Reports – SupportCandy

General settings

Pradeep Makone — Fri, 29 Jul 2022 10:15:41 +0000

Overview

The Reports feature helps you monitor support performance and review ticket-related data over different time periods. It provides auto-generated reports for key ticket metrics and allows you to generate reports based on selected custom fields.

Where to Find

Go to Support → Settings → Reports to configure the reporting settings.
Generated reports can be viewed from Support → Reports.

How It Works

The Reports feature provides auto-generated reports for:

Ticket statistics
Response Delays
Ticket Closing Delays
Communication gaps

You can also generate reports for selected custom fields and apply filters to get more precise results.
Reports can be generated for different time periods, including:

Days
Weeks
Last 30 days
Quarters
Custom date selections

Reports can also be printed for a selected duration.

How to Configure

Go to Support → Settings → Reports.
Configure the Default Duration.
Select the custom fields under Generate reports for custom fields for which you want to generate reports.
Save the settings.
Go to Support → Reports to view the generated reports.

Options / Settings

Default Duration
Defines the default time period used when generating reports. When a report is generated, the selected duration is used by default.

Generate reports for custom fields
Allows you to select the custom fields for which reports should be generated. Reports for the selected custom fields will be available under Support → Reports.

Using Report Filters

You can apply custom filters to generated reports to obtain more precise results. Select the required time period or other available filters based on the information you want to analyze.

Notes / Tips

Reports can be generated for both standard ticket information and selected custom fields.
You can select a predefined time period or specify a custom date range.
Reports can be printed for the selected duration.

Ticket statistics

Pradeep Makone — Fri, 29 Jul 2022 10:16:39 +0000

Overview

The Ticket Statistics report shows the number of Tickets created and closed during a selected period. This helps you compare Ticket creation and closure trends over time.

Where to Find

Go to Support → Reports → Ticket Statistics.

How It Works

The report displays a graph with the following types of data:

Number of tickets created – Shows the total number of Tickets created during the selected period.
Number of tickets closed – Shows the total number of Tickets closed during the selected period.
Closure Rate (%) – Shows how much incoming ticket volume your team is closing in this period.
Net Ticket Change – Indicates whether your backlog is growing or shrinking in this period.

You can select a predefined duration or choose a custom date range. You can also apply custom filters to generate more specific results.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter
Custom date range

You can also use the custom filter to narrow the report based on Ticket fields.
For example, you can filter Tickets by:

Category
Priority
Status
Other default or custom fields

The custom filter allows you to set multiple conditions to get more precise results.

Notes / Tips

This report tracks volume, not speed or quality. It only compares tickets created vs. tickets closed over time — for how long tickets take to close, see the Ticket Closing Delay report instead.
Closure Rate can exceed 100%. If your team clears out a backlog (closing more tickets than were created in that period), the rate will read above 100% — that’s expected and a good sign, not an error.
Net Ticket Change is your backlog indicator. A positive number means tickets are piling up faster than they’re being closed; a negative (or zero) number means the backlog is shrinking or stable. Watch this trend over consecutive periods rather than a single snapshot.
There’s no agent, category, or status breakdown in this report — it’s a high-level trend line only. If closure rate drops, use the Ticket Statistics chart to spot when it happened, then drill into other reports (Closing Delay, Category, Agent-specific views) to find why.

Response delay

Pradeep Makone — Fri, 29 Jul 2022 10:17:29 +0000

Overview

The Response delay report shows how much time Agents take to respond to Tickets. It provides two response time measurements: First Response Delay and Average Response Delay.

Where to Find

Go to Support → Reports → Responce Delay

How It Works

The report displays a graph with the following types of data:

First Response Delay – Shows the average time an Agent takes to send the first response to a Ticket.
Average Response Delay –Shows the average time an Agent takes to send a response to a Ticket.

You can select a predefined duration or choose a custom date range. You can also apply custom filters to generate more specific results.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter
Custom date range

You can also use the custom filter to narrow the report based on Ticket fields.
For example, you can filter Tickets by:

Category
Priority
Status
Other default or custom fields

The custom filter allows you to set multiple conditions to get more precise results.

Notes / Tips

Only counts replies that follow a customer message. Agent replies that don’t come right after a customer message (e.g., internal follow-ups, agent-initiated tickets) aren’t counted — First Response Delay and Average Response Delay are purely “customer waited, then agent replied” gaps.
Tickets with no agent reply yet are excluded entirely, not counted as zero. If a ticket is closed without an agent ever replying, it’s skipped from this report’s averages rather than dragging the numbers down (or up).
Reopened tickets keep accumulating into the same average. If a ticket is reopened and gets more customeragent exchanges, each new agent response after a customer message adds another sample to that ticket’s average — there’s no reset per reopen cycle, so a heavily reopened ticket can pull its own average up or down significantly.
First Response Delay and Average Response Delay measure different things — FRD is just the first reply gap, ARD is the mean of all reply gaps on the ticket. A ticket can have a fast first response but a slow ARD if later replies lag, so check both rather than one in isolation.

Ticket closing delay

Pradeep Makone — Fri, 29 Jul 2022 10:17:59 +0000

Overview

The Ticket closing delay report shows the average time taken by support Agents to close Tickets. This report helps you understand Agent performance and the average time required to close Tickets.

Where to Find

Go to Support → Reports → Ticket Closing Delay

How It Works

The report displays a graph with the following types of data:

Average Closing Delay (Days) – Shows the typical number of days tickets take to close in this period.
Tickets Closed Count – Total tickets closed in this period.
Longest Closing Delay (Days) – Shows the longest time taken to close a ticket in this period.

You can select a predefined duration or choose a custom date range. You can also apply custom filters to generate more specific results.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter
Custom date range

You can also use the custom filter to narrow the report based on Ticket fields.
For example, you can filter Tickets by:

Category
Priority
Status
Other default or custom fields

The custom filter allows you to set multiple conditions to get more precise results.

Notes / Tips

Delay is measured from ticket creation to closing, not from last reply. A ticket that sat untouched for days before an agent even looked at it will show a high closing delay even if the actual resolution work was quick — pair this report with First Response Delay to tell the two apart.
Only tickets closed within the selected date range are counted, regardless of when they were created. A ticket opened in June but closed in July will appear in the July report, not June’s.
A single old/stuck ticket can skew the average. Check the “Longest Closing Delay” figure alongside the average — if it’s far higher than the average, one or two outliers (e.g., a ticket left open for weeks) may be distorting the picture. Filter by status/category to isolate them if needed.
Lower isn’t always better in isolation. A very low average closing delay combined with a low ticket count may just mean few tickets were closed, not that resolution is fast — cross-check against ticket volume for context.

Communication gap

Pradeep Makone — Fri, 29 Jul 2022 10:18:22 +0000

Overview

Communication gap shows the average number of threads exchanged between the Agent and Customer before a Ticket is closed. This report helps you understand how effectively Agents communicate with Customers and how accurately they convey information.

Where to Find

Go to Support → Reports → Communication Gap

How It Works

The report displays a graph with the following types of data:

Average communication gap for this period – Shows the typical amount of back-and-forth communication per closed ticket.
Tickets Closed Count –
Shows how many closed tickets were included in this report.
Longest Communication Gap – Shows the highest back-and-forth communication seen in a single closed ticket.

You can select a predefined duration or choose a custom date range. You can also apply custom filters to generate more specific results.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter
Custom date range

You can also use the custom filter to narrow the report based on Ticket fields.
For example, you can filter Tickets by:

Category
Priority
Status
Other default or custom fields

The custom filter allows you to set multiple conditions to get more precise results.

Notes / Tips

This measures message count, not time. Despite the name, “Communication Gap” here counts the number of reply/report threads exchanged on a ticket (agent + customer combined) — it’s not a time delay like Response Delay or Closing Delay. A high value means a lot of back-and-forth, not a long wait.
Tickets with just one reply still count, at zero or one. A ticket resolved in a single exchange isn’t excluded from the average — it pulls the average down, so a low “Average Communication Gap” can simply mean most tickets get resolved quickly with minimal back-and-forth, which is generally a good sign.
Only closed tickets are included, filtered by date_closed in the selected range — tickets still open won’t affect this report’s numbers even if they already have a long thread history.
A high “Longest Communication Gap” flags tickets worth reviewing. Since it’s a raw thread count, a large outlier here usually points to a complex, escalated, or repeatedly-reopened ticket — worth checking manually rather than assuming it’s a process problem.

Other Reports

Milind Ighe — Fri, 10 Mar 2023 11:47:12 +0000

Overview

Other Reports allow you to generate reports based on selected Ticket fields. You can generate reports for supported custom fields, as well as Category, Priority, Rating, and SLA.

Supported Custom Fields

Reports can be generated for custom fields with the following field types:

Single select
Multiple select
Checkbox
Radio button

Supported Fields

You can also generate reports for the following fields:

Category
Priority
Rating
SLA

Add Custom Fields for Reports

To add a custom field for reporting, go to Support → Settings → Reports → Generate reports for custom fields.
Select the custom fields for which you want to generate reports.
The generated reports for the selected custom fields are available at Support → Reports.

Report Duration

You can select a predefined duration to generate the report, such as Last 30 days, This week, Last quarter, and other available periods.
You can also select custom dates to generate the report for specific days.

Custom Filter

Use the Custom Filter to generate a report for Tickets that match specific conditions.
You can filter Tickets using Category, Priority, Status, and other custom fields or default fields. The Custom Filter allows you to add multiple conditions based on Ticket fields.

AI Usage by Agent & Feature

Nikhil Gawali — Wed, 19 Aug 2026 07:01:56 +0000

Overview

The AI Usage by Agent & Feature report gives you visibility into how your team is using built-in AI capabilities (Reply Polish and Ticket Summary). It breaks down usage two ways — by agent, to see who’s relying on AI most, and by feature, to see which AI capabilities are used most — along with the token volume each generates, so you can track adoption and gauge relative usage without needing cost data.

Where to Find

Go to Support → Reports → AI Usage

How It Works

The report displays a graph with the following types of data:

Usage by Agent
This section lists AI usage broken down by each agent. For every agent, it shows how many AI requests they made in the selected period, the total number of tokens consumed across those requests, and the average tokens per request (total tokens divided by request count, rounded up). This tells you which agents are relying on AI features most, and how “heavy” their typical AI usage is.
Usage by Feature
This section shows the same usage data, but grouped by AI feature instead of by agent — currently either Reply Polish or Ticket Summary. For each feature, it shows how many times it was used in the selected period, the total tokens consumed by that feature, and the average tokens per use. This tells you which AI capabilities are being used most, and how much each one typically costs in tokens.

Together, Usage by Agent answers “who is using AI,” while Usage by Feature answers “what AI capabilities are being used.”

You can select a predefined duration or choose a custom date range. You can also apply custom filters to generate more specific results.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter

Notes / Tips

Only two features are currently tracked: Reply Polish and Ticket Summary. Any AI action outside these isn’t broken out separately yet — it’s bucketed under “Ticket Summary.”
This report measures tokens, not cost. There’s no dollar/currency figure — to estimate spend, apply your AI provider’s per-token pricing to the token totals yourself.
A rising average tokens per request is worth investigating, not necessarily alarming. It can mean longer tickets or prompts are being summarized or polished — check a few underlying logs before assuming inefficiency.
Usage by Agent reflects who triggered the AI action, not who owns the ticket. An agent who leans on AI to draft replies will show high usage here even if a different agent ultimately handles and closes the ticket.

AI Usage Trend

Nikhil Gawali — Wed, 19 Aug 2026 09:11:35 +0000

Overview

The AI Usage Trend report shows how AI feature usage changes over time. Instead of a snapshot broken down by agent or feature, it plots request volume and token consumption across your selected date range, so you can see whether AI usage is growing, shrinking, or spiking at particular points in time.

Where to Find

Go to Support → Reports → AI Usage Trend

How It Works

The report displays a graph with the following types of data:

Requests — the count of AI feature calls (Reply Polish, Ticket Summary) made in each time bucket
Tokens — the total tokens consumed across all AI calls in each time bucket

You can select a predefined duration or choose a custom date range. You can also apply custom filters to generate more specific results.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter

Notes / Tips

Requests and Tokens can move independently. A spike in requests with only a modest rise in tokens suggests many short/simple AI calls; a rise in tokens without more requests suggests fewer but heavier (longer) calls — read the two lines together, not in isolation.
This is a volume trend, not a per-agent or per-feature breakdown. To see who or what is driving a spike you notice here, cross-check against the AI Usage by Agent & Feature report for the same period.
Granularity affects what patterns you can see. A daily view will smooth out hour-of-day spikes (e.g., AI usage clustering around peak support hours) — switch to a shorter duration if you’re trying to spot intraday patterns.
No cost data is shown. Like the Agent & Feature report, this only tracks tokens and requests — apply your provider’s token pricing separately if you need to estimate AI spend over time.

Chat Sessions Trend

Nikhil Gawali — Wed, 19 Aug 2026 09:21:35 +0000

Overview

The Chat Sessions Trend report tracks how many chatbot sessions are being started over time. It plots session volume across your selected date range, giving you a simple view of chatbot engagement trends — whether usage is growing, holding steady, or spiking — without breaking sessions down by outcome (resolved, escalated, or abandoned).

Where to Find

Go to Support → Reports → Chat Sessions Trend

How It Works

The report displays a graph with the following types of data:

Sessions — the count of chatbot sessions started in each time bucket, regardless of their final status (active, resolved, handed off, or abandoned)

This is plotted as a single-line chart over the selected period, bucketed by day/week/month/year depending on the duration you choose.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter

Notes / Tips

This counts sessions started, not sessions completed or resolved. A session in any state — ongoing, abandoned, resolved by the bot, or escalated to an agent — is counted the same way here. For a resolved-vs-escalated breakdown, use the AI Resolution Trends / Escalation Rate report instead.
A spike in sessions doesn’t tell you if the bot handled them well. Pair this trend with the Escalation Rate report for the same period to see whether rising volume is also driving more human handoffs.
Sessions with no messages are still counted. A session record is created as soon as a chat starts, so an abandoned session where the visitor never sent a message still shows up in this count.
Granularity affects what you can spot. A weekly or monthly view smooths out short spikes (e.g., a burst of sessions during a product outage) — switch to a daily or hourly duration if you’re investigating a specific event.

Chat Reaction Summary

Nikhil Gawali — Wed, 19 Aug 2026 09:28:15 +0000

Overview

The Chat Reaction Summary report shows how visitors rated their chatbot conversations. At the end of each chat session, visitors can mark it as helpful () or not helpful (); this report aggregates those reactions over your selected date range, letting you track satisfaction trends and spot periods where the bot’s performance may have dipped.

Where to Find

Go to Support → Reports → Chat Reaction Summary

How It Works

The report displays a graph with the following types of data:

Happy — count of sessions marked “Helpful” in each time bucket
Unhappy — count of sessions marked “Not Helpful” in each time bucket

Both are plotted as a stacked bar chart over the selected period, bucketed by day/week/month/year depending on the duration you choose, so you can see reaction volume and the happy-vs-unhappy split at a glance.

Filtering the Report
You can generate the report for different time periods, such as:

Last 30 days
This week
Last quarter

Notes / Tips

This is a per-session rating, not per-message. Visitors rate the overall conversation once at the end, not each individual bot reply — so this report reflects overall session satisfaction, not which specific response caused a good or bad reaction.
Sessions with no reaction aren’t counted at all. If a visitor closes the chat without rating it, that session doesn’t appear in either the Happy or Unhappy count — the totals only reflect sessions where a rating was actually given, so a low bar count can just mean low rating engagement, not low chat volume.
A “Not Helpful” reaction can trigger ticket escalation, so a rise in Unhappy sessions may also show up as increased ticket creation from chat — worth cross-checking against ticket volume if you see a spike here.
Only the latest reaction is stored per session — there’s no history of a rating being changed, and no neutral/partial rating option exists between Happy and Unhappy.