Use cases
Campaign performance analytics
Pull cross-channel campaign reports together and surface what's working — without hours of spreadsheet wrangling.
Performance data lives in seven tools. The weekly campaign review takes someone half a day to pull together and another half day to translate into insight. A campaign analytics Agent does the pulling and the translating — so the review meeting starts with answers, not data hunting.
When this pays off
Reports take half a day
Pulling Google Ads + HubSpot + LinkedIn + GA into one slide takes the same person, the same way, every Monday.
Channel performance is opaque
You know spend by channel but not what each channel is actually doing for the funnel.
Campaign comparisons are manual
Was this campaign better than the last one? Nobody can answer without an hour of cross-referencing.
Leadership wants weekly digests
The CMO wants a Friday “what worked, what didn’t” — and it always slips because it’s not anyone’s job.
The shape of this use case
A campaign analytics Agent takes a time window + campaign set and returns a report with reasoning.
Inputs
Time window, campaign set (specific campaigns or all), channels to include, segmentation cuts.
Output
A digest — top performers, underperformers, key shifts vs. last period, suggested actions — with cited data and reasoning.
Where to start
Two ways in, depending on whether you want something running today or built to your exact spec.
Clone a pre-built Agent
Open the Email Marketing Strategist. More in the Marketplace.
Build your own
Start from scratch in the builder, or by describing it in Claude Code or Cursor with MCP & Plugins.
Either way, these are prompts your team can use on day one:
- “Pull last week’s campaign performance from HubSpot and Google Ads — top 3 winners, top 3 losers, with reasons.”
- “Compare the Q3 onboarding sequence against Q2 — what shifted in open and click rates?”
- “Which channels drove qualified pipeline last month? Cite the data.”
Where to take it
Once it’s running, deepen it in three moves:
Let it improve
Weight the Agent’s evals toward the metrics that actually predicted pipeline.
Common pitfalls
Pulling data without interpreting it
A wall of numbers in Slack isn’t useful. Force the Agent to explain why metrics moved and what to do — not just what they are.
Inconsistent attribution
HubSpot’s “campaign” and your ad platform’s “campaign” probably aren’t the same thing. Define attribution rules in Knowledge once and reference them every time.
No benchmark for 'good'
Without baselines, every CTR looks normal. Upload your historical benchmarks to Knowledge so the Agent flags actual outliers.
Stale data sources
Ad platforms can lag 24-48 hours. Have the Agent disclose data freshness in the report so the team knows what’s preliminary.