Send Time Optimisation

Written by Cobus van der Westhuizen, reviewed by Wynand van der Westhuizen, fact-checked by Lenata Oosthuizen. Last reviewed July 2026. Editorial policy.

What Is Send Time Optimisation?

Send time optimisation (STO) is a feature built into modern email marketing platforms that analyses each subscriber's historical open behaviour and schedules delivery to coincide with their personal engagement window.

Instead of choosing a single blast time for your entire list, STO staggers delivery so that someone who routinely opens emails at 07:30 receives yours at 07:30, while a subscriber who prefers 19:00 gets the same message that evening.

The underlying mechanics rely on machine learning models trained on prior campaign data. The algorithm looks at every open, click, and engagement event attached to a subscriber profile, calculates the hour-of-day and day-of-week patterns, and assigns a predicted optimal delivery slot.

Most platforms require a meaningful amount of historical data before these predictions become reliable. Early in a list's life, the system defaults to a population average, but accuracy improves steadily as more campaigns are sent and more interaction data accumulates.

STO is distinct from simple A/B time testing. A/B testing compares two fixed send times across random audience splits to identify which performs better for the audience overall. STO goes further by personalising the delivery window at the individual subscriber level.

The two approaches complement each other: A/B testing establishes a strong default time, while STO refines delivery on a per-subscriber basis once sufficient data exists.

For South African businesses, time-zone alignment is relatively straightforward since the country operates on a single time zone (SAST, UTC+2). However, audience behaviour still varies significantly by profession, device usage, and urban concentration.

A Johannesburg financial services audience may peak on weekday mornings, while a Durban retail consumer list may show stronger weekend evening engagement. STO accounts for these individual differences without requiring manual segmentation.

Send Time Optimisation In Practice

The scenario below is an illustrative example, not a Juicy Designs client result. The figures indicate the scale of effect that send time optimisation typically produces, so treat them as indicative rather than measured.

Consider a Cape Town-based online clothing retailer running a weekly newsletter to around 12,000 subscribers. Before enabling STO, it sends every campaign at Tuesday 10:00 and sees a consistent open rate of just under 20 percent.

After activating STO in Klaviyo and allowing three campaign cycles for the algorithm to gather data, the open rate could plausibly climb to something in the region of 25 percent, with a corresponding improvement in click-through rate. A gain of that kind would come entirely from timing, with no changes to subject lines or content.

The practical workflow is straightforward. When creating a campaign in Mailchimp or Klaviyo, you select the STO option instead of entering a fixed send time. The platform then queues each subscriber for delivery within a specified window (typically 24 hours) at their individual optimal time.

You set the campaign live, and the platform handles the staggered delivery automatically. Reporting consolidates all opens and clicks into a single campaign dashboard regardless of when each email was delivered.

For time-sensitive promotions where the offer expires within a few hours, STO should be disabled in favour of a fixed send time to ensure all subscribers receive the email while the offer is still valid.

What send time optimisation is

Send time optimisation is the practice, in email marketing, of sending emails at the time each recipient (or the audience overall) is most likely to open and engage with them, rather than sending at an arbitrary or one-size-fits-all time, so as to improve open and engagement rates. The premise is that when an email arrives affects whether it is noticed and opened: an email that lands when a recipient is checking their inbox and receptive is more likely to be opened than one that arrives when they are busy or asleep and gets buried under later messages. Send time optimisation seeks the best send times to maximise the chance of the email being seen and engaged with. It ranges from simple approaches (sending at times generally found to perform well for the audience, based on testing or best-practice guidance) to sophisticated, individualised optimisation (where email platforms use data on each recipient's past open behaviour to predict and send at the optimal time for that individual). Many email marketing platforms now offer send time optimisation features that automatically determine and use the best send time for each recipient based on their engagement history. Understanding send time optimisation matters because timing can meaningfully affect email performance, and optimising send times, whether through testing to find good times or using platform features that individualise send times, is a way to improve open and engagement rates without changing the content, so knowing what send time optimisation is helps a business consider timing as a lever for email effectiveness, while keeping it in perspective relative to the more fundamental drivers of email success.

Send time optimisation in practice

In practice, send time optimisation is a useful refinement to email marketing, but one to keep in perspective, since timing helps at the margin while the fundamentals (a quality list, relevant content and good deliverability) matter far more. On how it works: at the simple end, a business can test different send times and days for its audience, comparing open and engagement rates, to find the times that generally perform well for its subscribers, and then send at those times, this testing-based approach is accessible to any sender and can identify sensible send windows. At the sophisticated end, many email platforms offer automated send time optimisation that uses each recipient's individual past open behaviour to predict and send at the optimal time for that person, individualising the send time across the audience, which can improve engagement without manual effort where the platform supports it and there is enough data. On limitations and perspective: send time optimisation, especially the individualised, data-driven kind, works best with sufficient engagement data, so for small lists or subscribers with little history, there may not be enough data for reliable individual optimisation, in which case testing to find generally-good send times, or simply choosing sensible times, is the practical approach, and the benefit of sophisticated optimisation is modest for small lists. More importantly, timing is a marginal factor compared with the fundamentals of email success: a relevant, valuable email to an engaged, permission-based list with good deliverability will perform well across a range of send times, whereas optimising the send time of poor content to a disengaged list achieves little, so send time optimisation should be seen as a refinement that can lift engagement somewhat, not a substitute for good list quality, relevant content and sound email practice. For a South African business, send time optimisation is worth using where available and helpful: use your email platform's send time optimisation feature if it offers one and you have enough data, or test send times to find what works for your audience, to gain the marginal improvement in open and engagement rates that good timing provides, while recognising that for small lists the benefit is limited and that the fundamentals of email, a quality list, relevant content and good deliverability, matter far more than timing. The sensible approach is to optimise send times as a helpful refinement, particularly through platform features or simple testing, without over-investing in timing at the expense of the more important drivers of email performance.

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