BUILDER · charts & dashboards built live · data source: built-in synthetic campaign sample (not connected to QuickSight datasets)
Ask a question about the campaign data.
The assistant picks a chart and draws it here. Export it or save it.
Compare opens vs clicks by year
Which audience has the most unsubscribes?
Show click rate trend over time
Top 5 campaigns by opens
One question → one chart. Several lines → a dashboard (one panel each). ⌘/Ctrl+Enter or Build.
Saved Analyses
Charts and dashboards saved from the Builder.
Dashboard Links
Define navigation between dashboards. A link adds an "Open →" action to the source dashboard's charts — click a data point to jump. Managed here, no DevOps needed.
★ Recommended links for this dashboard
→
Defined links
Link audit admin
Scans dashboards for links authored inside them (text-box hyperlinks, URL actions) that point at the QuickSight console. Viewed here, those open the console login page and the app can't intercept them. Remove them in QuickSight Studio, then recreate the navigation as an in-app link.
Q Topics
Every Q Topic available in this account. Topics remain accessible here even when they aren't linked to a dashboard.
No Q Topics found.
Real campaign data. Engagement rates from 20,000 campaign sends (2021–2024). We lead with rates — open rate, click-through, unsubscribe rate — because they're the reliable signal; absolute volumes in the source are synthetic-scale and left out on purpose. Filter by campaign or audience to slice the trend. Data source: QC campaign data (aggregated engagement rates), built into the app — not a live QuickSight dataset.
Open rate by year
Email content characteristics. Extracted from 1,244 QC emails (subject + body feature pipeline): sentiment, tone, readability, structure. Descriptive for now — tying these to engagement is pending a data join, but the content patterns themselves are real. Data source: QC email content features (1,244 emails), built into the app.
⚠ Demonstration — synthetic signal. The QC campaign data has no real relationship between content and engagement, so to show how driver analysis works, a realistic signal was injected into a copy of the data. The correlations below are honestly computed — but on constructed data, not live campaign performance. This illustrates the analysis for review; it is not a finding about real campaigns. r is the correlation coefficient between that feature and open rate, ranging from -1 to +1: positive means the feature tends to rise with open rate, negative means it tends to fall, and values closer to 0 mean a weaker relationship. Data source: a synthetic copy of the QC campaign data, built into the app.
Ask about the campaign data. Question the real aggregated rates in plain language. Answers are grounded strictly in the data — Claude only cites numbers we computed and will say when something can't be answered or when engagement is steady across the board. Data source: the same aggregated QC campaign rates as Trends.
Delivery & bounce analysis. Real campaign data — delivery and failure rates across 1,244 sends. This is the "reduce rejection" view: overall delivery health, where bounces concentrate, and outlier sends worth investigating. Data source: QC delivery and bounce data (1,244 sends), built into the app.