The book on email metrics that distinguishes the numbers that move from the numbers that look impressive in dashboards. Open rates have been broken since iOS 15 (Apple's privacy fetch inflates them); the metric you actually need is one or two layers downstream. Covers the working metric hierarchy, the deliverability signals you should actually be tracking, the cohort analysis that shows whether your list is getting healthier or just getting larger, and the revenue-attribution model for email that handles the long sales cycle. Built for the email marketer or growth lead ready to engage with what email actually measures in 2026.

The structured audit for any email program's metrics setup. Walks through the deliverability monitoring (the signals that actually predict inbox placement), the engagement metrics that survive iOS 15 (clicks, downstream conversion), the list-health cohort analysis (whether the list is compounding or churning silently), and the revenue attribution model. Run quarterly. Most programs discover at least three metrics they were 'tracking' that don't actually predict anything useful.

The detailed framework for building an email metrics dashboard that produces decisions. Covers the metric hierarchy from business outcomes down to per-email proxies, the specific calculations (and the calculation traps most teams fall into), the cadence at which each metric should actually be reviewed (daily, weekly, monthly, quarterly), and the dashboard design that makes the right action obvious. Worked example through one full email program's measurement build. Pair with the toolstack for platform-specific implementation.

Working prompts for the AI-assisted parts of email analytics: the campaign post-mortem from raw performance data, the list-health diagnostic from engagement trends, the subject-line variant analyser, the deliverability incident triage. Each prompt comes with the input format and the output format expected. Tested across Claude and ChatGPT. The value is in turning analytics work from 'someone has to spend Friday afternoon on this' into a 15-minute structured exercise.

The platforms that compose into a working email analytics stack: ESP-native reporting (and where it's lying to you), the deliverability monitoring layer (Glock Apps, Inbox Monster, the tier above ESP-native), the BI layer for the cross-campaign analysis, the attribution tooling for revenue connection. Names specific tools at specific list sizes with the integration patterns that hold up. Includes the honest take on which 'AI email analytics' tools are useful and which are wrappers around the same data your ESP already shows.

Most email metrics get ignored because the operator doesn’t know which numbers actually predict revenue and which are dashboard noise. The workbook format installs the discipline: the operator does the actual metric work on their actual campaigns and ends with a real measurement system. This workbook walks the practice: the metric-definition exercises that pick the few numbers worth tracking (versus all 27 the platform reports), the calculation exercises for the math that actually matters (revenue per send, list-growth velocity, lifecycle progression rates), the goal-setting and benchmarking work matched to the operator’s specific list and category, the campaign-optimization exercises with real before-and-after, the long-term performance-tracking setup that holds across quarters, and the reporting-to-stakeholders practice that translates metrics into the language the rest of the team cares about. Pair with the email-metrics ebook for the strategic frame; this workbook is the install layer.