Smart Scheduling

Smart Scheduling analyzes your historical engagement data to recommend the best times to post on each platform. Instead of guessing when your audience is most active, the system uses real performance metrics to suggest time slots that maximize reach and engagement.

The feature is integrated into the post scheduling flow. When you schedule a post or use bulk scheduling, Smart Schedule suggestions appear as recommended time slots you can select with a single click.

Smart Scheduling works by analyzing the engagement metrics (likes, comments, shares, clicks) of your previously published posts. It correlates the time of posting with the engagement received to identify patterns in your audience's activity.

When your organization has enough published posts with engagement data, Smart Scheduling provides personalized recommendations based on your specific audience. These are marked with a "Personalized" badge.

For new organizations or platforms without sufficient data, the system falls back to industry-standard optimal posting times. These defaults are based on aggregated research across millions of posts and provide a solid starting point.

Smart Schedule suggestions appear in two places:

• Post card scheduling — When you click "Schedule" on an individual post, suggested time slots appear below the date/time picker.
• Bulk scheduling — When scheduling multiple posts at once, the recommended time slot is pre-filled in the date picker.

Click a suggested time slot to auto-fill the scheduler, or choose your own custom time.

For personalized recommendations, the system needs:

• At least 5 published posts on a given platform
• Engagement metrics fetched for those posts (automatic via analytics cron)
• Posts published across different days/times for meaningful comparison

Smart Scheduling recommendations improve over time as more data becomes available. Early recommendations may have lower confidence, shown by smaller sample sizes. As your publishing history grows, the system becomes increasingly accurate at predicting optimal posting windows.