Send Time Optimization: It Learns Open Time, Not Buy Time

Send time optimization is a feature that picks the hour a message goes out from when each recipient has opened or clicked before. Klaviyo, Braze and Mailchimp all ship a version. Every one learns primarily from the inbox clock, the hour a person reads mail, not from the buying clock, the hour a person is ready to order.

Relvino decides timing per shopper from store behavior rather than inbox behavior, so read this as informed but interested. Every quoted phrase below is copied from that vendor’s own help center, product page or public glossary; unquoted descriptions and the two-clocks argument are ours.

What send time optimization does, in the vendors’ words

• Klaviyo, Smart Send Time: finds “the optimal time for you to email your customers” with the aim of “maximizing open and click rates.” It runs an exploratory send in which “Customers will be randomly assigned to a time during that period.” Then a focused send in which “1 email sends at the optimal send time and 2 emails send in a 2-hour window from the optimal send time…” It is “only available for campaigns with 12,000+ recipients.” “is only available for email,” and cannot be used with flows.
• Klaviyo, Personalized Send Time: “automatically schedules each campaign message at every recipient’s predicted best time within a delivery window you choose.” “The model looks at opens, clicks, and placed order events across your campaigns (and channels where available) to learn when similar recipients are most likely to respond.” and “optimizes for opens, clicks, and placed order rate, using these signals to determine the best time to send to each recipient.” It “works for email, SMS, push, and WhatsApp campaigns. It is not available for flows yet.” It requires the Marketing Analytics or Advanced KDP packages, and “You must schedule campaigns at least one day in advance.”
• Braze, Intelligent Timing: delivers “to each user when Braze determines a user’s optimal send time, which is when a user is most likely to engage (open or click).” It is “based on a statistical analysis of your users’ past interactions with your app and their interactions with each messaging channel.” using session times, push opens, email opens and clicks, and SMS clicks. “If a user doesn’t have any relevant engagement data for Braze to calculate the optimal send time, you can specify a fallback time.”
• Mailchimp, Send Time Optimization: “uses data science to determine when your contacts are most likely to open your email within 24 hours of the date you select.” It “is included with the Standard plan or higher” and “is not available in automated emails.”
• Insider: predicts “a personal delivery window for each subscriber instead of applying one ‘best hour’ to the whole list,” weighting history so that “an open contributes a weight of 0.4, while a click contributes a weight of 1.0” across 24 hourly slots.
• Bento’s glossary definition, which ranks first for the term: “A feature that chooses the best time to send each email based on when a person usually opens and clicks your messages.” And a limit: “Most tools need a few opens from each person before the timing becomes accurate.”

Two clocks

Every definition above optimizes the same variable: engagement with the message. Opens, clicks, session times. That is the inbox clock, and it is real. Most people read mail at roughly the same hours each day, and a message that lands in one of those hours is more likely to be seen. The feature works as described, and the vendors’ own caveats are honest ones: it needs history, it needs a fallback, and it is mostly for campaigns.

The buying clock is a different variable. It is the moment a particular shopper is ready to place an order: the replenishment window on a consumable, the evening after a browse session, the day the abandoned checkout is still warm, the pay cycle. It is read from store events (visits, carts, checkouts, order history and the gaps between them), not from mail events. The abandoned checkout guide makes the same argument directly: an abandoner’s intent likely decays over hours, a pattern the inbox has nothing to say about.

The two clocks are correlated but not the same. A shopper who reads mail at 7am and buys at 9pm gets a well-timed message at 7am, fourteen hours before the moment the message was for. This is a narrower point than the one that a store’s event stream rather than a campaign calendar should drive scheduling: a feature can already be per person and already learn from behavior, and still be learning the wrong per-person variable. Klaviyo’s newer Personalized Send Time is the one feature above that adds a store event (“placed order events”) to the inbox signals, and it is the most useful of the set for exactly that reason; it still schedules a campaign someone already decided to send, inside a window someone already chose, at least a day ahead.

What send time optimization cannot decide

The feature answers one question, when. It runs after a person has already decided whether to send, on which channel, and with what offer; it moves a decided message within a window. Klaviyo’s own scoping says it: neither of its timing features runs in flows, the automated messages that fire on a shopper’s action, and Mailchimp’s “is not available in automated emails.” The messages closest to the buying clock, the ones triggered by what a shopper just did, are the ones the feature does not touch.

Insider’s page carries the caution that makes this concrete: “it can rise from better inbox placement while conversion stays flat,” so lift should be judged on “revenue per recipient” rather than opens. A feature optimized for the inbox clock can succeed on its own metric and change nothing on the store’s.

Timing as one of four decisions

Move the question of when out of the scheduler and into the decision itself and it stops being an optimization of a fixed message. For each shopper, Relvino decides whether to act, on which channel (email, SMS/MMS or a pop-up), with what message and offer, and when, as one decision, from that shopper’s live store behavior. Timing is read off the buying clock because the inputs are store events. Each decision is made in under 80 milliseconds and its outcome feeds the next, so a shopper whose pattern shifts is not waiting for a re-test.

The prior for a shopper with no history, the case every vendor above handles with a fallback time, comes from a Large Retail Model trained across 10K+ retailers and 7M+ data points, on 1.78M real shoppers: what is known about timing in that category is the starting point, then the shopper’s own behavior takes over. There is no 12,000-recipient minimum because there is no campaign-level test; the unit is one shopper.

Where the inbox clock is the right one

Two cases, stated plainly. A newsletter or a content send has no buying moment attached; for it, the inbox clock is the whole question, and any of the features above is the right tool. And a brand that runs campaigns by calendar (a launch, a sale with a fixed end) has already fixed the day; picking the hour per recipient is a real, small gain, which is why Klaviyo advises against sending “time-sensitive content such as a flash sale” through the test at all, where the day matters more than the hour. The argument here is about lifecycle messages tied to a purchase decision, which is most of what a store sends.

The two approaches, side by side

In the list below, the send-time-optimization column is assembled from the vendor pages quoted above; quoted phrases are theirs and everything else is our reading of what the features share. The timing-as-a-decision column describes Relvino.

• What it learns from · Send time optimization: Opens, clicks, sessions: “when a person usually opens and clicks your messages” (Bento); Klaviyo’s newer feature adds placed-order events · Timing as part of the decision: Store events: visits, carts, checkouts, orders and the gaps between them
• What it optimizes · Send time optimization: Engagement with the message; Klaviyo’s Personalized Send Time adds placed-order rate · Timing as part of the decision: Revenue per shopper, with timing as one input
• What it decides · Send time optimization: The hour, inside a window a person chose, for a message a person decided to send · Timing as part of the decision: Whether to act, channel, offer and moment, together, per shopper
• Applies to · Send time optimization: Campaigns; not flows (Klaviyo), not automated emails (Mailchimp) · Timing as part of the decision: Every intervention, including the ones triggered by what a shopper just did
• Cold start · Send time optimization: A fallback time (Braze); “a few opens from each person” first (Bento) · Timing as part of the decision: A Large Retail Model prior for the category, then the shopper’s own behavior
• Minimum scale · Send time optimization: 12,000+ recipients per campaign for Klaviyo’s Smart Send Time test · Timing as part of the decision: One shopper
• Lead time · Send time optimization: At least a day ahead (Klaviyo Personalized Send Time); 24 hours from a chosen date (Mailchimp) · Timing as part of the decision: In under 80 milliseconds, per decision
• Example · Send time optimization: Klaviyo, Braze, Mailchimp, Insider, by their own descriptions · Timing as part of the decision: Relvino

For a brand that already pays for one of these features, the test is simple: compare revenue per recipient, not open rate, before and after, as Insider’s own page suggests. For a brand weighing whether timing should be a scheduler setting or a decision, the Klaviyo vs Relvino comparison sets up a 14-day pilot beside the current setup, and pricing is public. The AI-powered email marketing platform guide covers the same shift for the message itself, and next best action marketing covers the decision layer the enterprise vendors sell under another name. For the feature-by-feature view of the tools named here, the Klaviyo review and Klaviyo vs Braze go deeper, and Mailchimp vs Omnisend covers the two tools at the small end.

Frequently asked questions

What is send time optimization?

A feature in an email or messaging platform that picks the hour a message is delivered to each recipient, based on when that person has opened or clicked before. Bento’s glossary, which ranks first for the term, calls it a feature that chooses the best time to send each email based on when a person usually opens and clicks your messages. Klaviyo, Braze, Mailchimp and Insider each ship a version; all of them learn from engagement history and most apply to campaigns rather than automated messages.

Does send time optimization increase revenue?

It can raise the chance a message is seen, because it lands in the hours a person reads mail. Whether that becomes revenue depends on whether the message was the right one for that shopper at all, which the feature does not decide. Insider’s own page cautions that open rates can rise while conversion stays flat and recommends judging the feature on revenue per recipient.

Is send time optimization available for flows in Klaviyo?

No. Klaviyo states that Smart Send Time is only available for email campaigns with 12,000 or more recipients and cannot be used with flows, and that its newer Personalized Send Time works for email, SMS, push and WhatsApp campaigns but is not available for flows yet. Mailchimp’s equivalent is likewise not available in automated emails.

What is the best time to send marketing emails?

There is no single hour. The features above exist because the best inbox hour differs per person, and their own documentation says a person needs some engagement history before the prediction is accurate. For a message tied to a purchase decision, the more useful clock is the shopper’s buying clock, read from store events such as a browse session, an abandoned checkout or a replenishment interval; that is the moment a message is for, and it is often not the hour the person reads mail.

How long does it take to migrate from Klaviyo’s send time features to Relvino?

About 30 minutes for the technical cutover: connect the Shopify store, point the sending domain, connect SMS, and shopper data ingests automatically. There are no send-time tests to re-run and no windows to configure; timing is decided per shopper as part of the same decision as the channel and the offer, inside guardrails the brand sets once, including quiet hours. Revenue is then proven in a 14-day pilot run beside the current setup.