What does it mean to resolve a flag?

When an inspector flags an item Yellow or Red and it later returns to Green, that flag is resolved. This card credits the inspector who personally brought it back to Green.

Can I sort this table?

Yes — click any column heading to sort by it, and click again to reverse. It's the fastest way to bring the highest or lowest values to the top.

Why can a small location show an extreme percentage?

Percentages swing hard on small numbers. A location with only a handful of records can read 0% or 100% on a single result, so read the rate alongside the count it's based on.

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Inspector Resolution Rate

For each inspector: the percentage of items they flagged that they also resolved back to Green.

August 3, 2026

991

Feature

Inspections

Viz Type

Table

Filters

Company

Location

User

Date

Company — applied automatically. You only ever see your own company's data.
Location — narrows what you see to the locations you select.
Date — sets the reporting window, based on when each inspection ran.
User — narrows the card to the team members you select.

How to read it

One row per inspector. Click any column heading to sort by it — useful for finding the highest or lowest values fast.

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The percentage columns are the quickest read; the count columns next to them show the volume those percentages are based on.

What this card shows

Which locations resolve most of what they see, and which deny the most?
Is my resolution rate holding steady or drifting?
Where are customers most likely to have a claim turned down?

Columns

Inspector — the team member who ran the inspection.
Location — the name of each of your locations.
Issues Flagged — how many items this inspector flagged Red or Yellow.
Personally Resolved — how many of their own flags they resolved back to Green.
Resolution Rate % — the share of closed incidents that ended Resolved.

Questions this answers

Which locations resolve most of what they see, and which deny the most?
Is my resolution rate holding steady or drifting?
Where are customers most likely to have a claim turned down?

Why your numbers might differ

Your filters shape everything here. Narrowing the location or date range changes which records are counted, so two people with different filters set will see different figures.
Small numbers swing hard. When a figure is based on only a handful of records, one more or less moves it a lot.

Frequently ask questions