Feedback Overview

The Feedback 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 feedback sent	Count of feedback items sent (one per feedback record)	Number of pieces of feedback given, in scope. It counts feedback items, not people, so a prolific sender who gave 20 pieces contributes 20. It measures volume of feedback activity, not reach. Read against # Users who sent feedback (distinct givers) to tell heavy use by a few apart from broad participation.
Feedback sent over time	Count of feedback items sent, by date (feedback sent each period)	Number of feedback items given in each period, plotted as a trend. Each point is that period's total, not a running total, so it shows the rhythm of feedback-giving (spikes around review cycles, quiet stretches). The points sum back to # Feedback sent over the full range. On the Individual People Day Summary page, it's scoped to one selected worker, so it reads as that person's outgoing-feedback timeline; on the Feedback report it's the whole population in view.
Feedback Flow	For each role pair (Manager→Manager, Manager→Employee, Employee→Manager, Employee→Employee), count of feedback items with that relationship÷ count of all feedback items	Direction of feedback given across the hierarchy: what share of feedback flows within peers versus up or down the reporting line. It shows who gives feedback to whom, so a heavy Manager→Employee slice means feedback is largely top-down, while Employee→Manager captures upward feedback. The four shares are of all feedback and sum to 100%. Read against Request flow to compare who gives with who asks.
Manager	Count of distinct line-manager IDs referenced across worker rows	Number of distinct people act as a line manager for the population in view. There is no separate manager list; a person counts as a manager because their worker ID appears as someone else's line manager, so the number reflects only managers who actually have a reporting worker in scope. It typically serves as the base for manager-level coverage or activity measures, and it re-bases whenever you filter to a team or org unit. Because it is inferred from reporting links, a manager whose reports fall outside the current selection will not appear.
Worker	Count of distinct worker instances	Number of distinct workers who make up the population in view, i.e. the full headcount. It includes everyone with a worker record, managers among them, so it overlaps with # Managers rather than complementing it (managers are a subset of workers, not a separate group). It is also a different population from # Active users : workers are counted from the worker/employment records, whereas active users are provisioned application accounts, and the two do not always line up.
New Logins over time	Count of connection IDs, by date (one point per period)	Same sign-in event count as # logins , broken out along a date axis so you can see activity rise and fall period by period. Each point is the logins in that bucket (not a running total), so the points sum back to the single # Logins figure. Read it for trends and seasonality in usage; pair it with the cumulative first-time-login curve to tell repeat activity apart from new adoption.
Feedback sent by role	Count of feedback items sent (one per feedback record)	Number of pieces of feedback given, in scope. It counts feedback items, not people, so a prolific sender who gave 20 pieces contributes 20. It measures volume of feedback activity, not reach. Read against # Users who sent feedback (distinct givers) to tell heavy use by a few apart from broad participation.
Feedback sent by type	Count of feedback items sent (one per feedback record)	Number of pieces of feedback given, in scope. It counts feedback items, not people, so a prolific sender who gave 20 pieces contributes 20. It measures volume of feedback activity, not reach. Read against # Users who sent feedback (distinct givers) to tell heavy use by a few apart from broad participation.
Active 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.
Endorsed, Public & Shared	Share of feedback that is endorsed, that is public, and that is shared, side by side	Three feedback-visibility rates shown together ( % endorsed feedback , % public feedback , % shared feedback ), so uptake of each can be compared. Each is a share of all feedback. Read against # feedback sent for the volume behind the percentages.
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.
Feedback quality metrics	Share of feedback with behaviors, followed up, and linked to goals, side by side	Three feedback-quality rates shown together ( % feedback with behaviors , % feedback followed-up , % feedback linked to goals ), so how richly feedback is used can be compared. Each is a share of all feedback. Read against # feedback sent for the volume behind the percentages.
Thanks, Favorites & Let's meet	Sum of Let's Meet reactions across feedback	Total number of "Let's Meet" reactions, where a recipient wants to discuss the feedback in person. It signals feedback that opens a conversation rather than closing the loop, so a rise can mean richer follow-up dialogue. It counts reactions, not feedback items or people. Read against # Thanks and # Favorite to see which reaction type dominates.

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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.