How often should you send marketing emails?

How often should you send marketing emails? Keep a workable starting schedule, including any cadence promised to subscribers. Count campaigns and promotional flows together per customer. Test an increase or reduction on a defined audience, comparing purchases and opt-outs over the same period. A weekly number alone does not establish whether another send is worthwhile.

How many marketing emails a week should you send?

Use the schedule you promised subscribers or your current workable cadence as the starting point. Our recommendation is to test an alternative after counting customers’ total promotional contact, rather than treating a weekly number as an industry rule.

If your signup form promises a weekly newsletter, preserve that commitment when planning it. If you have no schedule yet, choose one your team can sustain with useful content and record it as a starting hypothesis. Do not describe it as an optimised frequency before you have evidence.

There is a reason to assess sales and opt-outs together. In a research preprint by Baek, Chen, Ma and Mitrofanov, 603,153 customers of one North American online retailer were randomly assigned to daily or every-other-day coupon emails during October 25–31, 2024. Over that experimental week, fewer emails meant fewer unsubscribes, but also lower short-term revenue and purchasing.

That finding does not make every-other-day sending the right schedule for your store. It shows a trade-off in one retailer’s coupon-email setting. The researchers used unsubscribing as an annoyance and attrition signal; staying on the list is also a different outcome from continuing to buy.

Use this suggested decision table before adding any send:

Do automated flows count toward email frequency?

Count promotional flow emails alongside campaigns when assessing a customer’s marketing contact. Record transactional messages separately, and distinguish planned sends from messages actually sent or skipped. The campaign calendar alone can miss part of the customer’s experience.

Consider this invented example of one week, with every listed promotional email actually sent:

The same campaign schedule gives customer A 1 + 2 + 1 = 4 promotional emails, while customer B receives 1 + 0 + 0 = 1. A separate order receipt would sit outside this promotional subtotal. These are illustrative counts, not recommended flow lengths or a classification of any actual receipt’s content.

Build your inventory with customer, time window, campaign or flow, channel, send time, actual-send status and skip reason. Keep SMS in its own column so an email-only review does not silently omit other promotional contact.

For signup-sequence design, use the welcome-flow guide. Here the question is whether those sends overlap with the rest of the programme.

What should happen when a campaign and a flow overlap?

Decide which promotional message takes priority, then check the reason for any skipped send before changing a cap. A frequency window can prevent contact overlap; it does not necessarily queue a skipped message for later or set a weekly email total.

Klaviyo’s Smart Sending documentation gives a concrete example. Its default email window is 16 hours, and enabled marketing messages can be skipped after recent marketing contact. Skipped messages are not automatically rescheduled. This is a product default, not evidence of an ideal contact frequency.

The feature excludes transactional messages from both skipping and triggering the window. Email and SMS windows operate independently. Disabling it for a message bypasses that window; changing the window does not retroactively update existing recipient timers.

To inspect a flow collision, follow the documented skip-reason path: edit the flow, select the skipped message, then Performance → View details → Recipient activity → Skipped → Type. Smart Sending, suppression and failing flow filters are different reasons. A large skipped count alone does not establish which one applies.

The email window setting is under organization name → Settings → Email → Sending preferences → Smart Sending Period. Before editing it, choose which overlapping promotion should move or be omitted. Record any intentional exception. Removing every cap to improve the sent count leaves the customer-contact decision unresolved.

Should you send more often during a sale?

Treat a sale cadence as a temporary policy to assess, with a defined audience and contact limits. Check overlapping flows and delivery health before increasing volume. A busier promotional calendar does not by itself establish that each customer should receive more emails.

Google’s guidelines for senders emailing personal Gmail accounts recommend consistent sending rates and avoiding sudden volume spikes without a history of large-volume sending. They advise monitoring server responses, spam rate and domain reputation as volume rises, and reducing volume when messages bounce or are deferred until the SMTP error rate falls.

Keep delivery troubleshooting separate from the frequency experiment. The deliverability guide covers that diagnosis; a new promotional schedule cannot substitute for it.

How do you test whether another email is worth sending?

Compare your current promotional schedule with a defined alternative using randomly assigned customers who remain in the same groups throughout the test. Assess business outcomes and subscriber losses over a common follow-up period, and record the contact each group actually received.

For example, an alternative could add one specific campaign while retaining the shared campaigns, offers and unrelated flow rules. That tests adding that campaign to the schedule. It does not isolate frequency from the extra campaign’s content.

Where relevant, randomise within comparable customer groups. This applies NIST’s guidance on controlling nuisance factors and randomizing others; NIST does not prescribe an email cadence.

Write down the comparison before sending:

• Audience and assignment: eligible customers, groups and persistent assignment.
• Contact policy: shared sends, the added or omitted promotion, cap rules and message priority.
• Business outcome: total orders or order contribution per assigned customer, including measurable purchases beyond email-attributed orders.
• Downside checks: unique unsubscribers per assigned customer over the same period, alongside complaint and delivery monitoring.
• Decision rule: common follow-up, the improvement worth acting on and the downside you are prepared to accept.

Keep the unsubscribe denominator clear. Opt-outs per email sent and unique people opting out per assigned customer answer different questions when the groups receive different numbers of emails.

Pause expansion for delivery errors or unacceptable complaint changes. Revise or reduce the alternative if it breaches your predeclared opt-out limit or produces no credible additional value. If uncertainty leaves the choice unresolved, record an inconclusive result. For the cost calculation, use the email-budget decision guide.

Can AI choose how often to email each customer?

Evaluate a proposed AI frequency policy against your current schedule, using total promotional contact, business outcomes and subscriber losses. First check whether the feature actually changes contact frequency or only chooses a delivery time. A predicted hour does not answer how many messages to send.

The retailer preprint also models personalized frequency policies using its experimental estimates. Its long-term policy gains are modelled, rather than observed results from a live personalized-frequency rollout. That is a reason to investigate the approach, not proof that an AI feature will improve your store’s retention.

For choosing the hour, see the send-time guide. For choosing the next send, start with your contact inventory: identify the customers who would receive it, what else they are receiving, and the result that would justify keeping it.