Send Time Optimization (STO)

Most email marketers pick a send time based on industry averages ("Tuesday at 10am is best"). Send time optimization replaces that population-level guess with an individual-level prediction — built from each subscriber's actual email opening behaviour over time.

What is send time optimization?

Send time optimization is a machine learning feature built into email marketing platforms that analyses each subscriber's historical engagement behaviour — specifically, when they have opened emails in the past — and uses that data to predict the single best moment to deliver the next email to that person.

When you activate STO for a campaign, you do not set a specific send time. Instead, you trigger the campaign and the platform's algorithm handles delivery timing for each subscriber independently. The result is a "rolling send" where different subscribers receive the same email at different times over a 24-48 hour window.

The feature goes by different names on different platforms:

• Mailchimp: Send Time Optimization
• Klaviyo: Smart Send Time
• HubSpot: Smart Email Scheduling
• ActiveCampaign: Predictive Sending
• Campaign Monitor: Send Time Optimization

How send time optimization works

The underlying mechanism is a machine learning model that processes engagement history to build a "peak engagement window" for each subscriber. Here is the process step by step:

1. Data collection. The platform logs every open event with its timestamp for every subscriber, building a time-series dataset of when each person engages with email.
2. Pattern recognition. The model identifies recurring patterns in each subscriber's open behaviour — for example, someone who consistently opens emails between 7-8am on weekdays, or another who opens in the evening after 8pm.
3. Prediction. For each new campaign, the model predicts the next occurrence of each subscriber's peak window and schedules delivery to arrive just before that window opens.
4. Fallback. For new subscribers without sufficient history, the platform uses a population-level best time based on aggregated data from its entire user base or your industry segment.

Does send time optimization actually work?

The evidence is generally positive but nuanced. Platform-published research suggests lift of 3-15% in open rates when STO is used versus a fixed best-time send. However, several variables determine whether you see meaningful improvement.

When STO works well

• Large, active lists. More subscribers means more individual data, which means better individual predictions.
• Subscribers with consistent patterns. People with predictable routines — checking email at the same time daily — are ideal candidates. The model's prediction accuracy is high.
• Non-time-sensitive campaigns. Newsletters, nurture sequences, and re-engagement campaigns benefit most because there is no urgency to deliver simultaneously.
• High-frequency senders. Brands that email weekly or more frequently accumulate historical data faster, improving model accuracy sooner.

When STO has limited impact

• New lists with little history. Without individual data, STO falls back to population averages — which is no better than your own research-based best time.
• Highly engaged lists. If your list already opens at 40%+, there is less headroom for improvement from timing alone.
• Irregular subscriber behaviour. If your subscribers open emails sporadically with no pattern, the model cannot make meaningful predictions.

STO vs. fixed send time — when to use each

Best practices for send time optimization

1. Let the model warm up before evaluating. Do not judge STO results after one campaign. Give the algorithm 4-6 campaigns to accumulate signal before comparing performance to your prior fixed-time benchmark.
2. Segment your list before enabling STO. Run STO on active, engaged subscribers only. Inactive subscribers with no open history dilute the model and pull performance down.
3. Keep a control group. Split a portion of your list to receive at a fixed time to accurately measure the lift STO is delivering over your baseline.
4. Do not use STO for time-sensitive campaigns. Any campaign where timing is part of the value proposition — sales, launches, events — should use a fixed time.
5. Monitor for clustering effects. Some STO implementations send a disproportionate share of a list at the same popular time (e.g. Tuesday 9am). Check your delivery distribution to ensure true staggering is happening.
6. Pair STO with strong subject lines. Timing is a multiplier — if your subject line is weak, no amount of timing optimisation will save the open rate. Get the fundamentals right first.

Common send time optimization mistakes

• Using STO for every campaign regardless of use case. STO is a tool for specific campaign types. Applying it indiscriminately leads to time-sensitive emails arriving a day late.
• Assuming STO replaces subject line or content quality. The best send time for a boring subject line is still a bad open rate. Timing is a marginal gain; relevance is foundational.
• Enabling STO on a brand new list. Without historical data, the platform is guessing with population averages. You may as well pick a time yourself.
• Never reviewing results. STO performance varies by list composition and evolves as subscriber behaviour changes. Review open rate trends quarterly and compare STO campaigns to fixed-time campaigns in the same period.

Frequently asked questions

Related glossary terms

Sources

Akshay VR

Marketing Head · theStacc · ex-Sr Marketing Specialist, ARKA 360 · Malappuram, Kerala

Akshay leads editorial and content operations at theStacc. He writes about email marketing optimisation, content strategy, and the incremental improvements that compound into meaningful business results over time.

LinkedIn Author page More terms by Akshay