Check-in Overview
The Check-in Overview report page presents the visuals listed below. Each row gives the on-screen name, its underlying metric, and a short description; see the metric glossary for the full formula and interpretation.
Visuals:
| Name | Measure | Description |
|---|---|---|
| Total check-ins | Count of check-in IDs (one per check-in) | Total number of check-ins that took place, in scope. It counts check-in events, not people, so one person with many check-ins adds many; it measures activity volume, not reach. Read against # Users having check-ins (distinct participants) to separate heavy repeat use from broad adoption. |
| Check-ins over time | Count of check-in IDs, by date (check-ins each period) | Number of check-ins created in each period, plotted as a trend. Each point is that period's total, not a running total, so it shows the rhythm of check-in activity (spikes around review cycles, quiet stretches). The points sum back to # Check-ins over the full range. It counts check-in events, not people. |
| Avg. check-ins (workers) | Count of distinct check-ins involving a worker (from or to)÷ count of distinct workers who took part in a check-in | Average number of check-ins each participating worker was involved in, i.e. check-in intensity among individual contributors. It measures depth of use for workers who use check-ins at all (the denominator excludes non-participants), so a value near 1 means most workers had a single check-in, higher means repeat use. Read against Avg. check-ins per manage r to compare intensity between the two roles. |
| Avg. check-ins (managers) | Count of distinct check-ins involving a manager (from or to)÷ count of distinct managers who took part in a check-in | Average number of check-ins each participating manager was involved in, i.e. check-in intensity among managers. The denominator excludes managers who never checked in, so it measures depth of use among those who do; a value near 1 means most had a single check-in, higher means repeat use. Read against Avg. Check-ins per worker to compare intensity between the two roles, noting managers often sit across many check-ins with their reports. |
| Checkins created over Time | Count of check-in IDs, by date (check-ins each period) | Number of check-ins created in each period, plotted as a trend. Each point is that period's total, not a running total, so it shows the rhythm of check-in activity (spikes around review cycles, quiet stretches). The points sum back to # Check-ins over the full range. It counts check-in events, not people. |
| Goals created over time | Count of goals, by created date (one point per period = goals created that period) | Number of goals created in each period; i.e. the goal-creation trend over time. Each goal lands in the period of its created date, so this is a per-period count (not a running total), useful for seeing spikes around review cycles or planning seasons and whether goal-setting is sustained or bursty. The points sum back to # Goals over the full range. Shared goals: counts goals, each once and attributed to its owner, placed at its creation date. A shared goal counts once; goals shared with someone never count as theirs. Individual, team and org goals are counted together. |
| Users with check-ins by role | For each role (Manager / Worker), count of that role's check-in participants÷ count of that role's workers | Share of each role who participated in at least one check-in, shown for managers and workers side by side. It reveals where check-in use concentrates, whether managers are ahead of their reports or the reverse. Each bar is that role's participation rate, and a person with many check-ins still counts once. Read against % Users having check-ins for the blended figure across both roles. |
| Check-ins recurring | Count of check-ins where recurring flag = Yes÷ count of distinct check-ins | Share of check-ins that are part of a recurring series rather than one-off, i.e. how much of check-in activity follows a standing cadence. It puts # Check-ins recurring in context so periods with different volumes compare fairly; a higher rate means regular, habitual check-ins, a low rate means mostly ad-hoc ones. Read against # Check-ins for the underlying volume. |
| Check-ins in the future | Count of check-ins where future-check-in flag = Yes÷ count of all check-ins | Share of check-ins that are scheduled for a future date rather than already held. It puts # Check-ins in the future in context so periods with different check-in volumes compare fairly, showing how much check-in activity is still ahead versus behind. A higher share signals active forward planning; a very low share means check-ins are mostly logged after the fact. |
| Assigned check-ins | Count of check-in IDs where the assigned flag = Yes (created from a template) | Number of check-ins that were assigned from a template rather than created ad-hoc. It sizes the template-driven, standardized portion of check-in activity. It counts check-in events, not people, so a busy assigned type shows a larger figure. Read against # Check-ins for the total, or use % Assigned check-ins for the share. |
| Check-in Status | Count of check-in IDs, by status (New / In Progress / Done Pending Confirmation / Done / Deleted / Under Review / Assigned) | Distribution of check-ins across their lifecycle statuses, showing where check-ins sit at a glance. A large Done bar means most check-ins are completed, a large New / In Progress bar means an active pipeline, and a visible Deleted bar flags canceled ones. It counts check-in events, not people. Read against # Check-ins for the total these bars sum to. |
| % Check-ins with messages | Count of check-ins where has-message flag = Yes÷ count of all check-ins | Share of check-ins that include a written message, i.e. how many carry a qualitative comment rather than just a status or rating. It puts # Check-ins having comments in context so periods with different check-in volumes compare fairly; a low rate means check-ins are mostly quick tick-box entries with little written detail. Read against % Discussion points to gauge overall check-in richness. |
| Check-ins with discussion points | Count of check-ins where has-discussion-points flag = Yes÷ count of all check-ins | Share of check-ins that include at least one structured discussion point, i.e. how substantive check-ins are versus bare status updates. It puts # Check-ins with discussion points in context so periods with different volumes compare fairly; a higher rate means richer, agenda-driven check-ins. Read against % Check-ins having comments to gauge overall check-in depth. |
| Check-ins with messages by role | Count of check-ins where has-message flag = Yes÷ count of all check-ins | Share of check-ins that include a written message, i.e. how many carry a qualitative comment rather than just a status or rating. It puts # Check-ins having comments in context so periods with different check-in volumes compare fairly; a low rate means check-ins are mostly quick tick-box entries with little written detail. Read against % Discussion points to gauge overall check-in richness. |
This is a tip message.
For more information about the visuals and metrics used in Analytics Hub, refer toVisuals in Analytics hub and Metric dictionary, respectively.