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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.

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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.

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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.

Sources

Marketing automation (HubSpot, Marketo), ad platforms (Google, Meta, LinkedIn), web analytics, CRM, your own benchmark docs.

Output

A digest — top performers, underperformers, key shifts vs. last period, suggested actions — with cited data and reasoning.

Delivery

Posted to Slack, written to a Notion page for the team review, emailed to the CMO Friday morning.

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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 Programmatic GTM.
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.”

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Where to take it

Once it’s running, deepen it in three moves:

Give it a playbook

Shape it with a prompt, KPI definitions in Knowledge, and Tools to query each platform.

Automate it on signals

Wrap it in a workflow that fires on a trigger.

Let it improve

Weight the Agent’s evals toward the metrics that actually predicted pipeline.

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Common pitfalls

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.
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.
Without baselines, every CTR looks normal. Upload your historical benchmarks to Knowledge so the Agent flags actual outliers.
Ad platforms can lag 24-48 hours. Have the Agent disclose data freshness in the report so the team knows what’s preliminary.