A blended data source is a combined source in Looker Studio that joins fields from two or more data sources into one table, matched on a shared field such as date or campaign name. It lets one chart show, for example, Google Ads spend beside the enquiries GA4 recorded for the same campaigns, which neither source holds on its own.
How a blended data source works
In Looker Studio, you select a chart, choose to blend data and add further sources; at the time of writing (October 2026) a single blend can hold up to five. For each source you pick the dimensions and metrics you want, then set the join keys: the fields whose values must match for rows to be combined. Date is the most common key, often with campaign name alongside it.
You also choose how rows are joined. A left outer join keeps every row from the first source and adds matching rows from the others. An inner join keeps only rows found in every source. Right outer, full outer and cross joins exist too, but are rarely what a marketing report needs. The choice matters: an inner join quietly drops any campaign that spent money but recorded no conversions, which is exactly the campaign you most need to see.
Blends are also how blended metrics get built. Total spend across Google, Meta and Microsoft Ads, divided by total new customers from your CRM, gives a blended CAC: one cost of winning a customer across every channel, free of each platform’s own attribution claims.
Why it matters
A business advertising on two or three platforms gets a separate success story from each. A blend puts cost and outcome side by side, so you can see cost per enquiry by campaign or by month without exporting spreadsheets every Monday. For a business owner or a board, one chart of total spend against total enquiries is often more useful than any platform’s dashboard.
Meta Ads data does not come into Looker Studio through a Google connector, so blends that include it rely on a partner connector such as Supermetrics, or on a scheduled export to Google Sheets or BigQuery. That adds a cost and another point where data can break, which is worth knowing before you promise anyone a live cross-channel dashboard.
Common mistakes
- Join keys that look the same but are not. “Spring_Sale” in Google Ads and “spring_sale” in a utm_campaign tag will not match, so the row disappears or shows zeros. Consistent campaign and UTM naming fixes this at the source.
- Averaging ratios. Conversion rate or cost per lead must be recalculated after blending with a calculated field, such as SUM(cost) divided by SUM(leads), not averaged across rows.
- Mismatched currencies and time zones. A Meta account set up in US dollars or US Pacific time will not line up day by day with a Google Ads account in pounds and London time, and these settings cannot easily be changed once an account exists.
- Joining data held at different levels of detail, such as daily figures in one source and monthly in another, which repeats values and inflates totals.
- Never checking the blended totals against each platform’s own figures.
How to act on it
Keep blends small. Two or three sources joined on date plus one well-controlled field, such as campaign name, cover most reporting needs. Agree a naming convention for campaigns and UTM tags before you build anything, because a join key is only as clean as the names behind it.
Build the blend, then test it: pick one campaign and one week, and check that the blended spend and conversions match what each platform shows on its own. Recalculate every ratio after blending. When a report needs many sources, long date ranges or CRM revenue, move the joining into BigQuery and point Looker Studio at the result. Reporting cost and outcomes across channels against one target is everyday work in performance marketing.