Send Time Optimization
Send time optimization (STO) is an AI feature that predicts the hour of day a specific recipient is most likely to engage with email based on their historical open and reply pattern, then schedules outbound messages accordingly. Per-recipient STO typically lifts open rates 8 to 15 percent and reply rates 5 to 10 percent on cold outbound where the recipient has prior engagement history.
Generic best-practice send times ("Tuesday 10am") were always a population-average lie. Real engagement is highly per-recipient: one prospect reads at 6:30am while pouring coffee, another at 4:45pm in airport security. STO predicts the right hour per individual.
How the model works
The model ingests every historical open and reply event for the recipient (or for similar recipients if the prospect is cold). The features are: time-of-day, day-of-week, time-zone-adjusted hour, prior-open recency, and message-type (transactional vs cold vs follow-up). A gradient-boosted classifier predicts the probability of open within 6 hours of send for each candidate hour. The hour with the highest probability becomes the recommended send window.
How much lift to expect
On cold prospects with no engagement history, the lift is small (1 to 3 percent) because the model falls back to industry-average patterns. On prospects with at least three prior engagements, the lift on open rate is 8 to 15 percent and on reply rate 5 to 10 percent. Compounded over a 5-email sequence, the net difference in pipeline is material.
Frequently asked questions
Does STO delay urgent sends?+
Most implementations let the rep override the optimised window for individual sends. Outsolvi's STO presents the recommended hour and the rep can send immediately if context demands.
Does this work for cold prospects?+
Partially. Without prior engagement data the model uses cohort defaults by industry and title. The lift is smaller but still positive vs no scheduling.
Put this concept into practice
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