Post-call analytics - Amazon Transcribe

Post-call analytics

Call Analytics provides post-call analyses, which are useful for monitoring customer service trends.

Post-call transcriptions offer the following insights:

Post-call insights

This section details the insights available for post-call analytics transcriptions.

Call characteristics

The call characteristics feature measures the quality of agent-customer interactions using these criteria:

  • Interruption: Measures if and when one participant cuts off the other participant mid-sentence. Frequent interruptions may be associated with rudeness or anger, and could correlate to negative sentiment for one or both participants.

  • Loudness: Measures the volume at which each participant is speaking. Use this metric to see if the caller or the agent is speaking loudly or yelling, which is often indicative of being upset. This metric is represented as a normalized value (speech level per second of speech in a given segment) on a scale from 0 to 100, where a higher value indicates a louder voice.

  • Non-talk time: Measures periods of time that do not contain speech. Use this metric to see if there are long periods of silence, such as an agent keeping a customer on hold for an excessive amount of time.

  • Talk speed: Measures the speed at which both participants are speaking. Comprehension can be affected if one participant speaks too quickly. This metric is measured in words per minute.

  • Talk time: Measures the amount of time (in milliseconds) each participant spoke during the call. Use this metric to help identify if one participant is dominating the call or if the dialogue is balanced.

Here's an output example.

Call summarization

Call summarization provides succinct summaries of the important components in agent-customer calls, including issues, action items, and outcomes for each participant.

Using call summarization, you can:

  • Reduce the need for manual note-taking during and after calls

  • Improve agent efficiency so they can respond faster to customers

  • Streamline supervisor reviews, as call summaries are much quicker to digest than entire transcripts

Call summarization works across all industries and business sectors, and is context-based.


Call summarization is supported with these English language dialects: Australian (en-AU), British (en-GB), Indian (en-IN), Irish (en-IE), New Zealand (en-NZ), Scottish (en-AB), South African (en-ZA), US (en-US), and Welsh (en-WL).

Call summarization works out-of-the-box and thus doesn't support customization, such as model training or custom categories.

Here's an output example.

Custom categorization

Use call categorization to flag keywords, phrases, sentiment, or actions within a call. Our categorization options can help you triage escalations, such as negative-sentiment calls with many interruptions, or organize calls into specific categories, such as company departments.

The criteria you can add to a category include:

  • Non-talk time: Periods of time when neither the customer nor the agent is talking.

  • Interruptions: When the customer or the agent is interrupting the other person.

  • Customer or agent sentiment: How the customer or the agent is feeling during a specified time period. If at least 50 percent of the conversation turns (the back-and-forth between two speakers) in a specified time period match the specified sentiment, Amazon Transcribe considers the sentiment a match.

  • Keywords or phrases: Matches part of the transcription based on an exact phrase. For example, if you set a filter for the phrase "I want to speak to the manager", Amazon Transcribe filters for that exact phrase.

You can also flag the inverse of the previous criteria (talk time, lack of interruptions, a sentiment not being present, and the lack of a specific phrase).

Here's an output example.

For more information on categories or to learn how to create a new category, see Creating categories for post-call transcriptions.

Sensitive data redaction

Sensitive data redaction replaces personally identifiable information (PII) in the text transcript and the audio file. A redacted transcript replaces the original text with [PII]; a redacted audio file replaces spoken personal information with silence. This parameter is useful for protecting customer information.


Post-call PII redaction is supported with US English (en-US).

To view the list of PII that is redacted using this feature, or to learn more about redaction with Amazon Transcribe, see Redacting or identifying personally identifiable information.

Here is an output example.

Sentiment analysis

Sentiment analysis estimates how the customer and agent are feeling throughout the call. This metric is represented as both a quantitative value (with a range from 5 to -5) and a qualitative value (positive, neutral, mixed, or negative). Quantitative values are provided per quarter and per call; qualitative values are provided per turn.

This metric can help identify if your agent is able to delight an upset customer by the time the call ends.

Sentiment analysis works out-of-the-box and thus doesn't support customization, such as model training or custom categories.

Here's an output example.