A Data Studio marketing dashboard for every channel you pay for

What goes into a multi-channel Data Studio marketing dashboard?

It combines ad spend, site behavior, organic search and CRM outcomes in one place. Our marketing builds usually pull from Google Ads, Meta, LinkedIn and TikTok for paid media, GA4 for sessions and key events, Search Console for queries and clicks, and HubSpot, Salesforce or a CRM export for leads and deals.

Each platform names things differently: Meta calls it amount spent, Google Ads calls it cost, LinkedIn calls it total spent. Before any chart is built, we map every source to one shared schema (date, channel, campaign, spend, impressions, clicks, conversions) so totals add up and filters work across all of them.

• Paid media: Google Ads, Meta, LinkedIn, TikTok.
• Web and search: GA4, Search Console.
• Outcomes: CRM leads, opportunities, closed revenue.

How do you calculate blended CPA across channels?

Blended CPA is total spend across every paid channel divided by total conversions from one agreed source of truth. The hard part is choosing that source, since each ad platform claims credit for the same lead.

We usually take spend from the ad platforms and conversions from the CRM or GA4, never from the ad platforms themselves, so a lead is counted once. Platform-reported conversions stay on the channel pages for optimization, clearly labeled, while the summary page uses the deduplicated figure. A calculated field with SUM(spend) / SUM(conversions) at the right aggregation level avoids the classic mistake of averaging per-row CPAs.

How do you connect ad spend to pipeline and revenue?

We join CRM records to campaigns through UTM parameters captured on the lead form. When source, medium and campaign are stored on the contact, each opportunity can be traced back to a channel and a campaign.

That turns the dashboard from a traffic report into a pipeline report: cost per qualified lead, pipeline value by channel, and revenue per dollar spent. Where UTM capture is missing or inconsistent, we tell you during the review, because no dashboard can fix attribution data that was never recorded.

Should marketing data go through blends or BigQuery?

Small accounts can run on blends; larger ones should land in BigQuery. A blend joins at most 5 data sources with equality joins only, and every chart re-queries every source, which slows pages and burns API quota.

GA4 is the common bottleneck. The standard Data API quota is 14,000 tokens per project per property per hour, and a dashboard with many charts and many viewers hits it quickly. When we see quota errors or a report taking more than a few seconds per page, we load channel data into partitioned BigQuery tables through a partner connector or export, and point the dashboard at one tidy table.

Which partner connectors do you use for Meta, LinkedIn and TikTok?

Google's native connectors cover Google Ads, GA4, Search Console and YouTube. For Meta, LinkedIn, TikTok and most CRMs we use partner connectors such as Supermetrics, Windsor.ai, Porter Metrics, Funnel, Power My Analytics or Catchr.

The right choice depends on how many accounts you run and whether the data goes straight into Data Studio or into BigQuery first. Vendor pricing changes, so we compare current options with you rather than quoting them here.

What does a marketing leader see on the summary page?

One page answers whether marketing is working this month: spend, leads, blended CPA, pipeline and revenue, each against target and the previous period. Below that sits a channel table with the same metrics per channel, so a weak channel stands out without scrolling.

Drill-down pages follow for each channel, then a campaign table with filters for date, channel and region. A parameter lets the team change the target CPA without editing any chart, and scheduled email delivery sends the summary to leadership every Monday.

Why do marketing dashboards lose the team's trust?

Most lose trust because two people get two different numbers for the same thing. A paid social manager quotes Meta's conversions, the sales team quotes the CRM, and the dashboard shows a third figure nobody can explain.

We prevent that in three ways. First, every metric has one written definition and one source, shown in an info icon next to the chart. Second, campaign naming rules are agreed before launch, so a CASE statement can sort campaigns into brand, non-brand, prospecting and retargeting reliably. Third, a reconciliation page compares dashboard totals with each platform for the last closed month, so differences are explained before anyone asks.

Broken connectors are the other trust killer. Data Studio does not warn anyone when a source stops refreshing, so we add a freshness check that shows the latest date loaded for each channel at the top of the summary page.