Reports

Reports return numbers from a fixed catalog of metrics, such as conversations, AI-resolved conversations, task closes, and retention offers. You pick up to five metrics and a time range, and optionally a breakdown by week, phone line, team member, and more. POST /reports only reads data. It uses POST because the request is a JSON body.

Find the metric for your question

Question	Metrics	Useful `group_by`
How fast does someone act on a new request? (speed to lead)	`median_hours_to_first_action`, `avg_hours_to_first_action`	`team_member`, `category`
How long does it take to close a request?	`median_hours_to_close`, `avg_hours_to_close` (`team_member` or `category`), `same_day_close_rate` (`team_member` only)	`team_member`, `category`
How much does the AI handle without a person?	`ai_resolved_rate`, `ai_resolved_conversations`, `caller_resolved_rate`	`week`, `phone_line` for the counts
What is waiting on someone right now?	`open_tasks`	`task_age`, `team_member`
Who on the team does the most work?	`task_closes`, `tasks_worked`	`team_member`
How often does a customer ask for a person, and how often does the AI resolve it anyway?	`asked_for_person_conversations`, `asked_for_person_ai_resolved_rate`	`phone_line` for the count
How do retention offers do?	`retention_offers_pitched`, `retention_offers_accepted`, `retention_acceptance_rate`, `memberships_saved`	None. Run one report per period or per `filters.phone_line_id`.
How does each location compare?	Most conversation counts	`phone_line`

Read these definitions before you put a number in front of anyone. AI-resolved, for example, does not mean the customer’s problem was solved.

HTTP 200 does not mean the report ran. A combination of metrics, grouping, filters, and range that the catalog does not support returns HTTP 200 with "status": "unsupported_combination" and no numbers. Always check status before you read values. See Unsupported requests.

Run a report

POST /reports

Try it in the playground.

curl https://answeringagent.com/api/v1/reports \
  -H "X-API-KEY: $ANSWERING_AGENT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "measures": ["conversations"], "range": "30d", "group_by": "phone_line" }'

const res = await fetch('https://answeringagent.com/api/v1/reports', {
  method: 'POST',
  headers: {
    'X-API-KEY': process.env.ANSWERING_AGENT_API_KEY,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({ measures: ['conversations'], range: '30d', group_by: 'phone_line' }),
});
if (!res.ok) throw new Error(`${res.status}: ${await res.text()}`);
const report = await res.json();
// HTTP 200 does not mean the report ran. Check status first.
if (report.status !== 'ok') throw new Error(`${report.status}: ${report.message}`);

Request body

string[]

required

One to five metric keys from the catalog. Each key once.

string

required

The time window, in your team’s timezone:

today, yesterday, 7d, 14d, 30d, 90d, 365d, mtd, or ytd.
month, with month set to a calendar month.
custom, with from and to set. Up to 366 days.

today counts. 30d starts 30 days ago at the current time of day in your team’s timezone, and ends now. On October 6, the response’s range shows from 2026-09-06 and to 2026-10-06: 31 dates, the first and last of them partial. The other day ranges work the same way. For whole days, use custom.

string

YYYY-MM. Required when range is month.

string

YYYY-MM-DD, the first day. Required when range is custom.

string

YYYY-MM-DD, the last day, included. Required when range is custom.

string

Break the numbers into rows. One of day, week, month, hour_of_day, category, channel, phone_line, team_member, task_age, action_group, failure_type, failure_subcategory, or cancellation_reason. Each metric supports only some of them. See the catalog.

hour_of_day returns 24 rows, labeled 00:00 to 23:00, by the hour each conversation started in your team’s timezone. It works with include_percent_of_total but not with top_n.
task_age works only with open_tasks. It returns four rows by the time since the conversation or form came in: Under 1 day, 1 to 3 days, 3 to 7 days, and 7 days or more.

string

team_member. Splits one team-work metric (tasks_worked or task_closes) by team member over time. Requires group_by of day or week.

object

Narrow the data.

Show filters

string

phone, text, web_chat, or email, the same values as conversation search.

integer

One phone line. Get IDs from GET /account.

string

A category key: damage_claim, cancellation, lost_items, sales, scheduling, hours_status, service_status, billing, account_update, general_questions, job_seeking, no_answer, not_stated, spam, or other. Task and team-work metrics also accept form. GET /reports/metrics lists each key’s label under categories.

string

A two-letter language code, such as es.

integer[]

One to five team member IDs from members[].id in GET /account, each once. Team-work metrics only.

integer

1 to 10. Keep only the largest rows of a category-style breakdown, such as category, team_member, or cancellation_reason. For category, the rest go into one All other categories row. Any other grouping returns unsupported_combination. Without top_n, team_member_activity and account_updates return their top 5 rows.

boolean

Add percent_of_total to each row of a category-style or channel breakdown. One grouped count metric at a time.

Response

{
  "status": "ok",
  "range": { "requested": "30d", "from": "2026-09-06", "to": "2026-10-06", "timezone": "America/Chicago" },
  "values": {
    "conversations": { "label": "Conversations", "unit": "count", "value": 1264 }
  },
  "rows": [
    {
      "label": "Lamar Blvd",
      "phone_line": { "id": 2182, "name": "Lamar Blvd", "phone_number": "+15125550177" },
      "values": { "conversations": 461 }
    },
    {
      "label": "Westside Wash",
      "phone_line": { "id": 2181, "name": "Westside Wash", "phone_number": "+15125550142" },
      "values": { "conversations": 803 }
    }
  ],
  "resolved_group_by": "phone_line",
  "chart": {
    "kind": "bar",
    "title": "Conversations (2026-09-06 to 2026-10-06)",
    "label_kind": "phone_line",
    "series": [
      { "name": "Conversations", "points": [{ "label": "Lamar Blvd", "value": 461 }, { "label": "Westside Wash", "value": 803 }] }
    ]
  },
  "methodology": [
    "Counts each conversation shown on the dashboard, so spam and unanswered calls are left out. When a text or email continues a phone call conversation, it counts under both channels, so channel rows can add up to more than the total."
  ],
  "caveats": [],
  "url": "https://answeringagent.com/dashboard/reports"
}

string

ok when the report ran. See Unsupported requests for the other values.

object

The window the report used: the requested range, the from and to dates (both included), and the team timezone.

object

One entry for each requested metric, keyed by metric. Each entry has a label, a unit (count, percent, minutes, hours, or score), and a value, which is null when there is no data. Rates also include numerator and denominator. A count with a natural base, such as memberships_saved, also has numerator, denominator, and rate. A percent value or rate runs from 0 to 100, rounded to one decimal place.

object[]

The breakdown, when there is one. Each row has a label and values, usually keyed by metric. Only metrics that support the grouping appear in values. A metric with a null value has no data for that row.A row grouped by channel, category, phone_line, or team_member also has that key, next to label:

channel: a channel, such as phone.
category: a category key, such as cancellation. null on the Uncategorized and All other categories rows.
phone_line: the line’s id, name, and phone_number.
team_member: the person’s id and name. null on the Unassigned row.

Use these keys to match rows to your own records. The label is for display.A few breakdowns use other keys in values. team_member_activity uses tasks_completed, messages_sent, and notes_added. account_updates uses account_updates, completed, and failures. conversation_categories by phone_line uses Total plus one key for each category label. split_by uses team member names.Count metrics that support day get a trend in rows even without group_by: daily for ranges up to 31 days, weekly beyond that. Ranges today and yesterday get no trend.

string

The grouping the rows use. It can differ from your group_by. A daily series longer than 62 days comes back weekly.

object

The chart the dashboard draws from the rows: kind (line or bar), title, label_kind, and up to five series of points. Absent when the rows cannot be charted.

string[]

How each metric is counted. Read these before you compare numbers with another system.

string[]

Anything that limits this result, such as missing history or overlapping rows.

string

The dashboard Reports page.

Unsupported requests

The catalog limits which metrics, groupings, and filters go together. A request outside those limits returns status set to unsupported_combination, with a reason, a message, and the allowed_alternatives you can retry with.

{
  "status": "unsupported_combination",
  "error": "unsupported_combination",
  "reason": "unsupported_group_by",
  "message": "None of the selected measures supports that grouping.",
  "allowed_alternatives": { "group_by": [] },
  "url": "https://answeringagent.com/dashboard/reports"
}

An ID in filters.team_member_ids that is not a current team member returns reason set to unknown_team_member, with a message that lists the IDs. These responses come back with HTTP 200, not a 4xx. A client that checks only the HTTP status treats them as success. Check status before you read values. A missing month, from, or to, or a custom range that is reversed or longer than 366 days, is also an unsupported_combination. A malformed body returns 400. That includes an unknown metric key and any field the API does not know, such as grouping or filters.phone_number_id. A filters.phone_line_id that is not one of the key owner’s phone lines returns 404 with code not_found.

Read these definitions first

Several metrics mean something narrower than their name. The methodology in each response says exactly what was counted.

AI-resolved (ai_resolved_conversations) means nobody on your team replied, changed the task, or added a note. It does not mean the customer’s problem was solved. For that, use caller_resolved_rate.
Meaningful conversations (meaningful_conversations) have enough real exchange to judge how they went: phone calls longer than 10 seconds with a transcript where the caller said enough, and website chats with a real exchange or a follow-up task. Texts and emails are never counted.
Handled (handled_conversations) means a meaningful conversation that left no task for your team. Needs team (needs_team_conversations) means a meaningful conversation that did leave one. Handled does not mean the customer’s problem was solved.
Hours to close and hours to first action count elapsed hours from when the conversation or form came in. They are not business hours or time spent working.
First action is the first time a person changed a task’s status or assignee, added a note, sent a reply, or closed it. Creating the task does not count. Because assignment and notes count, hours to first action is not a customer response time. Tasks nobody has acted on are left out of the hours-to-first-action metrics. To see those, run open_tasks with group_by task_age.
Asked for a person, resolved by AI (asked_for_person_ai_resolved_conversations) counts conversations where the customer asked for a person, the caller’s need was met, nobody on your team worked it, and the AI attempted no transfer.
Transfer attempts (transfer_attempts) count conversations where the AI tried to transfer the caller. A transfer attempt does not mean a person answered.
Tasks completed (tasks_completed) counts tasks marked done, including tasks the AI closed on its own. To count closes by people, use task_closes.
Team-work metrics (tasks_worked, task_closes, the hours-to-close and hours-to-first-action metrics, same_day_close_rate, and open_tasks) have data from July 25, 2026. A range that ends before then returns unsupported_combination.
Memberships saved (memberships_saved) counts only cancellation conversations where the AI pitched a retention offer and the membership was saved. A save with no retention offer pitched is not counted. It counts from July 1, 2026, when save tracking started. Its denominator is the cancellation conversations with a retention offer pitched in the same period.
Open tasks right now (open_tasks) is a snapshot. It requires range today.

Examples

AI-resolved rate by week

ai_resolved_rate has no weekly breakdown. Request the two counts by week and divide them. The headline rate for the whole range comes back in values.

{
  "measures": ["conversations", "ai_resolved_conversations", "ai_resolved_rate"],
  "range": "custom",
  "from": "2026-09-08",
  "to": "2026-10-05",
  "group_by": "week"
}

{
  "status": "ok",
  "range": { "requested": "custom", "from": "2026-09-08", "to": "2026-10-05", "timezone": "America/Chicago" },
  "values": {
    "conversations": { "label": "Conversations", "unit": "count", "value": 1184 },
    "ai_resolved_conversations": { "label": "AI-resolved conversations", "unit": "count", "value": 497 },
    "ai_resolved_rate": { "label": "AI-resolved rate", "unit": "percent", "value": 42, "numerator": 497, "denominator": 1184 }
  },
  "rows": [
    { "label": "2026-09-08", "values": { "conversations": 291, "ai_resolved_conversations": 118 } },
    { "label": "2026-09-15", "values": { "conversations": 302, "ai_resolved_conversations": 126 } },
    { "label": "2026-09-22", "values": { "conversations": 288, "ai_resolved_conversations": 121 } },
    { "label": "2026-09-29", "values": { "conversations": 303, "ai_resolved_conversations": 132 } }
  ],
  "resolved_group_by": "week",
  "methodology": [
    "Counts each conversation shown on the dashboard, so spam and unanswered calls are left out. When a text or email continues a phone call conversation, it counts under both channels, so channel rows can add up to more than the total.",
    "Counts conversations the AI finished where nobody on your team replied, changed the task, or added a note. This measures that no person had to step in, not that the caller got what they needed. Caller resolved rate measures that.",
    "AI-resolved conversations divided by all conversations. AI-resolved means no person had to step in, not that the caller got what they needed."
  ],
  "caveats": [],
  "url": "https://answeringagent.com/dashboard/reports"
}

The weekly rate for the week of September 8 is 118 ÷ 291, or 40.5%. Each week label is the first day of a seven-day bucket. Buckets count back from the last day of the range, so pick a range whose length is a multiple of seven to get full weeks. The chart field is left out of this example and the ones below.

Tasks closed per team member, with median hours to close

{
  "measures": ["task_closes", "median_hours_to_close"],
  "range": "30d",
  "group_by": "team_member"
}

{
  "status": "ok",
  "range": { "requested": "30d", "from": "2026-09-06", "to": "2026-10-06", "timezone": "America/Chicago" },
  "values": {
    "task_closes": { "label": "Task closes", "unit": "count", "value": 212 },
    "median_hours_to_close": { "label": "Median hours to close", "unit": "hours", "value": 5.2 }
  },
  "rows": [
    {
      "label": "Priya Shah",
      "team_member": { "id": 9034, "name": "Priya Shah" },
      "values": { "task_closes": 96, "median_hours_to_close": 3.1 }
    },
    {
      "label": "Sam Ortiz",
      "team_member": { "id": 9021, "name": "Sam Ortiz" },
      "values": { "task_closes": 71, "median_hours_to_close": 6.4 }
    },
    {
      "label": "Jordan Lee",
      "team_member": { "id": 9040, "name": "Jordan Lee" },
      "values": { "task_closes": 45, "median_hours_to_close": 9.8 }
    }
  ],
  "resolved_group_by": "team_member",
  "methodology": [
    "Counts each time a person marked a task done. A task reopened and closed again counts twice; tasks closed automatically are left out.",
    "Each time a person closed a task, takes the hours since its conversation or form came in, then reports the middle value. This is elapsed time, not active handling time. Category rows use the task’s current category, not the category it had when it was closed."
  ],
  "caveats": [],
  "url": "https://answeringagent.com/dashboard/reports"
}

Rows are sorted by the first grouped metric, largest first. Hours to close run from when the conversation or form came in. They measure elapsed time, not time spent working.

Retention offers pitched and accepted

{
  "measures": [
    "cancellation_conversations",
    "retention_offers_pitched",
    "retention_offers_accepted",
    "retention_acceptance_rate",
    "memberships_saved"
  ],
  "range": "month",
  "month": "2026-09"
}

{
  "status": "ok",
  "range": { "requested": "month", "from": "2026-09-01", "to": "2026-09-30", "timezone": "America/Chicago" },
  "values": {
    "cancellation_conversations": { "label": "Cancellation conversations", "unit": "count", "value": 64 },
    "retention_offers_pitched": { "label": "Retention offers pitched", "unit": "count", "value": 41 },
    "retention_offers_accepted": { "label": "Retention offers accepted", "unit": "count", "value": 9 },
    "retention_acceptance_rate": { "label": "Retention acceptance rate", "unit": "percent", "value": 22, "numerator": 9, "denominator": 41 },
    "memberships_saved": { "label": "Memberships saved", "unit": "count", "value": 7, "numerator": 7, "denominator": 41, "rate": 17.1 }
  },
  "methodology": [
    "Counts conversations in the Cancellation category.",
    "Counts cancellation conversations where the AI made an offer to keep the customer.",
    "Counts cancellation conversations where the customer accepted the offer the AI made to keep them.",
    "Retention offers accepted divided by retention offers pitched, on cancellation conversations only.",
    "Counts memberships confirmed as saved, starting 2026-07-01 when save tracking began. Earlier conversations have no save record. The save rate divides saves by retention offers pitched from 2026-07-01 on, so its base is only the offers made since tracking began."
  ],
  "caveats": [],
  "url": "https://answeringagent.com/dashboard/reports"
}

None of these metrics has a daily breakdown, so the response has no rows. For one location, add "filters": { "phone_line_id": 2181 }. To see why customers cancel, run cancellation_reasons with group_by set to cancellation_reason.

Conversations by phone line

{
  "measures": ["conversations", "ai_resolved_conversations"],
  "range": "30d",
  "group_by": "phone_line",
  "filters": { "channel": "phone" }
}

{
  "status": "ok",
  "range": { "requested": "30d", "from": "2026-09-06", "to": "2026-10-06", "timezone": "America/Chicago" },
  "values": {
    "conversations": { "label": "Conversations", "unit": "count", "value": 1032 },
    "ai_resolved_conversations": { "label": "AI-resolved conversations", "unit": "count", "value": 431 }
  },
  "rows": [
    {
      "label": "Lamar Blvd",
      "phone_line": { "id": 2182, "name": "Lamar Blvd", "phone_number": "+15125550177" },
      "values": { "conversations": 377, "ai_resolved_conversations": 143 }
    },
    {
      "label": "Westside Wash",
      "phone_line": { "id": 2181, "name": "Westside Wash", "phone_number": "+15125550142" },
      "values": { "conversations": 655, "ai_resolved_conversations": 288 }
    }
  ],
  "resolved_group_by": "phone_line",
  "methodology": [
    "Counts each conversation shown on the dashboard, so spam and unanswered calls are left out. When a text or email continues a phone call conversation, it counts under both channels, so channel rows can add up to more than the total.",
    "Counts conversations the AI finished where nobody on your team replied, changed the task, or added a note. This measures that no person had to step in, not that the caller got what they needed. Caller resolved rate measures that."
  ],
  "caveats": [],
  "url": "https://answeringagent.com/dashboard/reports"
}

The response has one row for each phone line the key’s owner can see, sorted by name. Match rows by phone_line.id. The label is the line name, and when two lines share a name it adds the phone number. The phone channel filter limits the report to calls. Leave it out to count texts, chats, and emails too. For a metric that does not group by phone line, run one report per line with filters.phone_line_id.

Calls per hour

To find your busiest hours, group conversations by hour_of_day and filter to phone calls.

{
  "measures": ["conversations"],
  "range": "30d",
  "group_by": "hour_of_day",
  "filters": { "channel": "phone" }
}

{
  "status": "ok",
  "range": { "requested": "30d", "from": "2026-09-06", "to": "2026-10-06", "timezone": "America/Chicago" },
  "values": {
    "conversations": { "label": "Conversations", "unit": "count", "value": 1032 }
  },
  "rows": [
    { "label": "00:00", "values": { "conversations": 2 } },
    { "label": "01:00", "values": { "conversations": 1 } },
    { "label": "02:00", "values": { "conversations": 1 } },
    { "label": "03:00", "values": { "conversations": 0 } },
    { "label": "04:00", "values": { "conversations": 1 } },
    { "label": "05:00", "values": { "conversations": 3 } },
    { "label": "06:00", "values": { "conversations": 11 } },
    { "label": "07:00", "values": { "conversations": 37 } },
    { "label": "08:00", "values": { "conversations": 70 } },
    { "label": "09:00", "values": { "conversations": 87 } },
    { "label": "10:00", "values": { "conversations": 91 } },
    { "label": "11:00", "values": { "conversations": 85 } },
    { "label": "12:00", "values": { "conversations": 88 } },
    { "label": "13:00", "values": { "conversations": 93 } },
    { "label": "14:00", "values": { "conversations": 87 } },
    { "label": "15:00", "values": { "conversations": 81 } },
    { "label": "16:00", "values": { "conversations": 78 } },
    { "label": "17:00", "values": { "conversations": 72 } },
    { "label": "18:00", "values": { "conversations": 60 } },
    { "label": "19:00", "values": { "conversations": 41 } },
    { "label": "20:00", "values": { "conversations": 24 } },
    { "label": "21:00", "values": { "conversations": 11 } },
    { "label": "22:00", "values": { "conversations": 5 } },
    { "label": "23:00", "values": { "conversations": 3 } }
  ],
  "resolved_group_by": "hour_of_day",
  "methodology": [
    "Counts each conversation shown on the dashboard, so spam and unanswered calls are left out. When a text or email continues a phone call conversation, it counts under both channels, so channel rows can add up to more than the total."
  ],
  "caveats": [],
  "url": "https://answeringagent.com/dashboard/reports"
}

Each row counts the calls that started in that hour, in the team’s timezone, across all 30 days. The busiest hour here is 13:00, with 93 calls. For a daily average, divide by the number of days: 93 ÷ 30 is about 3 calls between 1 and 2 p.m. on a typical day. Add "include_percent_of_total": true to get each hour’s share of the day’s calls.

List the metrics

GET /reports/metrics

Try it in the playground. Returns the full catalog. Read it from code instead of hard-coding the table below. New metrics appear there first.

curl https://answeringagent.com/api/v1/reports/metrics \
  -H "X-API-KEY: $ANSWERING_AGENT_API_KEY"

const res = await fetch('https://answeringagent.com/api/v1/reports/metrics', {
  headers: { 'X-API-KEY': process.env.ANSWERING_AGENT_API_KEY },
});
if (!res.ok) throw new Error(`${res.status}: ${await res.text()}`);
const { measures } = await res.json();

{
  "measures": [
    {
      "key": "conversation_categories",
      "label": "Conversation categories",
      "description": "Conversation counts for every category, optionally for each phone line.",
      "unit": "count",
      "group_by": ["category", "phone_line"],
      "requires_group_by": true,
      "split_by": [],
      "methodology": "Groups conversations that have a category by that category. Percentages use all conversations with a category as the base."
    }
  ],
  "ranges": ["today", "yesterday", "7d", "14d", "30d", "90d", "365d", "mtd", "ytd", "month", "custom"],
  "group_by": ["day", "week", "month", "hour_of_day", "category", "channel", "phone_line", "team_member", "..."],
  "split_by": ["team_member"],
  "categories": [
    { "key": "damage_claim", "label": "Damage Claim" },
    { "key": "cancellation", "label": "Cancellation" },
    { "key": "form", "label": "Form" }
  ],
  "filters": {
    "channel": { "type": "string", "enum": ["phone", "text", "web_chat", "email"] },
    "team_member_ids": { "type": "array", "minItems": 1, "maxItems": 5, "items": { "type": "integer", "exclusiveMinimum": 0 } }
  }
}

When requires_group_by is true, the metric fails without one of its group_by values. categories lists every category key with its label. filters maps each filter name to its JSON Schema. The example shortens measures, categories, and filters.

Metric catalog

Fifty-four metrics as of October 2026. The table is generated from the same code as GET /reports/metrics. A metric with no group_by returns only a total. “Required” means the metric needs one of the listed groupings. Read these definitions first for the terms in the Description column.

Conversations and calls

Key	Label	Description	Unit	`group_by`
`conversations`	Conversations	Every conversation shown on the dashboard: phone calls, texts, website chats, and emails.	count	`day`, `week`, `month`, `channel`, `phone_line`, `hour_of_day`
`ai_resolved_conversations`	AI-resolved conversations	Conversations the AI finished without anyone on your team doing any work.	count	`day`, `week`, `channel`, `phone_line`, `hour_of_day`
`ai_resolved_rate`	AI-resolved rate	Share of conversations the AI finished without anyone on your team doing any work.	percent	None
`meaningful_conversations`	Meaningful conversations	Conversations with enough real exchange to judge how they went. Handled and needs-team conversations are counted out of these.	count	`day`, `week`, `channel`, `phone_line`, `hour_of_day`
`handled_conversations`	Handled conversations	Meaningful conversations that left no follow-up task for your team.	count	`day`, `week`, `channel`, `phone_line`, `hour_of_day`
`handled_rate`	Handled rate	Share of meaningful conversations that left no follow-up task for your team.	percent	None
`needs_team_conversations`	Needs-team conversations	Meaningful conversations that left a follow-up task for your team.	count	`day`, `week`, `channel`, `phone_line`, `hour_of_day`
`asked_for_person_conversations`	Asked for a person	Conversations where the customer asked to speak with a person.	count	`channel`, `phone_line`, `hour_of_day`
`asked_for_person_ai_resolved_conversations`	Asked for a person, resolved by AI	Conversations where the customer asked for a person but the AI resolved it anyway.	count	`channel`, `phone_line`, `hour_of_day`
`asked_for_person_ai_resolved_rate`	Asked for a person, AI-resolved rate	Share of conversations where the customer asked for a person that the AI resolved anyway.	percent	None
`transfer_attempts`	Transfer attempts	Conversations where the AI tried to transfer the caller to your team.	count	`channel`, `phone_line`, `hour_of_day`
`conversation_categories`	Conversation categories	Conversation counts for every category, optionally for each phone line.	count	Required: `category` or `phone_line`
`conversations_with_tasks`	Conversations with tasks	Conversations that left a follow-up task, by channel or over time.	count	`day`, `week`, `channel`, `hour_of_day`
`conversations_with_tasks_rate`	Conversations with tasks rate	Share of conversations that left a follow-up task.	percent	None
`texts_sent`	Texts sent	Text messages sent from conversations that the text provider accepted.	count	None
`avg_duration_minutes`	Average duration	Average conversation length in minutes.	minutes	None
`account_updates`	Account updates	Changes the AI made to customer accounts, grouped by type of change.	count	Required: `action_group`

Call quality (phone calls only)

Key	Label	Description	Unit	`group_by`
`handled_well_rate`	Handled well rate	Share of rated phone calls that the quality review marked handled well.	percent	None
`avg_quality_score`	Average quality score	Average score for how well the AI behaved on meaningful phone calls.	score	None
`quality_review_rate`	Quality review rate	Share of meaningful phone calls flagged for quality review.	percent	None
`caller_resolution_coverage_rate`	Caller resolution coverage	Share of meaningful phone calls with a recorded outcome for the caller.	percent	None
`caller_resolved_rate`	Caller resolved rate	Share of phone calls with a recorded outcome where the caller’s need was fully met on the call.	percent	None
`caller_resolved_with_task_rate`	Caller resolved with a task rate	Share of phone calls where the caller’s need was met that still left a follow-up task.	percent	None
`system_health_rate`	System health rate	Share of meaningful phone calls where your connected systems worked.	percent	None
`accuracy_rate`	Accuracy rate	Share of meaningful phone calls where the AI did not give wrong information.	percent	None
`escalation_failure_rate`	Escalation failure rate	Share of meaningful phone calls that should have been transferred to your team and were not.	percent	None
`quality_failure_types`	Quality failure types	Meaningful phone calls grouped by the main issue the quality review found.	count	Required: `failure_type`
`quality_knowledge_gaps`	Quality knowledge gaps	Phone calls where the AI could not help with what was asked, grouped by the reason.	count	Required: `failure_subcategory`
`questions_answered`	Questions answered	Questions callers asked on phone calls that the AI answered.	count	None
`questions_answered_rate`	Questions answered rate	Answered questions as a share of all questions callers asked on phone calls.	percent	None

Cancellations and offers

Key	Label	Description	Unit	`group_by`
`cancellation_conversations`	Cancellation conversations	Conversations in the Cancellation category.	count	`month`
`cancellation_reasons`	Cancellation reasons	Why customers cancelled, from the reason recorded on each cancellation conversation.	count	Required: `cancellation_reason`
`retention_offers_pitched`	Retention offers pitched	Cancellation conversations where the AI made an offer to keep the customer.	count	None
`retention_offers_accepted`	Retention offers accepted	Cancellation conversations where the customer accepted the offer.	count	None
`retention_acceptance_rate`	Retention acceptance rate	Retention offers accepted as a share of retention offers pitched.	percent	None
`retention_discounts_applied`	Retention discounts applied	Accepted retention discounts that your connected business software confirmed it applied.	count	None
`memberships_saved`	Memberships saved	Cancellation conversations where the AI pitched a retention offer and the membership was saved, counted from 2026-07-01.	count	None
`offers_pitched`	Offers pitched	Offers the AI pitched across all conversations, in the greeting or during the conversation.	count	None
`offers_accepted`	Offers accepted	Offers customers accepted across all conversations, from the greeting or during the conversation.	count	None
`offer_acceptance_rate`	Offer acceptance rate	Offers accepted as a share of offers pitched.	percent	None

Tasks

Key	Label	Description	Unit	`group_by`
`tasks_created`	Tasks created	Tasks created from conversations and forms that came in during the period.	count	None
`tasks_completed`	Tasks completed	Tasks from the period that are now marked done.	count	None
`tasks_still_open`	Tasks still open	Tasks from the period that are not marked done yet.	count	None
`task_completion_rate`	Task completion rate	Tasks completed as a share of tasks created.	percent	None
`task_categories`	Task categories	Task counts for every category.	count	Required: `category`

Team work

Key	Label	Description	Unit	`group_by`
`tasks_worked`	Tasks worked	Distinct tasks a person worked on during the period.	count	`day`, `week`, `team_member`
`task_closes`	Task closes	Times a person marked a task done during the period. A reopened and reclosed task counts again.	count	`day`, `week`, `team_member`
`avg_hours_to_close`	Average hours to close	Average hours from when the conversation or form came in to when a person closed its task.	hours	`category`, `team_member`
`median_hours_to_close`	Median hours to close	Median hours from when the conversation or form came in to when a person closed its task.	hours	`category`, `team_member`
`avg_hours_to_first_action`	Average hours to first action	Average hours from when a conversation or form came in to the first time a person acted on its task. This is the speed-to-lead measure.	hours	`category`, `team_member`
`median_hours_to_first_action`	Median hours to first action	Median hours from when a conversation or form came in to the first time a person acted on its task.	hours	`category`, `team_member`
`same_day_close_rate`	Same-day close rate	Share of task closes by a person that happened on the same local day the request came in.	percent	`team_member`
`open_tasks`	Open tasks right now	Tasks not yet done right now, by current assignee (including unassigned tasks) or by how long ago they came in.	count	`team_member`, `task_age`
`team_member_activity`	Team member activity	Tasks completed, messages sent, and notes added by each team member.	count	Required: `team_member`

group_by month covers at most 12 calendar months. With team_member, the hours-to-first-action metrics credit the person who acted first. For average call length, run avg_duration_minutes with filters.channel set to phone.

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AI-resolved rate by week

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Calls per hour

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Conversations and calls

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​AI-resolved rate by week

​Tasks closed per team member, with median hours to close

​Retention offers pitched and accepted

​Conversations by phone line

​Calls per hour

​List the metrics

​Metric catalog

​Conversations and calls

​Call quality (phone calls only)

​Cancellations and offers

​Tasks

​Team work

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Read these definitions first

Examples

AI-resolved rate by week

Tasks closed per team member, with median hours to close

Retention offers pitched and accepted

Conversations by phone line

Calls per hour

List the metrics

Metric catalog

Conversations and calls

Call quality (phone calls only)

Cancellations and offers

Tasks

Team work