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performance-tracking

performance-tracking

github.com/gtmagentsMarketing & growth43070 installsUpdated 30w ago
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About performance-tracking

performance-tracking is a skill for planning and monitoring marketing campaign results, intended for teams responsible for metrics, reporting, and optimization. It covers the tracking process from metric definition through retrospective documentation.

Key features

  1. 01Build business, leading, and diagnostic metric hierarchies
  2. 02Map data sources and standardize IDs and UTMs
  3. 03Plan executive and operator dashboards
  4. 04Set spend, CPA, and conversion alerts
  5. 05Record experiment hypotheses, actions, and outcomes

Use cases

  1. 01Define metrics, data sources, and reporting cadence before launch
  2. 02Monitor pacing and trigger optimizations during campaigns
  3. 03Capture learnings and benchmarks after campaigns

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gtmagents/gtm-agents --skill performance-tracking

After installing, type/performance-trackingin Claude Code to confirm it loaded.

Details

From the project README

Performance Tracking Skill

When to Use

  • Before launch to define metrics, data sources, and reporting cadence.
  • During active campaigns to monitor pacing vs goals and trigger optimizations.
  • Post-campaign retros to capture learnings and benchmarks.

Framework

  1. Metric Hierarchy – define business (pipeline, revenue), leading (MQL, CTR), and diagnostic metrics (CPC, landing page CVR).
  2. Data Plumbing – map sources (ad platforms, MA, CRM, BI) and ensure consistent IDs/UTMs.
  3. Dashboard Build – create executive + operator views with automated refresh.
  4. Alerting – set thresholds for spend pacing, CPA, conversion drops, SLAs.
  5. Insight Cadence – daily standups, weekly deep dives, end-of-campaign retros.
  6. Documentation – record hypotheses, experiments, and outcomes.

Templates

  • KPI tree worksheet.
  • Dashboard spec (dataset, visualization, owner, refresh schedule).
  • Optimization log (date, insight, action, result).

Tips

  • Automate alert routing (Slack, email) so anomalies are actioned quickly.
  • Pair dashboards with narrative summaries to help execs interpret shifts.
  • Capture experiment context in the optimization log to prevent repeating failed tests.
  • Align BI/RevOps early so new metrics get logged before launch.

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