Send Time Optimization (Next Best Time)

Send time optimization (STO) uses machine learning to predict when each individual customer is most likely to open, click, and convert. Instead of sending campaigns at a fixed time or using basic time-zone adjustments, ML-based STO analyzes each customer's historical engagement patterns to find their personal peak response window.

Most marketing platforms offer rudimentary send time optimization based on aggregate data or simple time-zone shifting. These approaches miss the individual variation that drives real engagement — a night-owl executive reads email at 11pm, while a morning commuter engages at 7am.

EkamFlow predicts the next best time for every customer as part of a unified prediction. The model learns from email opens, click timing, purchase timestamps, and app engagement patterns to recommend the specific hour and day that maximizes each customer's likelihood to respond.