Adoption Monitor
The Adoption Monitor 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 |
|---|---|---|
| Users having logged in | Count of distinct users who signed in÷ count of all users | Share of the user directory that has actually signed in, the login rate. It puts Unique Logins in context so groups of different sizes compare fairly, answering "what proportion of the people who could use the platform did." A low or falling rate flags an activation gap; it says nothing about how often those users return (that is the average-logins measure). |
| Users who sent feedback | Count of distinct users who sent feedback÷ count of all people in the directory | Share of people who gave feedback to someone. It's the giving side only, so people who merely requested or received feedback are not counted. Someone who sent many pieces of feedback counts once. Read against % Users received feedback and % Users used feedback to compare giving with getting and with overall active use. |
| Users who requested feedback | Count of distinct users who requested feedback÷ count of all people in the directory | Share of people who asked someone for feedback. It's the requesting side only, so people who gave or received feedback but never asked are not counted. Someone who made several requests counts once. Read against % Users who sent feedback and % Users received feedback to compare asking with giving and getting. |
| Avg. word count | Sum of feedback word counts÷ number of feedback items | Average length, in words, of the feedback messages that were sent. It's a rough proxy for how detailed or substantive feedback is: a higher value suggests richer, more considered feedback, a very low value suggests terse one-liners. It averages over feedback items with a non-blank word count, so a few long messages can pull it up. Read against # Feedback sent to see whether volume comes with substance. |
| Users having goals | Count of distinct goal owners (workers with at least one goal)÷ count of all people in the directory | Share of the population that owns a goal; i.e. the goal-setting adoption rate. It puts # Users having goals in context so groups of different sizes compare fairly, answering "what proportion of our people have set any goal." A low or falling rate flags a goal-setting gap; its complement is % Users having no goals . |
| Avg. current goal progress | Sum of goals' progress %÷ number of goals (in scope: goals that are In Progress or in a completion state, with a non-blank progress value) | Mean completion progress of goals that are actually being worked on or closing out, on a 0–100% scale. Not-started/draft goals are excluded from both the sum and the count, so it reflects momentum on live goals rather than being dragged down by ones that haven't begun. Rising means goals are advancing toward completion; read it with % Goals completed to separate "progressing" from "finished." Shared goals: this divides over goals, each counted once and attributed to its owner. A goal shared by its owner with a team contributes one progress value to both the sum and the count; goals shared with someone are never counted as theirs. Individual, team and org goals are pooled together. |
| Users having check-ins | Count of distinct people who took part in a check-in÷ count of all workers | Share of the workforce who participated in at least one check-in, i.e. the check-in adoption rate. It puts # Users having check-ins in context so groups of different sizes compare fairly; a low value flags check-ins not catching on. A person with many check-ins still counts once in the numerator. |
| Check-ins having 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. |
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For more information about the visuals and metrics used in Analytics Hub, refer toVisuals in Analytics hub and Metric dictionary, respectively.