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Hi All,

I am trying to create a report where I will be able to summarise which Divisions and Queues consumed Transcription minutes. I need this to align with the billing period of Invoices. 

I have been able to create the extraction but the minutes are extremly high compared to the consumed. Is there anyone that can assist with the calculation used by Genesys for the consumption numbers used in invoicing?

Hi All,

I am trying to create a report where I will be able to summarise which Divisions and Queues consumed Transcription minutes. I need this to align with the billing period of Invoices. 

I have been able to create the extraction but the minutes are extremly high compared to the consumed. Is there anyone that can assist with the calculation used by Genesys for the consumption numbers used in invoicing?

Hi All,

I am trying to create a report where I will be able to summarise which Divisions and Queues consumed Transcription minutes. I need this to align with the billing period of Invoices. 

I have been able to create the extraction but the minutes are extremly high compared to the consumed. Is there anyone that can assist with the calculation used by Genesys for the consumption numbers used in invoicing?

Hi Justin

Hope the procedure below can help.

Genesys uses "media minutes" for billing Speech & Text Analytics (STA) / Transcription.
These are not simply the duration of calls.

Billing is based on audio duration actually processed by Speech Transcription

For voice, the calculation is roughly:

Transcription Minutes = Total processed audio duration (in minutes)

Voice – Divisions, Queues, Transcription Minutes (approx.)

Endpoint:
POST /api/v2/analytics/conversations/aggregates/query

Example body (for a billing month):
{
  "interval": "2025-11-01T00:00:00.000Z/2025-11-30T23:59:59.999Z",
  "granularity": "P1M",
  "groupBy": [
    "divisionId",
    "queueId",
    "mediaType"
  ],
  "filter": {
    "type": "and",
    "predicates": [
      {
        "type": "dimension",
        "dimension": "mediaType",
        "operator": "matches",
        "value": "voice"
      },
      {
        "type": "dimension",
        "dimension": "hasRecording",
        "operator": "matches",
        "value": "true"
      }
    ]
  },
  "metrics": [
    "tRecording"      // total recorded audio ms
  ]
}

How to get "Transcription Minutes" from this:
For each row where mediaType = voice:
TranscriptionMinutes_Voice = tRecording / 60000

…and then aggregate by:
divisionId → map to division name
queueId → map to queue name

If you want to exclude internal/consult calls, you can tighten the filter with a segmentType predicate or direction = inbound/outbound.
2️⃣ Digital – Divisions, Queues, "Transcription Minutes" Equivalent
For chat/email, there's no real "minutes" of audio, so billing is typically based on text volume. You can approximate consumption using the Transcripts Aggregates API.

Endpoint:
POST /api/v2/analytics/transcripts/aggregates/query

Example body:
{
  "interval": "2025-11-01T00:00:00.000Z/2025-11-30T23:59:59.999Z",
  "granularity": "P1M",
  "groupBy": [
    "divisionId",
    "queueId",
    "mediaType"
  ],
  "filter": {
    "type": "and",
    "predicates": [
      {
        "type": "dimension",
        "dimension": "mediaType",
        "operator": "matches",
        "value": "chat"
      }
    ]
  },
  "metrics": [
    "oTranscriptsByteCount"   // total bytes of transcript text
  ]
}

If you also want email, add an or for mediaType:
"filter": {
  "type": "and",
  "clauses": [
    {
      "type": "or",
      "predicates": [
        {
          "type": "dimension",
          "dimension": "mediaType",
          "operator": "matches",
          "value": "chat"
        },
        {
          "type": "dimension",
          "dimension": "mediaType",
          "operator": "matches",
          "value": "email"
        }
      ]
    }
  ]
}

Converting to a "minutes" approximation
You'll need to align this with your billing model. A common internal approximation is something like:
TranscriptionMinutes_Digital = (oTranscriptsByteCount / 1024 / X)

Where X is your assumed "KB per minute equivalent" (you'll get that from your commercial / Genesys account team or by reverse-engineering from invoice totals).
Combining Voice + Digital per Division and Queue
Once you call both APIs:

Normalize keys: divisionId, queueId, mediaType
Convert:

VoiceMinutes = tRecording / 60000
DigitalMinutes = oTranscriptsByteCount → minutes via your rule

Create a final dataset:

Division    Queue    Media Type    Voice Minutes    Digital Minutes        Total "Transcription"
Div A        Q1        voice        12,345.6            –                12,345.6
Div A        Q1        chat            –            2,100.0                2,100.0
…    …    …    …    …    …

Practical step-by-step (summary)

Define the billing period
Example: 2025-11-01 to 2025-11-30 (in UTC).

Call Conversations Aggregates (voice)
POST /api/v2/analytics/conversations/aggregates/query

Group by: divisionId, queueId, mediaType
Metric: tRecording
Calculate: Minutes_Voice = tRecording / 60000.

Call Transcripts Aggregates (digital)
POST /api/v2/analytics/transcripts/aggregates/query

Group by: divisionId, queueId, mediaType
Metric: oTranscriptsByteCount

Convert to minutes according to your business rule (or keep it as bytes/KB).

Fetch reference tables
Divisions: GET /api/v2/authorization/divisions
Queues: GET /api/v2/routing/queues

Join everything in BI / Excel

Relate using the IDs (divisionId, queueId).
Create columns such as:
Division Name
Queue Name
Voice Minutes
Digital Bytes/KB or Digital Minutes
Total "Transcription Consumption" (if you want to sum voice + digital).

Hi All,

I am trying to create a report where I will be able to summarise which Divisions and Queues consumed Transcription minutes. I need this to align with the billing period of Invoices. 

I have been able to create the extraction but the minutes are extremly high compared to the consumed. Is there anyone that can assist with the calculation used by Genesys for the consumption numbers used in invoicing?

Hi Justin

Hope the procedure below can help.

Genesys uses "media minutes" for billing Speech & Text Analytics (STA) / Transcription.
These are not simply the duration of calls.

Billing is based on audio duration actually processed by Speech Transcription

For voice, the calculation is roughly:

Transcription Minutes = Total processed audio duration (in minutes)

Voice – Divisions, Queues, Transcription Minutes (approx.)

Endpoint:
POST /api/v2/analytics/conversations/aggregates/query

Example body (for a billing month):
{
  "interval": "2025-11-01T00:00:00.000Z/2025-11-30T23:59:59.999Z",
  "granularity": "P1M",
  "groupBy": [
    "divisionId",
    "queueId",
    "mediaType"
  ],
  "filter": {
    "type": "and",
    "predicates": [
      {
        "type": "dimension",
        "dimension": "mediaType",
        "operator": "matches",
        "value": "voice"
      },
      {
        "type": "dimension",
        "dimension": "hasRecording",
        "operator": "matches",
        "value": "true"
      }
    ]
  },
  "metrics": [
    "tRecording"      // total recorded audio ms
  ]
}

How to get "Transcription Minutes" from this:
For each row where mediaType = voice:
TranscriptionMinutes_Voice = tRecording / 60000

…and then aggregate by:
divisionId → map to division name
queueId → map to queue name

If you want to exclude internal/consult calls, you can tighten the filter with a segmentType predicate or direction = inbound/outbound.
2️⃣ Digital – Divisions, Queues, "Transcription Minutes" Equivalent
For chat/email, there's no real "minutes" of audio, so billing is typically based on text volume. You can approximate consumption using the Transcripts Aggregates API.

Endpoint:
POST /api/v2/analytics/transcripts/aggregates/query

Example body:
{
  "interval": "2025-11-01T00:00:00.000Z/2025-11-30T23:59:59.999Z",
  "granularity": "P1M",
  "groupBy": [
    "divisionId",
    "queueId",
    "mediaType"
  ],
  "filter": {
    "type": "and",
    "predicates": [
      {
        "type": "dimension",
        "dimension": "mediaType",
        "operator": "matches",
        "value": "chat"
      }
    ]
  },
  "metrics": [
    "oTranscriptsByteCount"   // total bytes of transcript text
  ]
}

If you also want email, add an or for mediaType:
"filter": {
  "type": "and",
  "clauses": [
    {
      "type": "or",
      "predicates": [
        {
          "type": "dimension",
          "dimension": "mediaType",
          "operator": "matches",
          "value": "chat"
        },
        {
          "type": "dimension",
          "dimension": "mediaType",
          "operator": "matches",
          "value": "email"
        }
      ]
    }
  ]
}

Converting to a "minutes" approximation
You'll need to align this with your billing model. A common internal approximation is something like:
TranscriptionMinutes_Digital = (oTranscriptsByteCount / 1024 / X)

Where X is your assumed "KB per minute equivalent" (you'll get that from your commercial / Genesys account team or by reverse-engineering from invoice totals).
Combining Voice + Digital per Division and Queue
Once you call both APIs:

Normalize keys: divisionId, queueId, mediaType
Convert:

VoiceMinutes = tRecording / 60000
DigitalMinutes = oTranscriptsByteCount → minutes via your rule

Create a final dataset:

Division    Queue    Media Type    Voice Minutes    Digital Minutes        Total "Transcription"
Div A        Q1        voice        12,345.6            –                12,345.6
Div A        Q1        chat            –            2,100.0                2,100.0
…    …    …    …    …    …

Practical step-by-step (summary)

Define the billing period
Example: 2025-11-01 to 2025-11-30 (in UTC).

Call Conversations Aggregates (voice)
POST /api/v2/analytics/conversations/aggregates/query

Group by: divisionId, queueId, mediaType
Metric: tRecording
Calculate: Minutes_Voice = tRecording / 60000.

Call Transcripts Aggregates (digital)
POST /api/v2/analytics/transcripts/aggregates/query

Group by: divisionId, queueId, mediaType
Metric: oTranscriptsByteCount

Convert to minutes according to your business rule (or keep it as bytes/KB).

Fetch reference tables
Divisions: GET /api/v2/authorization/divisions
Queues: GET /api/v2/routing/queues

Join everything in BI / Excel

Relate using the IDs (divisionId, queueId).
Create columns such as:
Division Name
Queue Name
Voice Minutes
Digital Bytes/KB or Digital Minutes
Total "Transcription Consumption" (if you want to sum voice + digital).

Hi All,

I am trying to create a report where I will be able to summarise which Divisions and Queues consumed Transcription minutes. I need this to align with the billing period of Invoices. 

I have been able to create the extraction but the minutes are extremly high compared to the consumed. Is there anyone that can assist with the calculation used by Genesys for the consumption numbers used in invoicing?

Hi Justin

Hope the procedure below can help.

Genesys uses "media minutes" for billing Speech & Text Analytics (STA) / Transcription.
These are not simply the duration of calls.

Billing is based on audio duration actually processed by Speech Transcription

For voice, the calculation is roughly:

Transcription Minutes = Total processed audio duration (in minutes)

Voice – Divisions, Queues, Transcription Minutes (approx.)

Endpoint:
POST /api/v2/analytics/conversations/aggregates/query

Example body (for a billing month):
{
  "interval": "2025-11-01T00:00:00.000Z/2025-11-30T23:59:59.999Z",
  "granularity": "P1M",
  "groupBy": [
    "divisionId",
    "queueId",
    "mediaType"
  ],
  "filter": {
    "type": "and",
    "predicates": [
      {
        "type": "dimension",
        "dimension": "mediaType",
        "operator": "matches",
        "value": "voice"
      },
      {
        "type": "dimension",
        "dimension": "hasRecording",
        "operator": "matches",
        "value": "true"
      }
    ]
  },
  "metrics": [
    "tRecording"      // total recorded audio ms
  ]
}

How to get "Transcription Minutes" from this:
For each row where mediaType = voice:
TranscriptionMinutes_Voice = tRecording / 60000

…and then aggregate by:
divisionId → map to division name
queueId → map to queue name

If you want to exclude internal/consult calls, you can tighten the filter with a segmentType predicate or direction = inbound/outbound.
2️⃣ Digital – Divisions, Queues, "Transcription Minutes" Equivalent
For chat/email, there's no real "minutes" of audio, so billing is typically based on text volume. You can approximate consumption using the Transcripts Aggregates API.

Endpoint:
POST /api/v2/analytics/transcripts/aggregates/query

Example body:
{
  "interval": "2025-11-01T00:00:00.000Z/2025-11-30T23:59:59.999Z",
  "granularity": "P1M",
  "groupBy": [
    "divisionId",
    "queueId",
    "mediaType"
  ],
  "filter": {
    "type": "and",
    "predicates": [
      {
        "type": "dimension",
        "dimension": "mediaType",
        "operator": "matches",
        "value": "chat"
      }
    ]
  },
  "metrics": [
    "oTranscriptsByteCount"   // total bytes of transcript text
  ]
}

If you also want email, add an or for mediaType:
"filter": {
  "type": "and",
  "clauses": [
    {
      "type": "or",
      "predicates": [
        {
          "type": "dimension",
          "dimension": "mediaType",
          "operator": "matches",
          "value": "chat"
        },
        {
          "type": "dimension",
          "dimension": "mediaType",
          "operator": "matches",
          "value": "email"
        }
      ]
    }
  ]
}

Converting to a "minutes" approximation
You'll need to align this with your billing model. A common internal approximation is something like:
TranscriptionMinutes_Digital = (oTranscriptsByteCount / 1024 / X)

Where X is your assumed "KB per minute equivalent" (you'll get that from your commercial / Genesys account team or by reverse-engineering from invoice totals).
Combining Voice + Digital per Division and Queue
Once you call both APIs:

Normalize keys: divisionId, queueId, mediaType
Convert:

VoiceMinutes = tRecording / 60000
DigitalMinutes = oTranscriptsByteCount → minutes via your rule

Create a final dataset:

Division    Queue    Media Type    Voice Minutes    Digital Minutes        Total "Transcription"
Div A        Q1        voice        12,345.6            –                12,345.6
Div A        Q1        chat            –            2,100.0                2,100.0
…    …    …    …    …    …

Practical step-by-step (summary)

Define the billing period
Example: 2025-11-01 to 2025-11-30 (in UTC).

Call Conversations Aggregates (voice)
POST /api/v2/analytics/conversations/aggregates/query

Group by: divisionId, queueId, mediaType
Metric: tRecording
Calculate: Minutes_Voice = tRecording / 60000.

Call Transcripts Aggregates (digital)
POST /api/v2/analytics/transcripts/aggregates/query

Group by: divisionId, queueId, mediaType
Metric: oTranscriptsByteCount

Convert to minutes according to your business rule (or keep it as bytes/KB).

Fetch reference tables
Divisions: GET /api/v2/authorization/divisions
Queues: GET /api/v2/routing/queues

Join everything in BI / Excel

Relate using the IDs (divisionId, queueId).
Create columns such as:
Division Name
Queue Name
Voice Minutes
Digital Bytes/KB or Digital Minutes
Total "Transcription Consumption" (if you want to sum voice + digital).

Hi All,

I am trying to create a report where I will be able to summarise which Divisions and Queues consumed Transcription minutes. I need this to align with the billing period of Invoices. 

I have been able to create the extraction but the minutes are extremly high compared to the consumed. Is there anyone that can assist with the calculation used by Genesys for the consumption numbers used in invoicing?