, well within BigQuery’s free quotas).<\/p>\n
For Windsor, the price mainly depends on the number of ad accounts to connect. In my case, managing around fifteen clients with several accounts each, I’m on a higher-tier plan, billed annually. But for a single company centralizing its own campaigns (Google Ads, Meta, Microsoft…), the base plan at \u20ac25 is more than enough. It covers 75 accounts across 3 different sources, which leaves plenty of room even with several platforms and several accounts per platform.<\/p>\n
That’s the full stack \u2014 adapt it to your own platforms and needs. Happy reporting!<\/p>\n","protected":false},"excerpt":{"rendered":"
Centralize Your Google Ads, Meta, and Microsoft Campaigns in Looker Studio (Complete Guide) The goal: a centralized dashboard, always up to date and accessible anywhere Managing several clients across Google Ads, Meta, and Microsoft, I was spending a lot of time juggling between interfaces and compiling exports by hand. I needed a single place to…<\/p>\n","protected":false},"author":1,"featured_media":5848,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[128],"tags":[],"class_list":["post-5866","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-web-analytics"],"yoast_head":"\n
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