Introduction
AWS Transcribe Call analytics includes the orderly examination, collection, and interpretation of voice information from phone intelligence. By changing over communication into content and applying different analytical methods, businesses can extract important experiences from client discussions. These experiences can incorporate understanding client estimation and identifying common issues. Call analytics uses common language preparing and machine learning to convert raw call information into actionable insights, upgrading operational productivity and decision-making.
Post-call analytics is important for businesses looking to improve client benefit, optimize operations, and drive key activities. By analyzing calls after they occur, organizations can recognize patterns and reveal root causes of issues. This review examination makes a difference in improving item offerings, and upgrading client involvement.
Additionally, post-call analytics gives a complete view of execution measurements, enabling continuous advancement and educated decision-making based on real-world information.
Understanding AWS Transcribe
Overview of AWS Transcribe
AWS Transcribe is an automatic speech recognition service provided by AWS (Amazon Web Services). It provides features to accurately and efficiently convert spoken language into text. It is designed to handle different audio formats and languages. A
WS Transcribe supports both batch and real-time transcription processes. With the advanced machine learning algorithms, AWS Transcribe can easily transcribe and recognize speech with high accuracy and precision.
Key Features and Benefits
AWS Transcribe provides a range of features that make it an adaptable tool for speech-to-text conversion. Key Features provided by AWS Transcribe are speaker diarization which can differentiate multiple speakers in a conversation and custom dictionary which allows users to add industry terms and specific terms to improve accuracy. This service also supports channel identification which is very useful for transcribing audio with many input sources.
The benefits of using AWS Transcribe are it increases productivity by automating the transcription process which will help in reducing the need for manual transcription. The accuracy of the transcriptions help businesses to extract important information from the audio data, leading towards better decision-making and improved customer experiences.
Important features that AWS Transcribe has is that it integrates smoothly with other AWS services, such as Amazon S3 and AWS Lambda for managing large volumes of audio data.
Core Concepts in AWS Transcribe Call Analytics
What is Call Analytics?
Call analytics is the process of efficient analysis of phone interactions to extract important and meaningful insights. This process converts voice data into text and applies different analytics techniques to identify trends and understand customer sentiments.
By using AWS Transcribe call analytics features that uses technologies such as natural language processing(NLP) and machine learning to find patterns, businesses are able to optimize agent performance, enhance their customer service and make informed decisions.
Components of Effective Call Analytics
Effective AWS Transcribe call analytics consists of several key components. These include:
- Sentiment Analysis: Evaluating the tone of the conversation to analyse customer satisfaction and dissatisfaction.
- Important Insights: Extracting important and meaningful information that can be helpful to improve customer service and to refine business strategies.
- Speaker Identification: Help in differentiating between different speakers in a conversation to calculate statements accurately. This property is particularly important in multi-party calls such as in customer support centers.
- Speech-to-text Conversion: This Speech-to-text Conversion helps in transforming audio recordings into written text which help in finding insights from the call recordings.
- Keyword and phrase finding: This feature helps in detecting specific phrases and words that are relevant to the business, for example issues, competitor mentions or products names.
- Natural Language Processing(NLP): AWS Transcribe call analytics apply NLP techniques to understand the sentiment, context and intent behind the spoken words. This component helps in identifying customer emotions and key topics discussed.
Metrics and KPIs in AWS Transcribe Call Analytics
Metrics and KPIs provided by Post-call AWS Transcribe APIs are as follows:
Call Characteristics
The AWS Transcribe call analytics’ KPIs measure the quality of agent-customer interactions using different criteria, such as:
Talk time: It measures the amount of time each participant spoke during the call. This feature helps in identifying if one participant is dominating the call or if the dialogue is balanced.
Non-talk time: It measures the amount of time that does not contain speech. This metric will help in identifying the long periods of silence, such as if an agent is keeping customers on hold for a large amount of time.
Talk Speed: It measures the speed at which both the participants are speaking. To measure this metric, words per minute are used.
Loudness: It measures the volume at which participants are speaking. Using this metric we can see if an agent or customer is speaking loudly. This metric is represented as a normalized value in scale from 0 to 100, where a higher value indicates a louder voice.
Interruption: It measures when one participant cuts off the other participant in the middle. Frequent interruptions may be related to anger or rudeness and could correlate to negative sentiment for one of the participants.
Issues, Action items and outcomes: It helps in identifying issues, outcome and action items from the call.
Generative call summarization
Generative call summarization feature of AWS Transcribe call analytics creates a summary of the entire call in which it captures all key components such as reason of the call, next steps and steps which are taken to resolve issues.
Generative call summarization involves below given properties:
- Reduces the need for manual note-taking after the calls.
- It helps in speeding up supervisor reviews which will be much quicker to review than entire transcripts.
- It helps in improving agent efficiency as they can spend more time in talking to customers rather than waiting in queue for the next call.
Sensitive Data Redaction
Sensitive data redaction replaces PII (Personally Identifiable Information) in the text transcript and the audio file. Sensitive information is replaced by PII. A redacted audio file replaces customer personal information with silence. This functionality is useful for protecting customer information.
The list of PII that AWS Transcribe use are:
- NAME
- ADDRESS
- BANK_ACCOUNT_NUMBER
- BANK ROUTING
- CREDIT_DEBIT_CVV
- CREDIT_DEBIT_EXPIRY
- CREDIT_DEBIT_NUMBER
- PIN
- SSN
Sentiment Analysis
Sentiment Analysis helps in estimating how the agent and customer are feeling during the call.
This metric is shown as both qualitative and quantitative value.
Quantitative value (with a range from 5 to -5) and a qualitative value (positive, mixed, negative and neutral). Quantitative values are determined per quarter as well as per call.
This metric will help in identifying if the agent is able to satisfy an upset customer by the time the call ends.
Implementation
Post Call Analytics
For Post call analytics user can use following methods:
- AWS Management console
- AWS CLI
- AWS SDK for Python(boto3)
Below is the code snippet of Post call analytics using AWS SDK for python (boto3)
Requirements
- First Create input s3 bucket to store the call recording audio files.
- Create output s3 bucket where output of AWS Transcribe call analytics will get saved.
- Create an AWS Transcribe role.
- Create a data access role and provide all access to that role to extract audio files from transcribe and save result data to s3.
transcribe = boto3.client('transcribe', 'AWS Region')
job_name = "first-call-analytics-job"
job_uri = "s3://EXAMPLE-BUCKET/input-files/my-media-file.flac"
output_bucket = "s3://EXAMPLE-BUCKET/output-files/"
data_access_role = "arn:aws:iam::100033324433:role/Role"
transcribe.start_call_analytics_job(
CallAnalyticsJobName = job_name,
Media = {
'MediaFileUri': job_uri
},
DataAccessRoleArn = data_access_role,
Outputbucket = output_bucket,
ChannelDefinitions = [
{
'ChannelId': 0,
'ParticipantRole': 'AGENT'
},
{
'ChannelId': 1,
'ParticipantRole': 'CUSTOMER'
}
]
)
while True:
status = transcribe.get_call_analytics_job(CallAnalyticsJobName = job_name)
if status['CallAnalyticsJob']['CallAnalyticsJobStatus'] in ['COMPLETED', 'FAILED']:
break
print("Not ready yet...")
time.sleep(5)
print(status)
Output
The output of AWS Transcribe post call analytics will give output in json format. Below is the sample output.
Call Characteristics
Fig. 1
Fig. 2
Sentiment
Fig.3
Summarization
Fig. 4
PII Redaction
Fig. 5
Language detection
Fig. 6
Real-world Applications
Quality Training and Assurance
AWS Transcribe call analytics supports quality assurance by examining agent interactions and provides adherence to standards. Insights from AWS Transcribe call analytics output are used to build targeted training programs and improve overall quality.
Customer Support Analytics
AWS Transcribe call analytics in customer support helps in identifying issues, assessing agent performance and enhanced customer satisfaction. By analyzing customer call data businesses can integrate processes and provide more effective solutions.
Sales call Analysis
AWS Transcribe call analytics provide sales team with valuable insights by identifying designs in successful calls, highlighting effective deals procedures, and checking client inclinations. By using this data-driven approach, the sales team can refine their procedures, tailor their pitches, and eventually make successful conversions. Analyzing call information makes a difference in understanding what works best in deals discussions, allowing teams to focus on strategies that drive superior outcomes and upgrade in deals execution.
Conclusion
AWS Transcribe Call analytics uses advanced technologies to convert raw call information into actionable experiences, driving changes in client support, deals, and quality confirmation. By understanding and applying these experiences, businesses can upgrade execution and accomplish greater client fulfillment.
FAQs
AWS Transcribe improves post-call analytics by accurately converting speech to text, providing detailed analysis of conversations which includes extracting actionable insights, trends to increase operational efficiency and customer service.
AWS Transcribe offers correct speech to text conversion, which supports multiple languages, helps in identifying speakers and easily get integrated with other AWS services. All these features facilitate detailed trends identification, call analysis, increasing operational efficiency, and customer service.
AWS Transcribe opens the control of post-call analytics by giving accurate translations, recognizing speakers, and supporting assumption examination. This enables businesses to extract profitable experiences, track execution measurements, and make data-driven choices to improve customer interactions and operational proficiency.
AWS Transcribe highlights post-call information examination incorporating exact speech-to-text transformation, speaker identification, multiple languages, and integration with other AWS administrations. These capabilities empower comprehensive analysis, trend detection, and actionable insights from call information.
