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Reports and data | Metrics, exports and Report Builder

Standard reports, Report Builder, exports and raw data.
Tech
Fabrício Rissetto
By Tech and 5 others
• 15 articles

Reports - Service Time Metrics

Image When to Use - You need to monitor ticket waiting time in queue (TME) - You want to track response time when the ticket is assigned to an agent (TMA) - You want to know the total time from creation to resolution of the ticket (TMR) - You want to differentiate "clock time" from "within business hours" time - You want to compare service time metrics across your teams and agents Prerequisites - Be logged in as supervisor, CX Engineer, or administrator Overview Every conversation in Cloud Chat goes through three moments in order: 1. Waiting — in the queue, without an responsible. 2. Attending — assigned to an agent (human or AI). 3. Resolved — finished. For each resolved conversation, the system calculates three metrics: | Acronym | What it measures | | --- | --- | | TME | How long the conversation stayed in the queue waiting | | TMA | How long the conversation was under an agent’s responsibility | | TMR | Total time from creation to final resolution | :::info These metrics are only calculated for resolved conversations. Open conversations appear with empty fields — until finished, the time continues to run and no final value is shown. This helps prevent biasing the averages. ::: Elapsed Time vs. Business Hours Time Each of the three metrics has two versions: - Elapsed Time — clock, 24/7. - Business Hours Time — only counts time spent inside the conversation’s designated operating hours. The operating hours are configured in this section of Cloud Chat Image Practical Example A conversation started Friday at 5 pm and resolved Monday at 10 am, with an inbox that operates Mon–Fri from 9 am to 6 pm: - Elapsed time: ~65 hours (including the full weekend) - Business hours time: ~1 hour (1h on Friday before closing + 1h on Monday after opening) When to use each - Elapsed Time → shows the customer’s real experience. They waited through the entire weekend. - Business Hours Time → indicates operational performance. The team only had 1 hour of effective response time. Use business hours time for targets and SLAs. Use elapsed time to understand the customer perception. :::info The considered business hours are those of the Inbox that received the conversation. ::: TME — Waiting Time What it is Total time the conversation was in the queue without an responsible. When the clock starts - When the conversation is created (enters the queue), OR - When the conversation loses responsible after being assigned to an agent (unassigned). When it stops As soon as someone (human or AI) is assigned. Conversation with multiple waiting periods If the conversation went through multiple cycles (created → agent A picked → agent A unassigned → back in queue → agent B picked), all waiting periods are summed. Why is my conversation’s waiting time = 0? Indicates that no one waited — the conversation was assigned immediately at creation. In cases where the conversation was resolved solely by AI, for example, there’s no waiting time because AI does not keep the conversation in the queue. TMA — Response Time What it is Total time the conversation was under an agent’s responsibility (human or AI). When the clock starts When the conversation switches from "without responsible" to "with responsible". When it stops - When the agent resolves the conversation, OR - When the conversation loses responsible (returns to the queue). Conversation handled by more than one agent If the conversation moved from agent A to agent B, both periods are summed into the response time. Which agent is considered in report filters? The final agent — the responsible agent at the moment of resolution. Previous agents do not appear there. This means that if a conversation passed through multiple agents, the response time is assigned to the final one. The agent type column indicates whether this final agent was AI or Human. :::warning If you filter by agent, you are filtering by the last agent, not all involved. ::: TMR — Resolution Time What it is Total time between creation of the conversation and final resolution. → TMR = resolution moment − creation moment Relationship with TME and TMA In simple conversations: → Resolution time ≈ Waiting time + Response time What if the conversation was reopened? The system considers the last resolution. If the conversation was: - Created at 10 am - Resolved at 11 am (1h) - Reopened at 2 pm - Resolved again at 4 pm The resolution time will be 6 hours (10 am → 4 pm), not just 1 hour. :::warning Thus, the number may vary between reports. A conversation with a TMR of 1 hour today can show as 6 hours tomorrow if reopened and closed again. Time data is not static — it reflects the most recent state of the conversation. ::: Numeric Example for Illustration To clarify everything, let’s review an end-to-end conversation. Scenario - Inbox: operates from Monday to Friday, 9 am to 6 pm (closed on weekends) Timeline of the conversation | When | What happened | | --- | --- | | Thursday, 5 pm | Customer opens the conversation → enters queue | | Thursday, 5:15 pm | Agent Ana is assigned → starts attending | | Friday, 2:30 pm | Ana resolves the conversation | What happens to each metric Viewing through the Inbox clock (which operates 9 am to 6 pm, Mon–Fri): Thursday: - 5 pm → 5:15 pm → waiting in queue (within business hours) - 5:15 pm → 6 pm → in attending with Ana (within business hours) - 6 pm → 12 am → continues assigned to Ana, but outside business hours Friday: - 12 am → 9 am → still assigned to Ana, outside business hours - 9 am → 2:30 pm → attending with Ana (within business hours) - 2:30 pm → Ana resolves Calculation of the three times TME — Waiting Time - Single waiting period: 5 pm → 5:15 pm Thursday (15 minutes) - Whole within business hours. | Metric | Value | | --- | --- | | Elapsed waiting time | 15 minutes | | Business hours waiting time | 15 minutes | TMA — Response Time - Single attending period: 5:15 pm Thursday → 2:30 pm Friday - Thursday 5:15 pm → midnight = 6h45 - Friday 12 am → 2:30 pm = 14h30 - Total: 21h15 ≈ 1,275 minutes Business hours: - Thursday 5:15 pm → 6 pm = 45 min - Friday 9 am → 2:30 pm = 5h30 - Period from 6 pm Thursday to 9 am Friday is discarded (outside business hours) - Total: 6h15 ≈ 375 minutes | Metric | Value | | --- | --- | | Elapsed response time | 1,275 minutes (~21h) | | Business hours response time | 375 minutes (~6h) | TMR — Resolution Time - Creation at 5 pm Thursday → resolution at 2:30 pm Friday. Elapsed: - Thursday 5 pm → midnight = 7h - Friday 12 am → 2:30 pm = 14h30 - Total: 21h30 ≈ 1,290 minutes Business hours: - Thursday 5 pm → 6 pm = 1h - Friday 9 am → 2:30 pm = 5h30 - Total: 6h30 ≈ 390 minutes | Metric | Value | | --- | --- | | Elapsed resolution time | 1,290 minutes (~21h30) | | Business hours resolution time | 390 minutes (~6h30) | Does the sum check out? → TME + TMA = TMR - Elapsed: 15 + 1,275 = 1,290 ✓ - Business hours: 15 + 375 = 390 ✓ In this example, the sum matches because the conversation had only one waiting and one attending period. In cases with transfers or returns to the queue, small gaps between events may cause the total TMR to differ slightly from TME + TMA. What does this example teach you? 1. Outside business hours "disappear" in business hours metrics but not in elapsed time. Thus, business hours numbers are usually smaller. 2. "Delaying" and "mark as pending" do not improve response time — the clock keeps running. To stop the clock, the conversation must change responsible (return to the queue or transfer). 3. The agent recorded in the conversation is the final responsible agent — in this case, Ana, who handled and resolved it. If Ana had transferred it back, and Bruno finished it, the line would show Bruno as responsible, even though Ana worked on it. Frequently Asked Questions Why do some conversations have empty time fields or are not considered in calculations? Because the conversation has not yet been resolved. Times are only calculated at a final closing. Why is the business hours time less than elapsed time? Because business hours exclude: - Early mornings - Weekends (if the Inbox doesn’t operate then) - Public holidays set as closed - Any moments outside the operating window If the entire conversation occurred within business hours, both values are equal. Can I have business hours time = 0 and elapsed time > 0? Yes. It means the conversation was completely handled outside business hours — for example, by AI overnight. The elapsed time exists, but no minutes fall within the useful window of the Inbox. Which agents or teams are considered in filters? In all filters — Agent, Team, Inbox — the system considers only the current state of the conversation after resolution. If a conversation involved multiple agents or teams, only the last appears in the filters. This applies to TME, TMA, and TMR. What timezone does the system use? The timezone of the Inbox that received the conversation — not the customer’s or agent’s timezone. Do AI-resolved conversations appear in the same reports? Yes. Metrics work the same for AI or human agents. The agent type column identifies the final responsible agent, allowing for segmentation. Why did the time of a conversation change from one day to another? Likely because the conversation was reopened and resolved again. The system always considers the last resolution — if a conversation closed today, reopened tomorrow, and closed again after tomorrow, the resolution time reflects this longer interval. Why are past metrics changing? Because only resolved conversations are considered. If a conversation was created earlier, lasted several days, and was only resolved today, its time is calculated based on the most recent resolution date — retroactively updating previous metrics. Older tickets tend to have fewer reopenings, but data can still change as history updates. In short: report data is not static — it can change as conversations are reopened or closed. Is the time spent in "Delayed" or "Pending" status counted in response time? Yes. These statuses do not remove the responsible agent — the agent remains assigned, so TMA continues to accumulate. Notes - For real-time overview (not historical): How to manage operations in real-time with the Dashboard report and Advanced Monitoring - To configure SLA policies based on these times: How to set up SLA and response time metrics - For batch data extraction via API: How to extract data in bulk via API (Data Extract API)

Last updated on Sep 02, 2026

How to export contacts (only those who already chatted) and cross-check with another list — no code

When to use it - You want to export only the contacts who have already chatted with your support (and not your entire base) - You want to cross-check your list (or a CSV from another system/CRM) against the Cloud Chat base to see which numbers already exist - You want to do this without using an API or any programming About this article Many of these extractions don't need an API: the Report Builder already builds the list with a click and exports it as a spreadsheet. Here we show the two most common scenarios. For the complete step-by-step of the tool, see the Complete guide to the Report Builder. :::info The Report Builder is under Reports → Report Builder. Access is available to Administrator, Supervisor or CX Engineer profiles. ::: Export only those who already chatted Use this when you want the list of contacts who had at least one conversation (for example, to compare it against another system). 1. Open Reports → Report Builder. 2. Choose the Cloud Chat topic and the Conversations table. 3. Select the contact fields you need — for example Phone, Name and Identifier (the Identifier is the contact's ID in your own system, useful for matching against your CRM). 4. (Optional) Apply filters — period, channel, conversation status. 5. Click Export and choose CSV or Excel. The result brings only the contacts who appear in some conversation — that is, those who actually talked to support. Cross-check your list against the base (which ones already exist) Use this when you have a list/CSV (e.g., from another CRM) and want to know which of those numbers are already in Cloud Chat. 1. In the Report Builder, Cloud Chat topic, Contacts table. 2. Select Phone (and Identifier, if you use it) and click Export (CSV/Excel). 3. Open your list and the export in a spreadsheet (Excel or Google Sheets). 4. Use VLOOKUP (or XLOOKUP) by phone number to flag which numbers from your list already appear in the base. :::warning For the cross-check to match, the phone numbers must be in the same format on both sides — with +55 and the mobile 9 (e.g., +5511960832431). ::: Lots of numbers (thousands)? An alternative is to use the CSV import itself: when importing, Cloud Chat updates the contacts that already exist and creates the new ones, matching by phone number. That way you can tell who was already in the base from the import result. See How to import contacts in bulk via CSV. Notes - For the complete step-by-step of the Report Builder (advanced filters, pivot table, export): Complete guide to the Report Builder - Need a very specific cut and couldn't find the field? Talk to the Cloud Humans team through the support chat.

Last updated on Jul 22, 2026

How to use the new Report Builder Agent for queries and reports

The Report Builder Agent is Cloud Chat's AI assistant that lets you query data, generate visualizations, and build reports using natural language — without writing any SQL. 1. How to access the Agent 1. Open the Report Builder (v2) 2. At the top of the left sidebar, click Agent. Once you click, the Agent's home screen opens, with the Ask a question about your data field at the bottom. 2. How to run a query 1. Click the text field at the bottom ("Ask a question about your data"). 2. Type your question in natural language. Examples: - "How many conversations were created in the last 30 days?" 3. Click the send button (the ↑ arrow to the right of the field) or press Enter. 3. How the Agent responds The Agent shows each step of its reasoning (which topic it searched, which fields it used) and delivers the answer along with the visualization. You can follow the steps as they appear. 💡 If the answer returns no data, check whether your account has the project's attributes/permissions set up — the Agent respects the user's permissions. 4. Handy features during the conversation - Choose the AI model: at the bottom of the field, the Standard selector lets you switch between Faster, Standard, and Smarter (speed vs. quality). - Attach context: click the + icon to send files or images that help the Agent. - Refine: ask follow-up questions (e.g., "And just the last 7 days?"). - Explore: opens the query directly so you can review it and save it inside the folder. 5. How to create reports and dashboards After exploring the data, you can ask the Agent to build a dashboard: 1. Ask directly in the chat: "Create a dashboard with this data" or "Build a panel of conversations per day". 2. The Agent generates the dashboard and saves it in the Meus documentos (My Documents) folder. 6. How to share - Share the conversation: Save the dashboard or the query Tips for better answers - Be specific: include the time period, metric, and breakdown in your question. - Prefer iterating on short questions over building one giant question. - Double-check the generated query before using the number in important decisions.

Last updated on Jul 22, 2026

How to use the Report Builder — complete guide

When to use it - You want to create custom reports in the Report Builder v2 version (with pivot, joins, PoP) - You need advanced filters (across queries, multiple conditions) - You want to use AI to generate queries and visualizations Prerequisites - Be logged in as a supervisor, CX Engineer or administrator :::info For the classic (previous) version: How to use the Report Builder. To learn about the available tables and columns: Explanation of the Report Builder columns. ::: About this article Welcome to the New Report Builder (v2) FAQ. This guide covers everything from field selection and creating filters to advanced features such as pivot tables, joins between tables, period-over-period (PoP) analysis and dynamic controls. First steps - First Steps — New Report Builder - Using the new builder with AI Field selection What are dimensions and measures? - Dimensions — groupings of the query (date, class, type, etc.) - Measures — aggregations over those dimensions (sum, count, average, etc.) Example: to query the total number of tickets per day, select a date dimension and a ticket count measure. How to search for fields more easily Use the search box above the field selector. By default it searches across fields and tables. You can refine it with: - In use — only fields selected or filtered in the current query - Field type — boolean, number, etc. - Topic — limits to the fields of selected topics - Label — filters by presence or absence - Descriptions — filters by presence or absence Advanced search operators | Operator | Description | Example | | --- | --- | --- | | data_type: | Filters by data type (date, string, boolean, number) | data_type:date | | is: | Filters by dimension, measure or hidden field | is:dimension | | model_type: | Filters by model type (schema, shared, branch) | model_type:schema | | schema: | Filters by schema | schema:marketing | | tag: | Filters by tag | tag:secure | Advanced actions on a field By hovering and clicking the three dots (or right-clicking), you get access to: - Pivoting - Aggregations - Filters - Primary key definition - Hiding the field - Duration creation (for timestamps) Filters Each dimension can be used as a filter. Different options appear for dates, numbers and text. Multiple values Separate the values with a comma: [bread, cheese]. If the value contains a comma, escape it with \: São Paulo\, SP. Date filters You can use: - Absolute dates — 2022, 2023-01-01, 2021-Q4 - Relative dates — after 7 complete days ago, 1 year ago - Flexible ranges — [start][duration] format Examples # First 10 days of 2023 [2023-01-01][10 days] # 100 days ago for 10 days [100 complete days ago][10 days] # 1 hour ago for 2000 milliseconds [1 hour ago][2000 milliseconds] Accepted time units | Type | Accepted units | | --- | --- | | Day | day, days, complete day, complete days | | Week | week, weeks, complete week, complete weeks | | Month | month, months, complete month, complete months | | Quarter | quarter, quarters, complete quarter, complete quarters | | Year | year, years, complete year, complete years | | Millisecond | millisecond, milliseconds | | Second | second, seconds | | Minute | minute, minutes | | Hour | hour, hours | Filter one tab by the results of another With two or more tabs open, you can use the results of one as a dynamic filter in another. 1. Create a tab with the definition of the subset that will serve as the filter 2. In another tab, locate the corresponding dimension 3. Click the three dots on the dimension and select Filter → Is from another query 4. Choose the reference tab and dimension Multiple conditions on the same field Filter by multiple conditions (e.g. date in the last 30 days OR null) by clicking Add condition in the filter modal. Then choose whether all or any of the conditions must be applied. Pivot (pivot table) How to create it Right-click any field in the selector and choose Pivot. Flatten pivot Turns pivot columns into individually filtered measures. You can create an "Others" grouping during the process. After flattening, the new measures appear under the query fields and the pivoted field is no longer active. Pivot limits :::info Supports up to 200 columns in a pivot. ::: Row totals In Options, enable Row Totals. It adds a total column on the right. Only available in queries with at least one pivot. Group measures In Options, enable Group Measures in Pivot. It organizes the measures together within each group. Fill null cells Right-click the table and select Fill Missing Cells in Grid to replace nulls with zero. Useful when using SUMIFS over pivoted tables. Custom fields and aggregations How to create them Use the + button in the field selector or the right-click menu on a field. Quick aggregations Right-click a field and select Aggregations. | Field type | Dimensions | Measures | | --- | --- | --- | | Dates | Date, Week, Month, Year, Second, Minute, Hour, Hour of Day, Day of Week, Day Number, Day of Month, Month Name, Month Number, Quarter, Quarter of Year | Minimum, Maximum | | Text | — | Distinct Count, List | | Numbers | — | Distinct Count, Sum, Average, Minimum, Maximum, List | | Booleans | — | Count Trues, Count Falses | Duration feature Available on timestamp fields — it creates a field that calculates the time difference between two timestamps in configurable intervals. Results table Row limit By default, 1,000 rows. It can be adjusted dynamically up to 50,000 rows with pagination. It only affects the display — not the rows processed in the database. Select cells - Click a cell to select it - Click and drag to select a region - Shift + click to select a range - Cmd/Ctrl + click to add/remove - Click the row number → selects the entire row - Click the column header → selects the entire column - Cmd/Ctrl + A → selects everything - Escape or click outside → clears the selection Copy data Select and press Ctrl+C (Windows) or Cmd+C (Mac). The data comes in tab-separated format (compatible with Excel and Google Sheets). With Cmd/Ctrl+A, the column headers are included. Edit calculation cells Press Enter or double-click the cell. To save: Enter. To cancel: Escape. Shift + click inserts a range reference into the formula. Reorder columns Drag the header to the desired position. :::warning Not available in pivot tables. ::: Display options In Options: - Column width (auto or manual) - Header text (clipped or wrapped) - Show the view name above the header - Enable column totals - Enable row totals (pivots only) - Transpose rows and columns - Group measures in pivots Column totals When you enable Column Totals, a total row appears in the footer. Right-click the total to customize it (switch from sum to average, for example). :::info Columns with formulas also have totals — the system uses the formula itself to calculate the total automatically. ::: Download the results Click Tab → Download or the download icon. - Format: CSV, XLSX or JSON - Apply data formatting — preserves number and date formatting - Row limit — current limit, all possible rows, or a custom value :::info The XLSX format supports up to 1 million rows. ::: Sorting by multiple fields Sort the first field normally. Then, hold Shift while clicking the following fields to add secondary sorts. Period-over-Period Analysis (PoP) What it is A business intelligence technique that compares metrics from a recent period with the same period in the past (this month vs. previous month). How to enable it In the field selector, click the three dots of a date dimension and select Period over Period. Two selectors appear above the table: one for the current period and another for the comparison period. Above the results panel, two dropdown menus: select the current period in the first and the comparison period in the second. By default, comparison options are based on relevant ranges — you can customize them. Customize comparison periods The periods have default options, but you can customize them freely. The exact parameters are visible in the information icon on the table. Filling in missing data How to fill in missing rows Right-click the dimension and select Fill Missing Rows. :::info For dates, make sure to apply a filter with a well-defined period (e.g. 2023-01-01 to 2023-02-01). Filling is limited to 8,000 values. For more complex cases, ask the Cloud team for support. ::: Supported field types - Dates with a defined range - Enumeration fields (bins and groups defined in the data model) Notes - For the classic version of the builder: How to use the Report Builder - To learn about the available tables and columns: Explanation of the Report Builder columns - To use standard reports instead of customizing: Overview of the Cloud Chat standard reports - To extract data via API: How to extract data in batch via API (Data Extract API)

Last updated on Jul 22, 2026

Explanation of the tables and columns in the Report Builder — ClaudIA and Cloud Chat topics

When to use - You are building a report in the new reports and need to understand which tables and columns are available in the ClaudIA and Cloud Chat topics - You want to understand what each field means before using it in an analysis - You need to identify the right table for a specific analysis (conversations, messages, CSAT, campaigns, agent and team metrics) About this article A quick reference of the tables and columns in the new reports module, organized by topic: - ClaudIA topic — data from conversations handled by ClaudIA (AI): conversations, messages, generated responses, audit feedback, CSAT, and knowledge base content - Cloud Chat topic — operational data from Cloud Chat: conversations, first responses, CSAT, tags, contacts, active outbound sends (campaigns), and daily agent and team metrics :::info Columns ending in Sk are internal keys used to relate the tables to one another — they are not listed below. The Etl Updated At column, present in several tables, indicates when the record was last updated in the data lake. ::: ClaudIA topic 1. claudia_conversation Main table of conversations handled by ClaudIA. Each row is a conversation. | Column | Description | | --- | --- | | Cloudchat Id | Identifier (UUID) of the conversation in ClaudIA — it is the link with the Conversation Uuid column of Cloud Chat | | Helpdesk Id | Identifier of the ticket in the integrated helpdesk (e.g., Zendesk) | | Creation Date / Update Date / Close Date | Creation, update, and closing dates | | Status Class | Conversation status | | Is N2 Flag | Whether the conversation was escalated to human support (N2) | | Resolution Level Class | Level at which the conversation was resolved (N1 = AI, N2 = human) | | Conversation Id | Internal conversation identifier | | Project Name | Project (customer) the conversation belongs to | | Application Identifier Class | Source channel/application of the conversation | | Low Confidence Count Value | Low-confidence responses in the conversation | | Enlightenment Question Count Value | Clarification questions asked by the AI | | Resolution Retries Count Value | Resolution attempts | | Problem Resolved Question Sent Count Value | How many times the AI asked whether the problem was resolved | | Unanswered Ticket Escalator Count Value | Escalations due to lack of response from the customer | | Activeintent Class | Active intent identified in the conversation | | Active Flow Id / Active Flow Session Id | Active flow (Eddie) and flow session | | Problem Solved Flag | Whether the customer confirmed that the problem was resolved | | Resolution Reason | Reason for resolution/closing | | Resolution Reason Refined | Refined resolution reason (normalized version) | | Tag Name | Tag assigned to the conversation | | Ab Key Name | Key of the A/B test the conversation participated in | | Gencx Value | GenCX score — automated evaluation of experience quality | | Gencx Reasoning Text | Justification for the GenCX score | | Frustration Score Value / Frustration Class | Customer frustration score and classification | | Frustration Reasoning Text | Justification for the frustration classification | | Frustration Score Update Date | When the frustration score was updated | | Has Rate Limit Error Flag | Whether there was a request rate limit error in the conversation | 2. claudia_messages All messages exchanged in ClaudIA conversations. | Column | Description | | --- | --- | | Message Id | Message identifier | | Project Name | Project (customer) | | Cloudchat Id | Conversation the message belongs to | | Sender Role | Who sent it (customer, AI, or agent) | | Content Text | Message content | | Send At | Send date and time | | Private Flag | Whether it is a private note (not visible to the customer) | | Message Type | Message type | | Originated From Answer / Originated From Action | Response or action that originated the message | | Originated From Enlightenment Limit Flag | Whether it was originated by the clarification question limit | | Originated From User Query | Customer question that originated the message | | Originated From Class | Origin classification | | Media Class | Media type (text, image, audio, etc.) | 3. claudia_prompt_revision Each row is a response generated by the AI — useful for analyzing ClaudIA's behavior response by response. | Column | Description | | --- | --- | | Prompt Revision Id | Identifier of the generated response | | Cloudchat Id | Conversation the response belongs to | | Project Name | Project (customer) | | Application Identifier | Source channel/application | | Response Action Class | Action decided by the AI (reply, escalate, hold, etc.) | | Enlightenment Limit Reached Flag | Whether the clarification question limit was reached | | Originated From User Query | Customer question that originated the response | | Response Class | Response classification | | Created At / Updated At | Creation and update dates | | Response Raw Final Answer | Final response generated by the AI | | Conversation Active Flow Id / Conversation Active Session Id | Flow (Eddie) and session active at the time of the response | | Response Bot Flow Id / Response Bot Session Id | Flow and session triggered by the response | | Response Used Content Id | Main knowledge base content used in the response | | Response Used Content Id 1/2/3 | Other content considered in the response | | Clarification Content Id 1/2/3 | Content used in clarification questions | | Response Model Reasoning | Model reasoning when generating the response | | Response Error Message | Error message, when generation failed | | Frustration Score / Frustration Label / Frustration Reasoning Text | Customer frustration at the time of the response | 4. claudia_conversation_feedback Audit feedback for ClaudIA conversations (quality evaluations). | Column | Description | | --- | --- | | Conversation Feedback Id | Feedback identifier | | Cloudchat Id | Evaluated conversation | | Project Name | Project (customer) | | Client Problem Solved Flag | Whether the evaluator considered the problem resolved | | General Feedback Text | General feedback about the conversation | | Error Impact Value | Impact of the identified error (score) | | Conversation Properly Closed Flag | Whether the conversation was closed correctly | | Conversation Should Scaled To N2 Flag | Whether it should have been escalated to N2 | | Conversation Should Resolved N1 Flag | Whether it should have been resolved at N1 | | Is Tag Correct Flag / Right Tag Name | Whether the tag is correct and the suggested tag | | Created By | Author of the feedback | | Created At / Updated At | Creation and update dates | 5. claudia_csat CSAT responses for conversations handled by ClaudIA. | Column | Description | | --- | --- | | Csat Id | Survey identifier | | Project Name | Project (customer) | | Cloudchat Id | Associated conversation | | Helpdesk Id | Ticket in the integrated helpdesk | | Csat Raw Value | Raw value of the customer's response | | Csat Value | Numeric score | | Comment Text | Customer comment | | Creation Date / Collect Date | Survey creation and response collection | 6. claudia_contents ClaudIA knowledge base content. | Column | Description | | --- | --- | | Content Id | Content identifier | | Project Name | Project (customer) | | Content Title / Content Text / Content Response Text | Content title, text, and response | | Tag Name | Tag applied when the content is used | | Is N2 Flag | Whether the content directs the conversation to N2 | | Topic Name | Content topic | | Content Type | Content type | | Flow Id | Flow (Eddie) triggered by the content, when applicable | | Source Metadata Class | Content origin | | Draft Flag / Disabled Flag / Excluded Flag | Whether it is in draft, disabled, or excluded | | Created At / Updated At | Creation and update dates | Cloud Chat topic 7. cloud_chat_conversations Main table of Cloud Chat conversations. Each row is a conversation (ticket), already with time and volume metrics consolidated. | Column | Description | | --- | --- | | Conversation Id | Internal conversation identifier | | Conversation Display Id | ID that appears in the conversation URL (e.g., .../app/accounts/1/conversations/{conversation_display_id}) | | Conversation Uuid | Identifier (UUID) of the conversation — it is the link with the Cloudchat Id column of the ClaudIA topic | | Account Id / Account Name | Associated account | | Customer Id / Instance Id | Environment (tenant) identifiers | | Inbox Id / Inbox Name | Inbox (channel) | | Team Id / Team Name | Assigned team | | Assignee Id / Assignee Name / Assignee Email | Responsible agent | | Agent Type | Agent type (human or AI) | | Ai Agent Flag | Whether the conversation was handled by an AI agent | | Contact Id / Contact Inbox Id | Associated contact | | Campaign Id | Campaign that originated the conversation, when applicable | | Status Code | Status (open, resolved, pending, snoozed) | | Priority Code | Conversation priority | | Tags List | List of conversation tags | | Csat Value | Conversation CSAT score | | Csat Sent Flag / Csat Responded Flag | Whether the CSAT was sent and answered | | Csat Response Submitted At | When the CSAT was answered | | Created At / Updated At | Creation and update dates | | Client First Message At | Customer's first message | | First Reply Created At | First reply to the customer | | Time To First Response Seconds/Minutes | Time to first response | | Waiting Since Date | Since when it has been awaiting a response | | Snoozed Until Date | Until when it is snoozed | | Contact/Agent/Assignee Last Seen At | Last views | | Last Activity At | Last activity in the conversation | | Proactively Initiated Flag | Whether it was proactively initiated (active outbound) | | Resolved Flag | Whether it was resolved | | Resolved At / Resolved At Date | When it was resolved | | Is Conversation Recontact Flag | Whether it is a recontact (customer returned within 24h) | | Minutes To Solve / Working Minutes To Solve | Time to resolution (elapsed and in business hours) | | Minutes Waiting / Working Minutes Waiting | Time awaiting a response (elapsed and in business hours) | | Minutes In Service / Working Minutes In Service | Time in service (elapsed and in business hours) | | Messages Sent/Received In Conversation | Messages sent by support and received from the customer | | Contact Custom Fields / Conversation Custom Fields | Contact and conversation custom fields | 8. cloud_chat_conversations_first_reply_metrics Detailed first-response metrics per conversation, including the first human response after AI handoff. | Column | Description | | --- | --- | | Conversation Id / Conversation Display Id | Conversation identifiers | | Customer Id / Instance Id / Account Id | Environment and account identifiers | | Inbox Id / Inbox Name | Inbox (channel) | | Assigned Team Id / Assigned Team Name | Assigned team | | Conversation Created At | Conversation creation | | Client First Message At | Customer's first message | | First Reply Created At | First reply to the customer (AI or human) | | First Responder Id/Name/Type | Who gave the first response and whether it was AI or human | | Time To First Response Seconds/Minutes | Time to first response | | Human Handoff At | When the conversation was transferred from AI to human | | Human First Reply Created At | First human response | | First Human Responder Id/Name | First human agent to respond | | Time To First Human Response After Handoff Seconds/Minutes | Time between the handoff and the first human response | 9. cloud_chat_csat CSAT responses for Cloud Chat conversations, already enriched with agent, team, inbox, and contact. | Column | Description | | --- | --- | | Conversation Id / Conversation Display Id | Conversation identifiers | | Customer Id / Instance Id / Account Id | Environment and account identifiers | | Conversation Created At | Conversation creation | | Response Submitted At | When the customer answered the survey | | Csat Value | Customer score | | Feedback Message | Customer comment | | Tags List | Conversation tags | | Assigned Agent Id / Assigned Agent Name | Assigned agent | | Inbox Id / Inbox Name | Inbox (channel) | | Team Id / Team Name | Assigned team | | Contact Id/Name/Phone Number | Contact who answered | 10. cloud_chat_conversations_tags Tags applied per conversation — each row is a tag applied to a conversation. | Column | Description | | --- | --- | | Conversation Id / Conversation Display Id | Conversation identifiers | | Customer Id / Instance Id / Account Id | Environment and account identifiers | | Conversation Created At | Conversation creation | | Tag Name | Name of the applied tag | | Tagged At | When the tag was applied | | Assignee Name / Inbox Name / Team Name | Agent, inbox, and team of the conversation | 11. cloud_chat_conversations_custom_fields Custom fields filled in per conversation — each row is a custom field of a conversation. It complements the Conversation Custom Fields column of the cloud_chat_conversations table (item 7), which brings the fields as a single blob: here each field becomes a row, ready to filter and pivot. | Column | Description | | --- | --- | | Conversation Id / Conversation Display Id | Conversation identifiers | | Customer Id / Instance Id / Account Id | Environment and account identifiers | | Conversation Created At | Conversation creation | | Conversation Custom Field Name | Name of the custom field | | Conversation Custom Field Value | Value filled in the field | | Custom Field Updated At | When the field value was last updated | | Custom Attribute Id | Identifier of the custom field definition in Cloud Chat | | Assignee Name / Inbox Name / Team Name | Agent, inbox, and team of the conversation | 12. cloud_chat_tags Catalog of tags registered in the account. | Column | Description | | --- | --- | | Tag Id | Tag identifier | | Customer Id / Instance Id / Account Id | Environment and account identifiers | | Tag Name / Tag Description | Tag name and description | | Created At / Updated At | Creation and update dates | 13. cloud_chat_contacts Contacts (end customers) registered in Cloud Chat. | Column | Description | | --- | --- | | Contact Id | Contact identifier | | Customer Id / Instance Id / Account Id | Environment and account identifiers | | Contact Name / Middle Name / Last Name | Contact name | | Contact Email / Contact Phone Number | Email and phone | | Identifier | External contact identifier (e.g., ID in your system) | | Contact Type | Contact type | | Location / Country Code | Location | | Additional Attributes / Custom Attributes | Additional and custom attributes | | Blocked Flag | Whether the contact is blocked | | Last Activity At | Last activity | | Created At / Updated At | Creation and update dates | 14. cloud_chat_outbound_events Active outbound events (campaigns and proactive sends) — each row is a send to a contact, with the full delivery trail. | Column | Description | | --- | --- | | Customer Id | Environment (tenant) identifier | | Conversation Id / Conversation Display Id | Conversation generated by the send | | Inbox Id / Inbox Name | Inbox (channel) of the send | | Campaign Id / Campaign Name | Campaign that originated the send | | Campaign Audience Name | Campaign audience | | Outbound Source Type | Send origin (campaign, API, single send) | | Created At | Event creation | | Message Sent Flag / Sent At | Whether the send was completed and when | | Message Received Flag / Received At | Delivery confirmation to the device and when | | Message Read Flag / Read At | Read receipt and when | | Message Replied Flag / Replied At | Whether the recipient replied and when | | Error Code / Error Category | Error code and category, when the send fails | 15. cloud_chat_outbound_events_funnel_chart Aggregated view of active outbound sends in funnel format (sent → received → read → replied), ready for charts. | Column | Description | | --- | --- | | Customer Id | Environment (tenant) identifier | | Event Created At | Event date | | Campaign Name / Campaign Audience Name | Campaign and audience | | Inbox Name | Inbox (channel) | | Stage Name | Funnel stage (sent, received, read, replied) | | Event Count | Number of events in the stage | 16. cloud_chat_agent_metrics Daily metrics consolidated per agent — one row per agent per day. | Column | Description | | --- | --- | | Agent Id / Agent Name | Agent | | Customer Id | Environment (tenant) identifier | | Metric Date | Reference date of the metrics | | Tickets Created Assigned | Tickets created assigned to the agent on the day | | Tickets Resolved Assigned | Tickets resolved by the agent on the day | | Sum (Working) Minutes To Solve | Sum of resolution times (elapsed and in business hours) | | Sum (Working) Minutes In Service | Sum of times in service (elapsed and in business hours) | | Sum (Working) Minutes Waiting | Sum of waiting times (elapsed and in business hours) | | Tickets Resolved With Recontact 24h | Resolved tickets in which the customer returned within 24h | | Csat Responses / Csat Positive Responses | CSAT responses received and positive | | First Reply Tickets Count / Sum First Reply Minutes | Tickets with the agent's first response and sum of times | | First Reply After Handoff Tickets Count / Sum First Reply After Handoff Minutes | Tickets with the first human response after AI handoff and sum of times | :::info To calculate averages (e.g., average resolution time), divide the sum column by the corresponding count — e.g., Sum Minutes To Solve ÷ Tickets Resolved Assigned. ::: 17. cloud_chat_team_metrics The table is identical to cloud_chat_agent_metrics, with the following difference: :::info The metrics are aggregated by team (Team Id / Team Name) instead of by agent — one row per team per day. ::: Cross-referencing ClaudIA and Cloud Chat The tables of the two topics connect through the conversation: the Cloudchat Id column (ClaudIA topic) corresponds to the Conversation Uuid column (Cloud Chat topic). This relationship is already configured — when building an analysis, just combine fields from both sides. Notes - For the column reference of the legacy builder: Explanation of the columns in the Legacy Report Builder - For the step-by-step of the Report Builder: How to use the Report Builder - To extract raw data via API: How to extract data in bulk via API (Data Extract API)

Last updated on Jul 22, 2026

How to analyze and export contact custom field data in the Report Builder

Overview The Report Builder V2 lets you analyze and export contact custom field data in CloudChat, cross-referencing that information with conversation data. Explainer video Step by step 1. Access the Report Builder V2 In the sidebar, go to the "Relatórios" (Reports) tab and click the last item — "Construtor de Relatórios V2" (Report Builder V2). 2. Create a new report On the home page, click "Novo" (New) in the top right corner. You will see the full catalog of tables available in CloudChat. 3. Select the correct table To analyze contact custom fields, select the CloudChat Conversations table — the first conversation option on the list. :::warning Do not select the second conversation option. It has no direct association with conversation data and is meant for a different type of analysis. ::: 4. Select the columns With the table open, select the columns you want. We recommend including: - Conversation ID — unique identifier of each conversation - Created At — date on which the conversation was started 5. Apply a time period filter To optimize performance and work with more recent data, apply a time period filter on the Created At column. We recommend using the last 90 days. 6. Add the contact custom fields column In the columns area, search for "contact" and select Contact Custom Fields. This field has a dictionary format: - The key is the identifier of the custom field - The value is the stored content A conversation can have multiple custom fields, resulting in several key-value pairs. Example: {"customAttribute1": "valor_x", "customAttribute2": "valor_y"} 7. Use the AI agent to extract the field you want Click the sparkles icon (✨) to open the AI agent. Copy the prompt below, replace the two highlighted fields and paste it into the agent: Generate a new column from the Contact Custom Fields field. The Contact Custom Fields field is a text (varchar) field that can store multiple custom fields of the contact associated with that conversation, in a format similar to a dictionary, with this structure: {"key1": "value1", "key2": "value2"}. Do a brief analysis of the possible values in this column to get more context. You must generate a new column called <<INSERT THE DESIRED COLUMN NAME HERE>> with the values of the field/key <<INSERT THE DESIRED KEY HERE>>. Additional context: #You are working on a Redshift database #You must not use the JSON_VALUE function or try to convert the field to the JSON type. The user has already made it clear that, although the column is stored in a format similar to a dictionary, it is of the text type, so you will not succeed with JSON functions #Sometimes the dictionary values are wrapped in double quotes "value" and other times in single quotes ''. Check whether this happens and, if confirmed, make sure it is handled Replace: - <<INSERT THE DESIRED COLUMN NAME HERE>> → the name you want to give the new column (e.g.: campo_customizado_1) - <<INSERT THE DESIRED KEY HERE>> → the exact key as it appears in the data (e.g.: customAttribute1) After adjusting the prompt, send it to the agent and wait for processing. 8. View and export the results Once the analysis is complete, you will have a table with the processed data. From there you can: Export the data: click the download icon to run external analyses. Create charts in the builder itself: 1. Remove the columns that will not be used in the visualization 2. Add an aggregation (e.g.: distinct count of conversations per field value) 3. Click "Gráfico" (Chart) and set up the axes: - X axis: custom field - Y axis: conversation count 9. Save as a dashboard In the top right corner, click + Dashboard to turn the analysis into a dashboard and save it in your company's folder on the "ClaudIA Panel/Centro" (ClaudIA Panel/Center) tab. That way anyone on your team can reuse the report at any time without having to recreate it from scratch.

Last updated on Aug 26, 2026

Reports - CSAT

When to use it - You want to track the satisfaction of the customers who talked to your operation. - You want to understand how many CSAT surveys were sent and how many got a response. - You want to compare satisfaction across different inboxes, teams, agents, or tags. - You want to spot days with a drop in satisfaction and investigate why. - You want to read the feedback each customer left in a conversation. Prerequisites - Be logged in as a supervisor, CX Engineer, or administrator. - Have at least one conversation with a CSAT survey sent within the selected period. Overview The "Relatórios - CSAT" (Reports - CSAT) screen brings together, in a single place, all the information about the satisfaction surveys your operation sends at the end of conversations — including how many were answered, what the CSAT value was, and the written feedback the customer left. The CSAT survey works in two moments: 1. Sending — at the end of a conversation, the survey is triggered to the contact. 2. Response — the customer picks a rating from 1 to 5 and, optionally, writes a comment. Based on the ratings received, each response is classified into three groups: | Group | Ratings | What it means | | --- | --- | --- | | Positive | 4 and 5 | The customer left satisfied with the service. | | Neutral | 3 | The customer left indifferent. | | Negative | 1 and 2 | The customer left dissatisfied with the service. | These three groups are the basis for calculating the metrics shown on the screen. What each block of the screen shows The screen is divided into four main areas: 1. Filters at the top — let you slice the data by date, inbox, tag, team, and grouping period. 2. KPIs — Total responses, CSAT, and Response Rate. 3. Charts over time — CSAT evolution (with the total of positive, neutral, and negative responses) and response rate evolution. 4. Detailed table — one row per CSAT response received, with contact, agent, team, inbox, rating, tags, and feedback. How the total cards are calculated The three cards at the top of the screen answer three different questions. Total de Respostas (Total Responses) How many CSAT surveys were answered in the period. Each response corresponds to a conversation in which the customer picked a rating from 1 to 5. CSAT The percentage of positive responses (ratings 4 and 5) out of the total responses received: CSAT (%) = Positive responses ÷ Total responses × 100 The higher it is, the greater the share of customers leaving satisfied. Rating 3 (neutral) and ratings 1–2 (negative) do not count as satisfaction in the CSAT calculation — they only pull the number down by increasing the denominator. Taxa de Resposta (Response Rate) The percentage of surveys that were answered out of the total surveys that were sent to the customer: Response Rate (%) = Answered surveys ÷ Sent surveys × 100 This number shows how engaged with the survey your base is. Difference between "survey sent" and "survey answered" - Sent: at the end of the conversation, the system triggered the survey to the contact. - Answered: the contact opened the survey, picked a rating, and submitted it. Every answered survey was, before that, a sent survey — but the opposite is not true. It is common for sent surveys to get no response. CSAT over time The "CSAT ao longo do tempo" (CSAT over time) chart shows, for each period (day, week, month), two pieces of information overlaid: - Stacked bars with the number of positive (green), neutral (yellow), and negative (red) responses. - Line with the period's CSAT (%), calculated as positive responses ÷ total responses × 100. How to read it - The total size of the bar shows how many responses the period received. - The proportion of the colors shows the distribution between positive, neutral, and negative. - The line shows the evolution of CSAT (%) over time — useful for spotting trends of improvement or decline. Response rate over time The "Taxa de resposta ao longo do tempo" (Response rate over time) chart shows, for each period: - Bars with the total responses received in the period. - Line with the response rate (%) — answered ÷ sent × 100. Detailed table The table at the bottom of the screen brings one row per CSAT response. Each column helps put the rating in context: | Column | What it shows | | --- | --- | | ID da Conversa (Conversation ID) | Unique identifier of the conversation that originated the survey. | | Contato (Contact) | Name of the customer who answered the survey. | | Agente atribuído (Assigned agent) | Agent who was assigned to the conversation when the CSAT was answered. | | Time (Team) | Team currently assigned to the conversation. | | Caixa (Inbox) | Inbox (channel) where the conversation took place. | | Classificação (Rating) | Rating given by the customer, from 1 to 5. | | Tags | Tags applied to the conversation — useful for understanding which topic the rating was about. | | Comentário (Comment) | Free text the customer wrote along with the rating. It may show up empty, since the comment is optional in the survey. | | Data da Resposta (Response date) | Day and time when the customer answered the survey. | | Data da criação do ticket (Ticket creation date) | Day and time when the conversation was created — the reference used by the screen's filters and charts. | The difference between Data da Resposta (Response date) and Data da criação do ticket (Ticket creation date) shows how long the customer took to answer the survey after the conversation started. It is common for responses to take a few hours (or even days) to arrive — that is why the CSAT of the most recent days may keep changing over the following days. Available filters The screen lets you cross responses by: - Data de Criação do Ticket (Ticket creation date) — the period in which the conversation was created (not the date the survey was answered). It accepts predefined ranges (last 7 days, 30 days, etc.) and custom ranges. - Caixa (Inbox) — inbox (channel) where the conversation took place. - Tag — tag applied to the conversation. - Time (Team) — team (currently) responsible for the conversation. - Agrupar por período (Group by period) — defines the granularity of the evolution charts (Day, Week, Month). Practical example Scenario You are looking at the screen with the "last 7 days" filter for a specific inbox. Result | Metric | Value | | --- | --- | | Responses | 7,195 | | CSAT | 71.73% | | Response Rate | 19.02% | How to interpret it - 7,195 responses for the conversations created in the last 7 days. - CSAT of 71.73% → about 7 out of every 10 customers who answered left satisfied. The other 3 were either neutral or dissatisfied. - Response rate of 19.02% → about 1 out of every 5 surveys sent was answered. The remaining 81% simply did not answer. To understand what is pulling CSAT down: 1. Look at the chart over time and identify the day(s) with the most red bars. 2. Add filters to assess whether the dissatisfaction is concentrated in a specific topic. 3. Open the conversations listed to read the written feedback and understand what happened. Frequently asked questions Why is my response rate so low? It is not unusual to have low response rates on CSAT surveys. Most customers simply do not answer — it does not mean the service was bad. If your rate is far below what you expected, it is worth reviewing: - The channel in which the survey is sent. - The message that goes with the survey. Can I trust the CSAT when the response rate is very low? With care. If only 5 customers answered in the period, any low rating weighs too much on the result. Use the number of responses as a reference: the higher the volume, the more reliable the period's CSAT is. Can the numbers on this screen change over time? Yes. A conversation closed today may have its survey answered tomorrow or later. Since the screen uses the ticket creation date as the reference, the metrics for the most recent days keep growing in the following days, as customers answer. Looking at the current day's CSAT right after conversations are closed usually underestimates both the volume of responses and the consolidated CSAT. Wait a few days before treating the numbers as final. Why do some rows in the table show up with the "no_tag" tag? It means the corresponding conversation did not receive any tag during or after the service. It is worth reviewing with the team whether tagging is being done consistently. Does the "Data de Criação do Ticket" (Ticket creation date) filter consider when the conversation was opened or when the survey was answered? It considers when the conversation was created. This means that, when you filter by the "last 7 days", you are looking at surveys from conversations started within those 7 days — some of them may have been answered days later. Do answered surveys without written feedback show up too? Yes. The written feedback is optional. Every rating given counts as a response, even without a comment. In the detailed table, the feedback column stays empty in these cases. Why is the total number of responses different from one chart to another? At times, the total responses may show up slightly different between the charts. This happens temporarily and tends to sort itself out over the course of the day. There are two main reasons: - Updates at different moments — the survey information and the conversation information are updated every hour, but not at exactly the same instant. If you open the screen right in the window where one has already been updated and the other has not, the numbers may not match 100%. - Cached view — to make the screen faster, part of the content is stored for a few minutes. It may happen that one chart shows the most recent version and another still shows the previous one. In both cases, the difference is usually small and resolves itself on the next update. :::info If you need a more stable view — for example, to generate a report, compare periods, or present the data — we recommend excluding the current day from the analysis. Today is the day most affected by these variations, because it is the period in which new conversations are coming in, new surveys are being answered, and the updates have not yet run from start to finish. When you look from yesterday backwards, the numbers tend to be more stable. ::: Notes - To understand other metrics of your operation, check the other articles in the Metrics and reports category.

Last updated on Aug 03, 2026

Reports - Campaign Metrics

When to use it - You want to track the performance of a campaign (bulk sends) in Cloud Chat - You want to understand how many messages were created, sent, delivered, read and replied to - You want to compare performance across different campaigns, audiences or inboxes - You want to identify why some of the sends never reached the recipient - You want to monitor proactive messages sent individually by automations or through API integrations - You want to understand at which times of day and days of the week messages are read the most Prerequisites - Being logged in as a supervisor, CX Engineer or administrator - Having at least one campaign sent or at least one proactive message sent Overview The Campaign screen brings together, in a single place, all the information about proactive messages that go from your operation to the end customer — that is, sends made by you without the customer having started the conversation. There are two types of sends considered: 1. Campaigns — bulk sends configured in Cloud Chat, usually segmented by audience (e.g., "customers with overdue payments", "new sign-ups of the week"). 2. Individual proactive messages — messages sent one at a time to a contact from previously configured automations, either manually through Cloud Chat or via API. On the screen, these sends appear grouped as "via API or individual messages". Each message sent goes through up to five states, in this order: | State | What it means | | --- | --- | | Created / Scheduled | The operation registered the send attempt | | Sent | The platform (e.g., WhatsApp) accepted and dispatched the message | | Received | The message was delivered to the recipient's device | | Read | The recipient opened the conversation and viewed the message | | Replied | The recipient replied to the message | These states form the main send funnel on the screen. What each block of the screen shows The screen is divided into four main areas: 1. Filters at the top — let you slice the data by date, campaign, audience, inbox and grouping period. 2. Total cards — Created Events, Send Errors, Sent, Delivered, Read and Replied. 3. Send Funnel — shows the drop-off between each stage, with the percentage relative to the total created. 4. Complementary visualizations — sends with errors by error type, a heatmap of reading time by day of the week, and the evolution of sends over time. How the total cards relate to each other The cards at the top of the screen follow the funnel arithmetic — each stage is the result of the previous one minus what stopped along the way: - Scheduled − Errors = Sent - Sent − Not delivered = Delivered - Delivered − Not read = Read - Read − Not replied = Replied :::warning The rule Scheduled − Errors = Sent does not apply to campaigns/sends via API or individual messages. ::: The funnel is cumulative When you see 1,000 in Received, that does not mean "1,000 messages stopped at Received". It means "1,000 messages reached at least the Received state" — including those that were also read and replied to afterwards. That is why the number at each stage is always less than or equal to the previous stage: :::info Created ≥ Sent ≥ Received ≥ Read ≥ Replied ::: How to calculate the conversion rates The natural way to read the funnel is to always compare against the total of created/scheduled events: - Send rate = Sent ÷ Created - Delivery rate = Received ÷ Created - Read rate = Read ÷ Created - Reply rate = Replied ÷ Created In the funnel on the screen itself, the percentage displayed next to each stage already treats the total of Created as 100%. Difference between "Created (or Scheduled)" and "Sent" - Created: the operation attempted to send the message. There is a record of the send on your side. - Sent: the messaging platform (e.g., WhatsApp) accepted the send and dispatched it to the recipient. The difference between the two usually represents failures before sending: invalid number, policy block, send limit reached, ineligible contact, etc. To understand the reason, see the Send errors section below. On the screen, this difference is highlighted by the "Send Errors" card, which shows how many created messages never made it to being sent. Send errors (why my message did not go out) When a message is created but not sent, you can identify the reason from an error code. The screen translates the most frequent codes: | Code | Meaning in plain language | | --- | --- | | 131026 | Message not delivered — generic reason. It may be an inactive number, a number without WhatsApp, or one that blocked the sender. | | 131049 | Policy violation — the message was blocked by WhatsApp for not following the platform's guidelines. | | 130472 | The contact is part of a WhatsApp experiment and is not eligible to receive this send. | | 131042 | Restriction on the sender's account — usually a payment issue or a problem enabling the business account. | | 130429 | Send limit reached — too many messages were sent in a short period of time. | | 131053 | Failure uploading the media (image, video, audio or document) attached to the message. | | Others | Other errors that do not fit the codes above. | Use this breakdown to understand whether the problem is: - In your contact base (wrong numbers, blocks) → 131026 - In the message content → 131049 - In your business account → 131042 - In the send volume → 130429 - In the attached media → 131053 The "Sends with errors by error type" chart shows, day by day, the share of each code in the total of errors — useful for spotting whether a specific type of failure is growing over time. Heatmap of reading time by day of the week The heatmap crosses the day of the week with the hour of the day and colors the cells according to the volume of reads observed in that interval. How to read it - The darker the cell, the higher the volume of reads at that time and day of the week. - Light or blank cells indicate times with few or no reads. What it is for - Identifying the window in which your base consumes messages the most (e.g., Tuesday around midday). - Adjusting the timing of your sends to match the peaks of attention. - Spotting days or times when it is worth avoiding sends (due to low open rates). :::info The heatmap considers the time at which the message was read — not the time at which it was sent. ::: Evolution of Sends Over Time This chart shows, for each day in the selected range, the number of events at each stage of the funnel: - Created events - Sent events - Received events - Read events - Replied events What it is for - Tracking the consistency of your sends over time. - Identifying atypical days — sharp drops (technical issue, holiday, campaign pause) or spikes (extra campaigns). - Checking whether the read and reply rates follow the creation volume, or whether the curves are drifting apart. Because the funnel is cumulative, the lines usually appear in order: created at the top, sent just below, and so on down to replied. "via API or individual messages" When you open the screen's filters, it is normal to see a line called "via API or individual messages" in place of a campaign name. This means that this group of sends was not made by a campaign configured in Cloud Chat. These are proactive messages sent individually — through sends from automations or via API integration. To isolate campaigns only, filter by the campaign column and remove that group. To look only at sends outside campaigns, keep only that group. Available filters The screen lets you cross-reference sends by: - Send scheduling date — the day on which the message was created. - Campaign — the name of the campaign configured in Cloud Chat (or "via API or individual messages" when the proactive send does not come from a campaign). - Audience — the contact segment tied to the campaign. As a rule, messages generated via API have no audience configured — which is why "via API or individual messages" appears for them. - Inbox — the inbox the conversation was directed to. - Group by period — defines the time granularity used in the charts (day, week, etc.). The filters can be combined: you can see, for example, "audience X of campaign Y in Inbox Z in a given week". Practical example Scenario On Monday, you send the "Black Friday Promotion" campaign to an audience of 10,000 contacts. Funnel result at the end of the day | State | Amount | Rate relative to the total created | | --- | --- | --- | | Created | 10,000 | 100% | | Sent | 9,700 | 97% (send rate) | | Received | 9,200 | 92% (delivery rate) | | Read | 6,500 | 65% (read rate) | | Replied | 1,300 | 13% (reply rate) | How to interpret it - 300 created messages that were not sent → go into the error breakdown to understand the reason for the send failures. - 500 sent that were not received → contacts with the device turned off, without internet, or with outdated numbers. - 2,700 delivered that were not read → an opportunity to review the send timing and the subject line / first message. The heatmap can help identify a better time. - 5,200 read that did not reply → an opportunity to review the message's call to action (CTA). Frequently asked questions Why is the "Created" number higher than the "Sent" one? Because some of the messages were registered for sending, but the messaging platform refused the send. The cause appears in the error breakdown — usually an invalid number, a policy violation, a send limit, or a problem with the business account. Can the numbers on this screen change over time? Yes. A message sent today may be read tomorrow and replied to the day after. Since the funnel date is the message's creation date, the "Read" and "Replied" metrics for the send day keep growing in the following days, as the recipient interacts. :::warning Looking at a campaign right after the send underestimates the read and reply rates. Wait a few days before considering the numbers final. ::: Why can "Replied" show up as zero even with many reads? Some campaigns are informational (notices, reminders, confirmations) and the recipient has no reason to reply. In those cases, a low reply rate is expected and does not indicate a problem. For campaigns that ask for action ("Confirm your order", "Click to complete"), a low reply rate is a sign that the message content needs to be reviewed. Do sends via API or individual message automations appear alongside the rest of the data on the screen? Yes, but in a separate category called "via API or individual messages". You can filter it in or out of the analysis as needed. Why does the same message appear in more than one stage of the funnel? Because the funnel is cumulative. A message that was sent, received and read appears in all three stages — it is the same event moving through the funnel, not three different messages. Do test messages count in the metrics? Yes. All sends made through the platform — including internal tests — are counted. Can I see a campaign's performance by day? Yes. The screen lets you filter by the desired date range, and the Evolution of Sends Over Time chart shows the daily breakdown. Since each send is tied to its creation date, you can track the daily evolution of a campaign that stayed active for several days. How do I use the heatmap to improve my campaigns? The heatmap shows at which times and on which days your base reads messages the most. Use this information to schedule your next sends during reading peaks and avoid cold windows. Keep in mind that the heatmap reflects historical behavior — peaks may vary depending on the audience profile. Is the percentage in the funnel relative to the previous stage or to the total created? Relative to the total of Created (which appears as 100% in the first step). That is why the "Sent" percentage represents the send rate, the "Received" one the delivery rate, and so on — always compared to the initial volume. Notes - Related category: 📊 Metrics and reports. - Detailed WhatsApp error codes are published by Meta: Meta Developers — Cloud API Error Codes.

Last updated on Aug 03, 2026

How to access your operation’s raw data (Data Export)

When to use - You want to bring your Cloud Chat and ClaudIA operation data into your company's data lake or BI tool. - Your data team needs the raw data, not just the platform's ready-made reports. - You want to cross your support data with other internal sources in your operation. Prerequisites - A technical team — preferably a data team — to consume the files and bring them into your environment. - The service must be enabled by Cloud Humans. It is off by default and cannot be turned on by yourself from the platform. :::info If your operation does not have a dedicated data team, Cloud Chat's dashboards and Report Builder usually serve you better: they are visual, ready to use, and don't depend on a technical team. ::: How it works Where the data lives We provide an S3 bucket dedicated to your company on AWS. In practice, it is a folder in the cloud — like a Google Drive folder, only Amazon's. Every day the files with your operation's data are uploaded to that folder. Daily load (D-1) The load runs overnight and covers data up to the previous day. In other words: today you access data from yesterday back; tomorrow, data from today back. Read-only access Access is read-only. Nobody on your team can write, overwrite, or delete files in the bucket. You keep control of access We do not issue our own credentials to your team. Your data team provides the identifier of a role in your own AWS account, and we authorize that access on the bucket. The credential never leaves your environment. How to request it 1. Talk to your point of contact at Cloud Humans (CSM) or to our support team and ask for Data Export access. 2. Our team confirms whether the service makes sense for your operation and provisions the bucket. 3. Your data team sends the role identifier from your AWS account, and we grant access. 4. From the next day on, data starts being delivered to your folder. Notes - Technical documentation for your data team: docs.cloudhumans.com — service overview, how files are organized in the bucket, table and column catalog, best practices, and troubleshooting. - Prefer something with no technical dependency? See Como usar o Construtor de Relatórios — guia completo and the visão geral dos relatórios padrões do Cloud Chat.

Last updated on Aug 19, 2026

How to find the most used tags in a period

When to use this - You want to know which tags are applied the most in your conversations — for example, the top 5 in the last 60 days - You opened the Tags report, saw that it shows one tag at a time, and weren't sure how to compare them all About this article Cloud Chat does not have a ready-made tag ranking screen. But there are two paths that deliver exactly that number — one in just a few clicks, the other with more flexibility. Path 1 — Export the Tags report (fastest) The "Relatórios > Tags" (Reports > Tags) screen shows one tag at a time: you pick the tag in the selector and see how it evolved over the period. That doesn't help you compare. The ranking is in the export, which brings all tags at once: 1. Go to "Relatórios > Tags" (Reports > Tags). 2. In the period filter, choose "Personalizado" (Custom) and enter the dates you want (e.g. the last 60 days). 3. Click "Baixar relatórios de marcadores" (Download tag reports). 4. Open the file and sort by the conversations column, from highest to lowest. The file has one row per tag, with: | Column | What it is | | --- | --- | | Tag | Tag name | | Conversations | Number of conversations with that tag in the period | | Average first response time | Average for the period | | Average resolution time | Average for the period | :::info The first five rows after sorting by the conversations column are the five most used tags in the period. ::: Path 2 — Report Builder (more flexible) Use this when you want to cross tags with inbox, team or agent, or keep the report saved so you can check it any time. 1. Go to the "Construtor de Relatórios" (Report Builder). 2. Select the cloud_chat_conversations_tags table (one row per tag applied to a conversation). 3. As the dimension, choose Tag Name. 4. As the measure, choose the conversation count. 5. Filter by date using Tagged At (when the tag was applied). 6. Sort the count in descending order and limit it to the first rows. To break it down by inbox, team or agent, add the matching columns as a filter. :::warning A conversation with several tags appears once for each tag. That is expected when you are counting tag usage, but it means the sum of the counts is higher than the total number of conversations in the period. Do not use that sum as your total conversation volume. :::

Last updated on Aug 31, 2026

Real-Time Operation Management with Advanced Monitoring

The Advanced Monitoring screen provides a detailed and centralized view of the current status of conversations in each Inbox on the platform. This panel allows identifying bottlenecks in support and monitoring the distribution and waiting times of conversations in real time. Accessing Advanced Monitoring To access, click on "Reports" and then on "Advanced Monitoring" in the platform's side menu. The interface displays filters at the top and two main sections: 1. Metrics by Inbox 2. Conversation Traffic (heatmap by hour) Metrics by Inbox The "Metrics by Inbox" section presents a table with consolidated support data per inbox. Columns displayed: - Inbox: Name of the inbox configured in the platform. - Open Conversations: Total conversations with status "Open". - Unattended: Number of open conversations where the last message was sent by the customer. - Unassigned: Open conversations that do not have an assigned agent. - Longest Waiting Time (minutes): Indicates the unassigned conversation waiting the longest for support (i.e., the conversation in queue the longest). - Average Waiting Time (minutes): Calculates the average waiting time of open and unassigned conversations. These metrics facilitate proactive monitoring, helping to identify inboxes with overloads or waiting times above the ideal. Conversation Traffic Below the metrics table, there is a conversation traffic heatmap, which visually displays the volume of messages received over the past 7 days. - Each cell represents a one-hour interval on a specific day. - The color indicates the volume of conversations initiated or received during that period (based on your own history). - Hovering over a cell reveals the exact number of conversations received during that hour of that day. This visualization helps identify peak hours and periods of higher activity on the platform. It is useful for adjusting team scale and capacity according to demand and improving customer response times. Available Filters The view can be segmented using the filters located at the top of the screen: - Agents: Displays metrics considering only the selected agents' inboxes. - Team: Allows filtering by specific teams. - Inbox: Filters the displayed metrics for one or more specific inboxes (mainly applicable to conversation traffic). These filters enable targeted analysis, assisting leaders and managers in making quick, data-driven decisions.

Last updated on Sep 02, 2026

Overview of the Cloud Chat standard reports

When to use - You want to understand all the standard reports available - You need to decide which report to use for a specific case (volume, CSAT, team, agent, campaigns) - You want an overview before diving into a specific report Prerequisites - Be logged in as a supervisor, CX Engineer or administrator About this article In this article you will understand what the standard reports available in Cloud Chat do — standard for every customer. :::info For custom reports, see: Basic guide to Report Builder v2. ::: Instructional video: https://www.loom.com/share/76332999ffab41df99c65352792cc799 Cloud Chat offers four main standard reports: "Conversas" (Conversations), CSAT, "Time" (Team) and "Agentes" (Agents), plus complementary reports ("Campanhas" (Campaigns) and "Tempo de Serviço" (Service Time)). Conversations report General metrics of the operation with several filters. Metrics displayed - Conversations — number of conversations created - Messages received — messages the customer sent. A ticket can have more than one - Messages sent — messages sent by the agent or the AI. A ticket can have more than one - First response time (in seconds) — difference between the ticket creation and the sending of the first message - Resolution time (in minutes) — difference between creation and being marked as resolved. In case of reopening, it counts the last resolution - Recontact — tickets in which the same customer opened a new conversation in less than 24h after the previous one was closed - Resolution count — number of tickets resolved. There is a small difference from the conversations chart because the latter counts the creation date Available filters Multi-select: - Agent — agent assigned to the ticket - Inboxes — inbox of the tickets - Tag — contact reason - Teams — team responsible - CSAT — satisfaction score received - Date — predefined (today, yesterday, last 7/30 days, last 3/6/12 months) or custom ranges And grouping by hour, day, week, month, quarter or year. :::info Specifically for the Conversations dashboard, you need to click "Aplicar Filtros" (Apply Filters) after selecting the filters. To refresh with the most recent data, click "Atualizar o dashboard" (Refresh the dashboard). ::: Export Share on the web, download as PDF or image, or send it to a specific email address. CSAT report Visibility into the operation's CSAT. Main metrics 1. Number of CSAT responses in the period 2. Average CSAT score per period (scores 4 and 5 are positive) 3. Response rate — percentage of customers who responded Breakdown by score The second part of the report shows the total number of responses per score. Detailed table Lists each CSAT of the period, with: - Contact name - Agent assigned at the time of the survey - Rating (1 to 5) - Comments (if any) - Date the CSAT was recorded - Team assigned at the time - Inbox - Ticket tags :::info You can sort the table in ascending or descending order by clicking on the title of any column. ::: Filters and human vs AI split Multi-select: agent, inbox, tag, team, rating. Date filter identical to Conversations. :::info Unlike the other reports, here you can filter by human responses only — turn off "Incluir agentes de IA" (Include AI agents). ::: Team report Focused on granular data by team and tags in table format. Available tables - Ticket volume by team (creation date) - Ticket volume by tag (creation date) - Reopened tickets by team (recontact in less than 24h) - Average first response time by team - Average resolution time by team - Average CSAT score by team - CSAT score by tag Views | View | Shows | | --- | --- | | Month | Current month + last 4 months | | Week | Current week + last 5 weeks | | Day | Current day + last 28 days | Agents report Format similar to the Team one, with a focus on the agent. Metrics - Volume of tickets created per agent - Volume of reopenings per agent (recontact in <24h) - Average first response time per agent - Average resolution time per agent - Average CSAT score per agent Same views (month, week, day) as the team report. Campaigns report Consolidates data from Campaigns and sends via API/manual proactive. Available filters: - Campaigns (campaign_id) - Inboxes (inbox_id) - Audiences Funnel view At the top, general data with a breakdown of errors and responses: - Agendado - Erros = Enviados - Enviados - Não entregues = Entregues - Entregues - Não lidos = Lidos - Lidos - Não respondidos = Respondidos :::info The Agendados − Erros = Enviados (Scheduled − Errors = Sent) rule does not apply to campaigns/sends via API or individual Messages. ::: Just below, the behavior of the sends over time. X axis = creation date of the send. Service Time report The Service Time report provides detailed metrics on TME, TMA, TMR and other service times, allowing you to monitor and improve the efficiency of the operation. Details in: How to understand the service time metrics. Notes - To create fully custom reports: Basic guide to Report Builder v2 - For real-time (not historical) management: How to manage the operation in real time with the "Gestão à Vista" (Live Report) report and Advanced Monitoring - For extraction via API with more fields: How to extract data in bulk via API (Data Extract API)

Last updated on Sep 16, 2026

Basic Guide to the Report Builder

Here you will learn the basics of working with the report builder. If you want to know about the available tables in the report builder, visit this FAQ. Data Selection Start by choosing the data you want to include in your report by selecting a dataset. You can preview the columns before making your selection. The data selection will apply to all visualizations in the report — so if you want to use data from another source, it might be best to create a new report with that dataset. Table Setup By default, all columns will be displayed in the table. You can reorder columns directly in the Report Data section by dragging fields. It is also possible to hide or show columns using the eye icon. To create aggregations, simply drag specific columns into the Values section and choose the desired aggregation type from the menu. The “+” icon next to the section name allows you to add extra columns. You can also create custom aggregations via the Add path within Values. You can group data using Group By and Breakdown sections. Columns in Group By define table rows, while columns in Breakdown define the columns. You can enable or disable totals and subtotals on the rows. For most fields, it is possible to change the aggregation type by clicking the column header — except for fields in the Values section in the grouped format. These aggregations can be adjusted in the data panel. Building Reports with AI Kick off your visualizations with the AI Center! You can generate tables, charts, and other visualizations based on text commands, without manually configuring everything in the interface. You can write a generic command like: "Transactions over time" Or a more specific one: "Transactions broken down by category in the last year" 💡 Tip: Use keywords like “bar chart”, “line chart”, “group by”, or column names from your dataset to make the AI more precise. You can fine-tune the details before creating the new visualization in your report. How to Export Your Report You can download your report as CSV immediately or schedule recurring email deliveries with your preferred frequency. Scheduled exports can be sent to multiple emails with daily, weekly, or monthly frequency. To set this up, click Export → Schedule Export and configure the details in the Export Schedule Manager (Export → Schedule Export). Visualizations You can choose from 5 types of visualizations. Each visualization can contain only one chart or table at a time. The available types are: - Table – Shows all data or grouped in tabular format - Line Chart – Ideal for displaying data over time - Bar Chart – Good for visualizing data by category - Pie Chart – Shows how values are divided within a whole - KPI – Displays a single key performance indicator After selecting the visualization type, the chart will be generated automatically once all fields are correctly filled. The interface indicates which ones need adjustments. If you try changing the visualization type within the same tab, the system will attempt to convert the current visualization to the new one. Some charts offer additional formatting options, such as bar direction or grouping. Views Each report can have multiple views, but each view contains only one table or chart. All views share the same data selection (see Data Selection section). When exporting or sharing, you can include all views in a single report. Filters Filters allow you to view specific parts of the data. Each visualization can have its own filters. They are based on the columns included in your report (according to the data selection) and can include: - Category Filters - Numeric Filters - Date Filters

Last updated on Sep 02, 2026

Explanation of Report Builder Columns

This FAQ provides a quick and practical understanding of the available columns in the Report Builder, ideal for clear and effective reports. 1. account_user_availability_histories - Event Id: Unique identifier of the event. - Agent Id: Unique identifier of the agent. - Agent: Name of the agent. - Availability: Agent status (online, offline, busy). - Availability Reason Id: Reason for the status change. - Created At: Date and time of the record. 2. agents - User Id: Unique identifier of the agent. - Email: Agent's email. - Name: Full name of the agent. - Assignment Limit: Maximum number of tickets allowed simultaneously. 3. conversation_events - Id: Unique identifier of the event. - Name: Type of event. - Value: Additional details of the event. - Account Id: Account identifier. - Inbox Id: Associated inbox. - User Id: User related to the event. - Conversation Id: Associated conversation. - Created At: Date and time of creation. - Updated At: Last update. - Instance: Specific instance. 4. conversations - Created At: Conversation creation date. - Id: Internal identifier. - Display Id: Identifier shown to the user. - Assignee Id: Responsible agent. - Inbox Id: Associated inbox. - Agent Type: Type of agent (human/AI). - Repeats In Last 24h: Repeated conversations in 24 hours. - Conversation Custom Field Name: Refers to user-defined custom fields. - Conversation Custom Field Value: Contains the value filled in custom fields defined by the user. Note: For more visual analyses and reports, it is recommended to use both columns (Conversation Custom Field Name and Conversation Custom Field Value). 5. csat - Assignee Name/Email/Id: Assigned agent's information. - Contact Name/Email/Number/Id: Customer's information. - Csat Id: Survey identifier. - Inbox Id: Associated inbox. - Rating: Customer's rating (1 to 5). - Rating Emoji: Associated emoji. - Feedback Message: Additional customer comments. - Created At: Response date. - Display Id: Visible ticket identifier. - Ticket Link: Direct link to the ticket. - Agent Type: AI or human agent. 6. inboxes - Id: Unique identifier. - Name: Inbox name. 7. messages - Id: Message identifier. - Instance/Account Id/Inbox Id: Related identifiers. - Conversation Id: Associated conversation. - Message Type: Type of message. - Created At/Updated At: Creation and update dates. - Private: Message privacy. - Status: Message status. - Source Id: Origin of the message. - Content Type: Type of content. - Content: Message content. - Content Attributes: Additional attributes. - Sender Type/Id: Sender of the message. - Processed Message Content: Processed content. - Conversation display id: ID that appears in the conversation URL. Example: https://cloudchat.cloudhumans.com/app/accounts/1/conversations/{{conversation_display_id} 8. Tags - Id: Tag identifier. - Title: Tag name. - Description: Tag description. - Color: Associated color for quick identification. 9. teams - Id: Team identifier. - Name: Team name. - Description: Role or composition of the team. - Allow Auto Assign: Automatic assignment enabled. - Account Id: Associated account. - Created At/Updated At: Relevant dates. - Working Hours Enabled: Set working hours. - Out-of-office message: Automatic message outside working hours. - Timezone: Team's timezone. - Instance: Specific instance. 10. Campaign Delivery events - Campaign Delivery Events ID — Unique identifier of the campaign delivery event record. - Campaign ID — Identifier of the campaign that triggered the message. - Contact ID — Identifier of the contact/recipient who received the message. - Conversation ID — Identifier of the helpdesk/omnichannel conversation where the event occurred. - Message Sent — Indicates whether the message was successfully sent by the system. - Sent At — Date and time when the message was registered as sent. - Message Received — Indicates whether delivery confirmation to the recipient's device was received. - Received At — Date and time when delivery confirmation was recorded. - Message Read — Indicates whether a read receipt was received. - Read At — Date and time when the message was read. - Message Replied — Indicates whether the recipient replied after sending. - Replied At — Date and time when the recipient's reply was recorded. - Error — Error message when there is a failure in sending/delivery/reading. - Created At — Record creation date and time. - Updated At — Last update date and time. - Instance — Identifier of the client's instance/tenant where the event was recorded. - Campaign display id: ID that appears in the campaign URL. It is a unique ID within the Account. - Conversation display id: ID that appears in the conversation URL. It is a unique ID within the Account. Example: https://cloudchat.cloudhumans.com/app/accounts/1/conversations/{{conversation_display_id} - Inbox ID - Inbox (channel) ID. - Title - Campaign name. - Audience - Campaign audience. 11. Proactive Delivery events: The Proactive Delivery Events table is similar to Campaign Delivery Events. The only difference is that Proactive Delivery Events do not have Campaign ID, Title, or Audience. Since they are sent via API or single-shot via cloud chat, these fields do not exist.

Last updated on Sep 02, 2026

Real-Time Operation Management with the Management at a Glance Report

The Management at a Glance report is a centralized dashboard that displays the current ticket traffic on the platform. The dynamic panel shows in real-time how many tickets are open, which agents are online, and how many tickets are assigned to each agent. The goal of this screen is to make managing operations easier and simpler, including allowing immediate action where necessary. How to access the Management at a Glance screen To access the overview of reports, just click the reports button in the side tab and then select Management at a Glance. Operation Overview (Conversations and Agents) These are the first two rectangles on the screen, containing general information about tickets and agents. Open Conversations This panel shows four pieces of information about conversations: 1. Open: The number of open conversations, which are the total conversations with status open. 2. Unattended: The number of unattended conversations, meaning of open conversations, how many have the last message from the end customer. 3. Unassigned: The number of messages assigned, meaning, of the number of open conversations, how many are without an agent assigned. 4. Pending: Number of conversations moved to the Pending status by an agent. This status indicates that the support is waiting for a future action, internal validation, or completion of a step. The conversation remains pending until an agent changes the status back to Open. 5. Deferred: Number of conversations moved to the Deferred status by an agent. Works similarly to Pending, with the difference that if the customer sends a new message while the conversation is Deferred, the ticket automatically returns to the Open status. Agent Status This panel shows three pieces of information about agents: 1. Available: How many agents are available 2. Occupied: How many agents are marked as busy, including custom busy statuses 3. Disconnected: How many agents are disconnected or offline Detailed View of the Team, Agent by Agent In the Conversations by Agents table, the following information is displayed for each team member, enabling management of each individual. The goal here is to provide granular and quick info so team leadership can act swiftly. Available information: - Agent Name and Email - Agent Status: shows the current status as Online, Offline, or Busy, as well as custom statuses if created. - Duration in Current Status: how long the agent has been in their current status (time counter). Seconds are not displayed. - Open: total number of conversations assigned and open with the agent at that moment. - Unattended: number of open conversations where the last message was from the end customer (probably waiting for a response from the agent) - Assignment Limit: the number of conversations defined for the individual agent. If a global limit is set for the account, it will not appear here. - Inbox Assignments: in which inboxes the agent is assigned. - Actions: available actions or quick info for easy access directly from the Management at a Glance panel. We will delve into some of these in the sections below. Details and Information on Displaying Agent Status Each agent has a visible status in the table: - Online: The agent is active on the platform; - Offline: The agent is disconnected; - Busy: The agent has activated a busy status, such as In a meeting or Having lunch. Administrators can configure custom busy statuses (like Having lunch or External meeting), which will be displayed directly in the table. For more info on custom statuses, visit this FAQ. The table also displays a time counter indicating how long the agent has been in their current status, allowing monitoring of availability changes throughout the day. ⚠️ For agents with auto-offline enabled Some agents use the Automatically go offline feature. For more info on this, visit this FAQ. Listing Inboxes per Agent: In the Agents Table, it also shows the inboxes each agent is linked to. - Up to 3 inboxes are shown directly in the row, with their names visible; - When an agent participates in more than 3 inboxes, a "See More" button appears, which can be clicked to view the full list. Clicking "See More" opens a modal containing: - The full list of inboxes associated with the agent; - Details such as inbox name and identifying icons; ACTION: Viewing All Assigned and Open Conversations of an Agent Clicking the View Assigned Conversations button in the Action column opens a modal with the agent’s open conversations. Upon opening, you can see: - All open conversations assigned to the agent - Details of the assigned conversations, such as Conversation ID, Inbox, and Contact Name - A "View Conversation" button, which opens a new tab and takes you directly to the conversation on the platform. ACTION: Editing Agent Information One common need for team leaders is quickly updating agent details. To access this editing, just click the pencil-shaped button (✏️), located below the actions column. Clicking the edit button allows you to modify that specific agent’s information. The editable points are: - Agent Name - Agent Type: change access type to admin, supervisor, agent, or assistant - Availability: set agent as busy, available, or disconnected - Automatic assignment limit: set max number of concurrent conversations; leave blank to use global account limit. - Inbox Assignments: inboxes the agent is assigned to and can receive tickets from. After making your changes, just click "Edit Agent" to save. To cancel, click the "Cancel" button. How to Filter Data in the Management at a Glance Report The Management at a Glance report can also be filtered using filters by team, inbox, agents, or status, allowing you to view only data related to your selected filter. For example, to monitor the "sales" team, select the team name in the filter, and the panel will update to show only tickets related to that team. This makes it easier to track and manage workflow across large teams. Note that the filter also applies to status data and conversations by agents. ⚠️ IMPORTANT: When you first open the report, the default view is filtered to show only ONLINE agents. This is the default setting for agent management.

Last updated on Sep 02, 2026