Outreach timing built from real LinkedIn activity

Outreach timing studies how a prospect uses LinkedIn. For each profile you provide, it samples recent activity — posts, comments and reactions — and records when in the week that activity happened.

From that sample it builds a day-of-week by hour heatmap and identifies the three windows where the person is most consistently active. Each profile is returned with those top windows and a short, plain recommendation, so you do not have to read a chart to act on it.

Across a list, the result is a send schedule rather than a guess. You can group prospects by their peak window and line up each batch of outreach to land when its recipients are most likely to be looking.

Timing is the cheapest lever in outreach because it costs nothing to pull. The same message, the same list and the same rep produce different results depending only on whether it landed while the recipient was on the platform. Outreach timing makes that lever usable at scale: instead of one blanket send time, each prospect carries their own window, and a campaign can be batched so every message arrives near its recipient’s peak. It is most powerful on a multi-step sequence, where lining up each touch with an active window compounds across the whole cadence. For teams selling across time zones, it also removes the guesswork of when morning even means for a given contact.

• Outbound teams scheduling each touch to land just before a prospect’s usual active window.
• Founders avoiding dead send times by reaching an exec when they actually engage, not at nine on Monday.
• Campaign managers grouping a target list by peak window to coordinate batches across the week.
• Account-based teams timing a multi-step sequence around when a key contact is most reachable.

Each profile returns its top active windows and a recommendation:

Outreach timing works through your own LinkedIn session. You connect a session cookie once in Settings, where it is encrypted at rest, and the tool samples each profile’s recent public activity.

The timestamps of that activity are bucketed into a day-by-hour grid, and the busiest buckets become the recommended windows. A larger activity sample produces a sharper read, so the recommendation is strongest for profiles that post and engage regularly.