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Email sends go out on a single global time and miss the per-recipient engagement window. A Send Time Optimization Playbook reads engagement history per recipient to predict the optimal send time and lift open and click rates without changing content.
The Challenge
A campaign sent at 10am on Tuesday hits engaged hours for some recipients and inbox-burial hours for others. The aggregate open rate looks like the campaign performance; the actual variance is per-recipient timing fit, not content.
Every recipient has an engagement pattern: time of day, day of week, frequency tolerance. The signal sits in the email platform but never gets used to time individual sends. The campaign treats timing as a constant when timing is the most actionable variable.
A/B tests on send time take weeks to deliver significance and reveal aggregate patterns rather than per-recipient patterns. By the time the test reaches significance, the campaign cycle has moved on and the lesson never gets applied at the recipient level.
How eyko Solves It
A Send Time Optimization Playbook reads per-recipient engagement history (time-of-day open patterns, day-of-week patterns, frequency tolerance) and message-fit signals to predict the optimal send time for each recipient on each message. It surfaces the per-recipient engagement window, routes sends to those windows, and lifts open and click rates without any content change.
The Playbook optimized send times for an upcoming campaign across 184,000 recipients. Per-recipient send-time optimization projects 22% lift in open rate and 18% lift in click rate against a single 10am Tuesday baseline. Recipients with strong engagement signal saw the steepest lift; low-engagement recipients saw modest lift. Optimized send times spread across 6 distinct engagement windows.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
Send Time Optimization reads per-recipient engagement history (time-of-day open patterns, day-of-week patterns, frequency tolerance) and message-fit signals to predict the optimal send time for each recipient on each message. The Playbook surfaces the per-recipient engagement window, routes sends to those windows, and lifts open and click rates without any content change.
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FAQ
Everything you need to know about Send Time Forecast.
Send Time Optimization is an AI-driven prediction of the optimal send time per recipient per message using per-recipient engagement history (time-of-day open patterns, day-of-week patterns, frequency tolerance) and message-fit signals. The Playbook surfaces the per-recipient engagement window, routes sends to those windows, and lifts open and click rates without any content change.
The Playbook reads from your email platform (per-recipient open and click timestamps, message metadata), marketing automation (send history, frequency data), and CRM (recipient stage and persona context). At least 12 months of paired send-and-engagement timestamp data per recipient anchors the prediction.
A/B testing identifies an aggregate best-send-time over weeks of testing. Send Time Optimization identifies a per-recipient best-send-time using historical engagement timestamps. The two are complementary, but per-recipient prediction is what catches the engagement variance that A/B tests smooth over.
No. Send Time Optimization changes the send timing only. The same content and creative goes out, but timed per recipient rather than at a single global time. The engagement lift comes from timing alone, which makes this one of the lowest-effort optimizations in marketing automation.
Join the enterprises replacing weeks of manual analysis with a single prompt. See what eyko Playbooks can do with your data.
