How do you build dashboards combining influencer and paid media metrics?

Standardize a shared metric set (spend, reach, engagement, conversions, cost per result) and enforce consistent UTM and naming so both channels line up.

Centralize both sources in a BI tool via connectors and exports, then design views that compare cost-per-outcome side by side, not just display each.

Flag where attribution differs (paid is tighter, influencer fuzzier) and credit influencer value paid lacks, so one channel does not look better just for being measured more tightly.

The foundation is making the two comparable before you visualize anything, because influencer and paid media are tracked in different places with different native metrics and a dashboard that just pastes them side by side without aligning them misleads. Define a shared metric set that means the same thing for both channels: spend, impressions or reach, engagement, clicks, conversions and the comparison metric that matters most, cost per result (cost per click, per acquisition, per conversion). Then enforce consistency in how data is captured, the same UTM structure and naming conventions across influencer links and paid campaigns, so traffic and conversions attribute cleanly and line up in one place rather than in incompatible silos. Without that discipline, the numbers never reconcile and the combined view is fiction.

For the build itself, the usual approach is a BI or dashboard tool (the kind that pulls from multiple sources) as the hub. Paid media platforms expose their data via native connectors or APIs and influencer data comes in via your influencer platform export, your analytics or manual upload, all flowing into the shared dashboard where you map them to your common metrics. Then design the view to actually compare rather than just display: show each channel cost per result next to the other so you can see which is more efficient, blend reach and conversion so you understand the full funnel each contributes and segment by campaign or objective so comparisons are like-for-like. The honest challenges to plan for: attribution differs between channels (paid has tighter tracking, influencer is fuzzier, especially for awareness and offline effect), so flag where numbers are precise versus directional rather than implying false equivalence and account for influencer value that paid does not have (earned reach, content you can reuse, authenticity) which a pure cost-per-click view undersells. So the workflow is: standardize metrics and tracking, centralize both sources in a BI tool and design comparable cost-per-outcome views with honest notes on attribution confidence. Done well, leadership gets a single view that judges channels on equal terms; done carelessly, it makes one channel look better simply because it was measured more tightly.

To be clear, the dashboard itself is built in your BI and analytics stack, not in a discovery tool, so it is outside what Flinque does. The contribution Flinque makes upstream is data quality on the influencer side: cost-per-result comparisons are only fair if the influencer reach was real, so vetting creators for genuine audiences before the campaign, which Flinque does, is what keeps the influencer column in your dashboard honest against paid.