The cadence question: how often should you email?

Built into the question is an assumption: cadence is a single program-wide setting — a dial you turn to find the magic number. It isn't. Cadence is an emergent property of five other decisions, each of which is a more productive conversation than the top-level one.

A user three days into onboarding should get more mail than your average subscriber. A user dormant for six months should get less. A user who bought this week has a different post-purchase sequence than one who's never converted. The subscriber who opens everything and clicks three a week can receive what a cold-list recipient cannot. The right cadence for user A is the wrong cadence for user B, so the program-level version of the question forces an answer that's wrong for most of the audience.

The more productive version of the question: what are the inputs that determine the right cadence per cohort? Answer those, and the program-level frequency falls out as a consequence, not as an argument.

Lifecycle stage. Different stages tolerate different frequencies. Onboarding is intense by design — more email in week one than in any other week of the relationship. Engaged users tolerate regular cadence because they're finding value. At-risk users tolerate some cadence but need higher-signal messages, not more of them. Lapsed users need specific low-density sequences, not the standard program. The Lifecycle Program Design skill covers stage-specific cadence for each canonical stage.

Engagement tier within the stage. Inside any stage, users vary. Someone who opened four of your last five emails can handle more than someone who opened one. Tier explicitly and ship different cadences by tier; most programs that pick a single rate end up under-mailing the engaged base, over-mailing the light base, and producing worse aggregate numbers than they could.

Natural product cycle.A daily-engagement product tolerates a different frequency than a monthly-engagement one. The lifecycle cadence should match or just slightly lead the product's usage rhythm — not impose a separate one that ignores when the user is ready for the product.

Deliverability headroom. Strong sender reputation and clean list hygiene give you more room. Weak reputation, more volume compounds the damage. Most programs that "can't send more email" actually can — they just need to fix hygiene first so the additional volume doesn't poison reputation. The deliverability guide covers the full connection between frequency and sender reputation.

Content inventory. You can only send as many messages as you have worth sending. Shipping a second email in a week just because the schedule said so — when the content is thin — is worse than shipping one good email. The cadence ceiling is set by the quality bar; the floor is set by the cycle.

Even with well-designed per-cohort cadences, a user-level frequency cap is what prevents compound damage when multiple programs fire at the same user in the same window. Without it, a user can end up with onboarding email plus lifecycle newsletter plus product-update broadcast plus abandoned-cart push all in one afternoon — not because anyone designed it that way, but because four independent systems fired independently.

Practical cap: no more than N marketing messages per user per week, with transactional and critical-service messages exempt. N depends on program and tier; three to five marketing messages per week is the range that balances engagement and fatigue for most B2C audiences. Under-mailing a cohort 20% below the cap rarely hurts; over-mailing 20% above it reliably does.

Priority is the harder question. Which message gets cut when a user is about to cross the cap? Decide in advance and encode it in the system. Onboarding beats newsletter. Abandoned-cart beats promotional. Transactional beats everything. Without explicit priority, the cap produces random cuts and the program becomes less coherent, not more.

A user who hasn't opened anything in 30 days should receive dramatically less than their tier's usual cadence — not because the cadence is wrong, but because the engagement signal is telling you the user is heading toward lapsed. Continuing to mail at the normal rate accelerates that journey.

Spam complaint, move-to-junk, or an abandoned unsubscribe flow is a louder signal. Suppress outright or drop to minimum-touch immediately. The cost of one extra send to a user who's already said "stop" through behaviour is a complaint, and complaint rate is the metric mailbox providers actually weigh against your reputation. Unsubscribes don't damage deliverability. Complaints do.

The principle: cadence rules define the ceiling, not the floor. The ceiling is the maximum you can mail without damage. The floor is whatever engagement signals say the user is willing to receive, and it can be much lower than the ceiling. A cadence system that ignores the floor over-mails disengaging users and ships them straight to spam complaints — the one thing the cap was supposed to prevent.

The lifecycle team rarely gets to answer cadence questions in isolation. The common pattern: a senior stakeholder observes they're getting "too many emails from your own company" and asks the team to cut back. The ask is real, but it's a sample size of one. A cadence cut based on a single user's experience is usually wrong for the base.

The productive response isn't "actually our engagement rates are fine" — that reads as defensive. It's to surface the tiering. Show which users are receiving how many messages. Show engagement-by-cadence data for each tier. Invite the stakeholder to look at whether their own cadence is actually aligned with their own engagement tier — which, usually, it isn't. They opened two emails all year and are receiving three a week. The right answer is almost never "cut everyone's cadence". It's "this user's cadence doesn't match their engagement — fix the mismatch, not the program".

The other version of this question: what's the risk of over-mailing? Higher complaint rates, higher unsubscribe rates, and sender reputation damage that compounds over months. The cost usually surfaces 30–90 days after the frequency increase, which is why programs rarely link them. The signal to watch is complaint rate, not unsubscribe rate. Complaints poison deliverability. Unsubscribes just trim the list.

Significance testing has a role here too — any cadence change deserves a proper test before it rolls program-wide. A 20% cut that feels right but produces a 30% revenue drop is worse than the problem it was trying to fix.