Metric recipes & combined targets

The metric picker is just the starting point. The most useful targets often come from combining metrics — either by building a custom metric from raw inputs, or by setting multiple metrics in one plan that tell a fuller story together. This article gives you six concrete recipes and five plan patterns to copy.

Custom metrics in SEINō are simple expressions over your existing fields. The engine supports + − × ÷ and parentheses, and references to any existing native or custom metric. It computes the expression after summing the inputs over your chosen period, so rates like “clicks per send” come out correctly across any time window.

Quick reference

Recipe	Formula	Available
Engagement rate	(opens + clicks_unique) / sent	Universal
List churn rate	unsubscribes / sent	Universal
Negative engagement rate	(unsubscribes + complaints) / sent	Universal
Quality-weighted CTR	clicks_unique / (sent − hard_bounces − soft_bounces)	Universal
Sender-reputation complaint rate	complaints / delivered	Universal
Revenue per opened email	revenue / opens	Needs ESP-attributed revenue

Useful custom metrics

Every formula below works on the universal email-funnel data (sent, delivered, opens, clicks_unique, hard_bounces, soft_bounces, unsubscribes, complaints) — no ESP-specific data required, except where noted. Formulas use the canonical field names; the picker displays the same fields with friendlier labels (for example, clicks_unique appears as Unique clicks).

Engagement rate

(opens + clicks_unique) / sent

Captures total interaction with a campaign in a single number. More forgiving than CTR (which only counts clickers) but more demanding than open rate (which is inflated by Apple Mail Privacy Protection).

Use it when: you want one rolled-up engagement signal — for example, as a quality guardrail on a list-growth plan. Pair it with: unsubscribe rate as a guardrail; complaint rate as a second guardrail.

List churn rate

unsubscribes / sent

Captures how aggressive your send pattern is — what fraction of each send removes itself from the list. Doesn’t depend on knowing total list size.

Use it when: you suspect over-mailing, or when monitoring a re-engagement campaign that may push borderline subscribers off. Pair it with: any primary outcome metric (revenue, CTR).

Negative engagement rate

(unsubscribes + complaints) / sent

A composite warning signal — captures both forms of “I don’t want this” in one number. More sensitive than either alone.

Use it when: you want a single guardrail that catches both forms of list damage. Pair it with: any primary outcome.

Quality-weighted click-through rate

clicks_unique / (sent − hard_bounces − soft_bounces)

CTR computed over the reachable audience instead of gross sends. Strips out the deliverability noise.

Use it when: you suspect deliverability issues are hiding real engagement performance, or you want a “true CTR” for executives. Pair it with: deliverability or bounce rate as a guardrail.

Sender-reputation complaint rate

complaints / delivered

Complaint rate over delivered (rather than over sent) — closer to how mailbox providers actually measure your sender reputation.

Use it when: you want a guardrail that lines up with how Gmail, Yahoo, and others evaluate you. Gmail penalizes bulk senders that cross 0.1% on this measure. Pair it with: any primary outcome.

Revenue per opened email

revenue / opens

Revenue isolated to the audience that actually engaged. Useful for lift analysis — distinguishes “we sent more” from “the engaged audience responded more”.

Use it when: revenue is available from your ESP and you want to measure quality of engagement, not just volume. Pair it with: open rate or engagement rate. Requires an ESP that exposes attributed revenue.

Combined-metric target patterns

The strongest target plans pair a primary outcome with one or two guardrails. Each pattern below is a multi-metric SEINō plan that tells a fuller story than any single metric could.

Pattern	Primary	Guardrails	When to use
Volume + quality	Send volume / frequency	Engagement rate, unsubscribe rate < 0.5%	Scaling output
Engagement plan	CTR or engagement rate	Complaint rate < 0.1%, bounce rate < 2%	Iterating on content or creative
Revenue plan	Revenue or revenue per email	Unsubscribe < 0.5%, complaint < 0.1%, engagement stays ≥ baseline	Promotional or lifecycle programs
List health plan	Engagement rate at baseline	Bounce < 2%, complaint < 0.1%, sender-reputation complaint < 0.1%	Quiet months between campaigns
Recovery plan	Deliverability ≥ 97%	Complaint < 0.05%, bounce trending down	After a deliverability incident

Volume + quality

Primary: send volume or campaign frequency. Guardrails: engagement rate stays ≥ baseline; unsubscribe rate < 0.5%.

Story: grow output without burning the list. Useful when scaling the program — catches the case where more sends produce flat or worse total engagement.

Engagement plan

Primary: click-through rate or engagement rate (custom metric above). Guardrails: complaint rate < 0.1%; bounce rate < 2%.

Story: improve interaction without harming deliverability. Useful for content or creative teams iterating on subject lines and copy.

Revenue plan

Primary: revenue or revenue per email. Guardrails: unsubscribe rate < 0.5%; complaint rate < 0.1%; engagement rate stays ≥ baseline.

Story: drive revenue without short-term tactics that hurt the list. Useful for promotional and lifecycle programs. Revenue depends on whether your ESP exposes attributed revenue.

List health plan

Primary: engagement rate (custom metric) at a healthy baseline. Guardrails: bounce rate < 2%; complaint rate < 0.1%; sender-reputation complaint rate < 0.1%.

Story: maintain a healthy active list against natural drift. Useful for the quiet months between major campaigns, when there’s no obvious outcome to chase.

Recovery plan

Primary: deliverability ≥ 97%. Secondary: engagement rate trending up. Guardrails: complaint rate well below 0.1% (aim for 0.05%); bounce rate trending down.

Story: rebuild reputation gradually after a deliverability incident. Useful when authentication has been fixed but mailbox providers haven’t yet restored full inbox placement.

Building a custom metric in SEINō

1. Go to https://app.seino.ai/data-model
2. Open the dataset where the metric should live.
3. Add a custom metric and paste the formula.
4. Set the output format — usually percent for rates.
5. The metric appears in the picker for that dataset and can be added to any plan.

Tip: When you paste a formula, match field names exactly. The picker uses friendly labels (for example, Unique clicks for clicks_unique) — but the formula must reference the canonical name. Hover any field in the picker to see its canonical name.