Most product teams track feature adoption in Amplitude and sprint velocity in Linear, but you are likely ignoring the richest signal in your entire stack: the unstructured audio sitting in your contact center. Every support call contains a caller's exact words, the product they stumbled on, the fix that worked or did not, and how they felt about all of it. The problem is that none of this reaches your CRM or product analytics automatically. It sits in an audio file until an agent types three words into a CRM field and moves on.
This piece defines the specific data points a complete support call record should contain, explains where manual capture breaks down, and shows a concrete pattern for extracting all of it from a clean async transcript into your CRM and product analytics using structured JSON prompting.
Preventing product drift with customer signals
Product drift does not happen because a team stops caring about users. It happens when teams build roadmaps on assumptions rather than aggregated signals, and the most granular signals, the ones where a customer describes exactly what broke and exactly what they needed, live in call audio that no one has structured.
When you aggregate call data across thousands of interactions, patterns emerge that NPS scores and app store reviews miss entirely. If a significant portion of support calls reference confusion about a recently shipped UI change, that quantitative signal can carry more weight than a planned roadmap item. You already have the data. The challenge is extracting it reliably at scale.
Feed product analytics and the roadmap with user insights
Every call that mentions a specific feature failure is a vote on your roadmap that currently goes uncounted. When a B2B SaaS support team can segment calls by product area and see that a recent change produced a spike in related tickets, that is direct evidence to prioritize a rollback or a UX fix over a net-new capability. The CCaaS use case architecture Gladia supports surfaces these patterns from contact center audio at scale, not just after a manual review cycle.
Ensure agents have full customer context
When an agent picks up a repeat caller, they need the previous call's resolution steps, sentiment trajectory, and unresolved items before the first sentence is spoken. Without a structured call record from the previous interaction, the agent starts from scratch, the caller repeats themselves, and handle time climbs. Historical data capture is the baseline requirement for a support experience that does not frustrate the customers most likely to churn.
Resolution data for scalable unit costs
Accurate resolution records keep unit costs predictable. When the resolution field is incomplete or wrong, two costs rise at once: repeat contacts climb because the next agent starts without context, and after-call work stretches as agents reconstruct what happened. Capturing the exact fix and the first-call resolution status straight from the transcript removes both.
Core data points every support call should capture
A complete support call record contains seven categories of structured data, each serving a different downstream consumer, from the CRM to the coaching scorecard to the product analytics dashboard.
- Caller identity and account information
- Problem severity and impact
- User's previous attempts
- Resolution steps
- Voice of customer (sentiment, intent, feature requests)
- Follow-up commitments
- Call metadata (duration, timestamps, speaker turns)
Caller identity and account information
The baseline identity layer covers:
- Customer ID: The unique identifier that links the call to the CRM record.
- Account tier and lifetime value: Priority routing and escalation thresholds depend on knowing whether you are talking to a free trial user or a top-tier enterprise account.
- Contact information: Verified phone, email, and preferred contact channel for follow-up.
- Previous interaction history: Open tickets, recent purchases, and unresolved issues from prior calls.
Where regulations apply, PII redaction is available as an optional async feature in Gladia's pipeline. It must be explicitly configured and does not activate by default.
Problem severity and impact
Severity classification drives everything from SLA clock behavior to escalation routing. A complete severity record includes:
- Business impact classification: Priority levels based on organizational standards.
- Scope of impact: Number of users or systems affected.
- Financial impact: Revenue or transaction value at risk as reported by the caller.
- Environment details: Product version or deployment tier where the issue occurs.
A caller describing a billing failure during month-end close carries different urgency than one reporting a cosmetic UI bug, and the structured record should reflect that distinction explicitly.
Documenting user's previous attempts
Self-service failure data is one of the most underused signals in product development. When callers describe the documentation they read, the chatbot answers they tried, or the troubleshooting steps they attempted before calling, each of those represents a dead end in your UX. Aggregating these signals across hundreds of calls identifies where users consistently get stuck before they escalate, which is exactly where a product investment pays off in call deflection.
Support call resolution steps
The resolution record should capture the exact fix applied and whether the call reached first-call resolution (FCR). FCR is widely tracked in contact centers because repeat contacts drive up operational costs, and tracking which resolutions hold versus which generate callbacks feeds directly into knowledge base quality.
Capturing voice of customer
This is where the richest product signal lives. The voice of customer (VoC) record covers:
- Overall sentiment: Positive, neutral, or negative at the call level. Sentiment is derived from transcript text, not vocal emotion.
- Customer intent: The underlying job the customer was trying to do when the issue occurred.
- Explicit feature requests: Verbatim phrases like "I wish I could export this to CSV" are product signals that manual tagging rarely captures in full.
- Churn risk signals: Phrases indicating the customer is evaluating alternatives or questioning their renewal.
Ensuring effective follow-ups
Every commitment made on a call needs a structured record: tickets or cases created, promised actions with deadlines, scheduled callbacks, and escalation paths. When agents over-promise and under-deliver, it is not because they are careless. It is because the information lives only in their memory from the moment the call ends.
Agent validation: Ensuring accurate metrics
Automation handles extraction, but the human layer still owns validation. Agents carry context that no transcript can supply: the tone that does not come through in word choice, the caller they recognize from a previous escalation, the account detail that is one version behind in the CRM. The goal is not to remove agents from data capture but to remove the parts that slow them down without adding accuracy.
The table below compares the two approaches across the dimensions that drive cost and data quality decisions:
| Dimension |
Manual agent tagging |
Automated JSON extraction |
| Time cost per call |
Varies by industry and workflow |
Async processing (seconds to minutes) |
| Data granularity |
Limited CRM fields |
Extensive structured JSON keys |
| Error risk |
Subject to memory and time constraints |
Inherits transcription accuracy limitations |
| Scalability |
Limited at high call volumes |
Handles high call volumes |
Traditional wrap-up workflow: The agent selects a disposition code, types a free-text summary, updates the CRM record, and flags follow-up actions, typically within an ACW window that varies by industry. In some industries like banking and financial services, ACW may run longer due to transaction verification and compliance documentation. The resulting record is selective by necessity: agents document what they remember, in the time they have, using whatever taxonomy the CRM enforces.
Manual data entry: the scaling bottleneck
Manual tagging fails because it doesn't scale and only captures what agents remember, not the full call. Rushed documentation correlates directly with repeat contacts, quality failures, and compliance violations. The granular signals, the exact phrase a customer used, the self-service step they described, the feature they asked for three times, never leave the audio file.
Generating structured data from transcripts
The pivot from manual to automated data capture does not require changing the call workflow. The agent handles the call. After it ends, your engineering team routes the audio to an async transcription API, a structured LLM prompt extracts every data category from the transcript, and the result writes directly to the CRM. Agents validate exceptions rather than building the entire record from scratch.
The pipeline your engineers will configure routes raw audio through Gladia's async API, which includes Solaria-1 ASR (Gladia's proprietary speech recognition model that achieves on average 29% lower WER than alternatives on conversational speech) and pyannoteAI Precision-2 diarization (the model behind Gladia's async speaker attribution). The output is a clean transcript with speaker labels, which flows through a structured LLM prompt to generate JSON that your CRM ingests via webhook.
Minimize data extraction costs
Running the cost model at realistic call center volumes illustrates the difference. At Gladia's Growth plan rate of as low as $0.20 per hour for async transcription, with diarization, entity recognition, and sentiment analysis included and no add-on fees, the extraction layer becomes economically viable at scale compared to manual wrap-up labor costs.
Extracting call data with structured prompts
The audio-to-LLM feature applies structured prompts directly to the transcript. Your engineers define the fields in prompt instructions, pass them in the API config, and receive structured JSON responses. The config looks like this:
{
"audio_to_llm": true,
"audio_to_llm_config": {
"prompts": [
"Extract the following from the call: primary topic, customer sentiment, any feature requests mentioned, resolution outcome",
"Summarize the key points from this support call as bullet points"
]
}
}
The response result.audio_to_llm.results[].results.response provides structured JSON output that you can route to your CRM or other systems. No intermediary processing is required, and human review can be limited to exceptions flagged by confidence thresholds.
Reducing agent workload without losing accuracy
Automated extraction captures more detail than a rushed agent because it reads the full transcript rather than the agent's memory of it. In production implementations, QA teams now validate AI findings rather than manually reviewing calls, which is the correct division of labor: humans checking machine output for exceptions rather than humans building the entire record from raw audio. The async architecture for AI note-takers Gladia has documented processes one hour of audio in under 60 seconds, meaning the structured record is ready before the agent's wrap-up window closes.
Implementing Gladia for call transcript JSON
The pipeline your engineers will implement has three steps: transcribe accurately, map to JSON, and query for specific fields. Each step depends on the one before it, and the LLM extraction is only as good as the transcript it reads.
Step 1: Your engineers configure accurate transcription
Downstream data quality depends directly on the accuracy of the transcription layer. Transcription errors can corrupt CRM entries, affect coaching scores, and turn valid feature requests into uninterpretable strings. On conversational speech, Solaria-1 (Gladia's proprietary ASR model) achieves on average 29% lower WER than alternative providers, benchmarked across multiple providers, datasets, and hours of audio using an open, reproducible methodology.
For multilingual contact centers, the accuracy differential widens. Solaria-1 covers 100+ languages, including 42 not supported by any other API-level STT provider, such as Tagalog, Bengali, Punjabi, Tamil, Urdu, and Marathi, which matter when BPO operations run across Southeast Asia or South Asia. True code-switching detection works across all 100+ supported languages, including mid-conversation language changes.
Aircall, processing over 1 million calls per week through Gladia, cut transcription time by 95%, from 30 minutes to 1.5 minutes per call, while running search, AI summaries, sentiment analysis, and other features from their API integration.
Step 2: Your engineers map call data to JSON
Once the transcript is clean, Gladia's async API returns word-level timestamps, speaker labels from pyannoteAI's Precision-2 diarization model, translation if configured, and the enrichment fields your team has defined. A simplified support call record in JSON looks like this (actual output includes additional metadata and word-level timestamps):
{
"result": {
"transcription": {
"full_transcript": "I can't log into my account. I tried the password reset twice.",
"utterances": [
{
"speaker": 0,
"transcript": "I can't log into my account.",
"start": 0.5,
"end": 2.1
},
{
"speaker": 1,
"transcript": "I tried the password reset twice.",
"start": 2.4,
"end": 4.0
}
]
},
"audio_to_llm": {
"results": [
{
"prompt": "Classify: intent, sentiment, resolution_status, product_mention, feature_request, churn_risk""Extract key information from this support call",
"results": {
"response": {
"intent""primary_topic": "account_access_issue",
"sentiment""customer_sentiment": "negative",
"resolution_status""issue_status": "unresolved",
"product_mention""mentioned_feature": "authentication_system",
"feature_request": "passwordless_login",
"churn_risk": "high"
}
}
}
]
}
}
}
The structured output includes all enrichment fields configured in the API request, enabling direct CRM integration without manual data entry.
Essential call extraction fields
These are the JSON keys every contact center should configure as a baseline extraction schema:
| Field |
Description |
Why it matters |
intent |
Primary reason for the call |
Enables topic clustering and trend analysis |
sentiment |
Positive, neutral, or negative |
Flags escalation risk, informs coaching |
resolution_status |
Resolved, unresolved, or escalated |
Enables first-call resolution tracking |
product_mention |
Specific product or feature referenced |
Supports product analytics and feedback loops |
feature_request |
Verbatim feature request language |
Captures customer voice for product teams |
churn_risk |
High, medium, or low based on signals |
Triggers customer success intervention |
Optimizing call data for machine processing
Not all call data extracts with equal reliability. Understanding what machines handle well versus what still requires human judgment prevents over-automation and the errors that follow from it.
Reliable automated data fields
The fields that automated extraction handles accurately and consistently are:
- Timestamps and call duration
- Speaker diarization and turn counts (async only, powered by speaker diarization)
- Named entity recognition for various entity types
- Sentiment polarity at the utterance and call level
- Resolution steps described in the transcript
Gladia's async benchmark shows on average 3x lower DER than alternatives.
Unautomatable call data points
Transcripts cannot capture some call context, requiring human input:
- Account tier validation: The transcript contains a name, not a subscription status, so CRM lookup is required.
- Priority classification: Business impact is often implied rather than stated explicitly.
- Resolution verification: The transcript captures what the agent said, not whether it fixed the issue 24 hours later.
- CRM relationship context: Background the caller did not mention during the call.
The practical design is a mixed system: automated extraction fills the majority of the call record, and agents handle exceptions flagged by confidence thresholds or missing required fields.
Minimizing automation errors in calls
The most reliable way for your team to reduce LLM extraction errors is to improve the transcript, not the prompt. A high-WER transcript produces ambiguous input that causes the LLM to guess, and a garbled phrase can turn a valid intent into a nonsensical classification. Teams using Gladia's CCaaS infrastructure can benefit from managed API services that eliminate self-hosting overhead. The accuracy improvement compounds directly into LLM extraction reliability.
Troubleshooting support call data quality
Protocol for customer ID refusal
When a caller declines to provide a customer ID or account number, capture all non-identity fields from the transcript regardless. Intent, sentiment, resolution steps, and feature requests are extractable without a linked account. Flag the record as identity-unverified in the CRM rather than dropping the call from analytics entirely, because these calls often contain valid product signals even without a linked account.
How accurate is automated sentiment detection?
Gladia's sentiment analysis classifies sentiment polarity from transcribed text. Sentiment is derived from transcript text, not vocal emotion. The model does not analyze raw audio features like pitch, tone, or speaking rate.
Identifying customer self-service attempts
Named entity recognition and keyword extraction pull explicit mentions of documentation, knowledge base articles, chatbot interactions, and specific troubleshooting steps from the transcript. When a caller says "I already tried the reset link in your help docs and it did not work," that phrase contains a self-service failure signal that an agent will almost never enter into a CRM field. Aggregating these phrases across thousands of calls maps the UX dead ends that drive avoidable call volume.
Routing call data into your CRM and product analytics
Routing Gladia's JSON output directly to Salesforce or HubSpot via webhook closes the loop between the call and the CRM record without agent input for standard fields. The structured field set, intent, sentiment, resolution status, product mention, feature request, and churn risk, maps cleanly to custom CRM objects. Agents receive a draft record populated from the transcript and confirm or correct exceptions rather than building the record from memory. The same structured fields feed product analytics in parallel: intent and product mention cluster into roadmap signals, while churn risk routes to customer success.
For teams processing multilingual call volume across European or Asian markets, data residency matters as much as extraction accuracy. On Growth and Enterprise plans, Gladia does not use customer audio to retrain models, with no opt-out required. Full details are in Gladia's compliance documentation, covering GDPR, HIPAA, SOC 2 Type II, and ISO 27001.
The contact center that treats support audio as a product intelligence layer, not just a customer service metric, closes the loop between what users experience and what gets built next. Start with 10 free hours and run the JSON extraction pipeline on your own call audio to see how it handles multilingual input, code-switching, and entity extraction in your specific production conditions.
FAQs
What are the core data fields every support call should capture?
Essential JSON fields include intent, sentiment, resolution status, product mention, feature request, and churn risk. These can be extracted from a clean transcript using structured LLM prompts via Gladia's audio-to-LLM feature, which allows you to define custom extraction schemas.
Does automated sentiment analysis detect emotional tone from voice?
No. Sentiment is derived from transcript text, not vocal emotion. Text-based sentiment analysis and acoustic emotion detection, which analyzes raw audio features like pitch and speaking rate, are separate technical approaches. Gladia provides the former.
Is PII redaction enabled by default in Gladia's transcription?
No, PII redaction is an optional feature that must be explicitly configured in the API config by passing an entity type preset such as GDPR, HIPAA_SAFE_HARBOR, or PCI, as documented in Gladia's PII redaction docs. It does not activate automatically on any plan.
Does Gladia use my call audio to train its models?
On Growth and Enterprise plans, Gladia never uses customer data for model training and no opt-out action is required. On the Starter plan, customer data may be used for model training by default.
Key terms glossary
After-call work (ACW): The tasks an agent completes after a call ends, including CRM entry, disposition coding, and follow-up scheduling.
Word error rate (WER): The percentage of words in a transcript that differ from the ground truth. WER measures transcription accuracy and sets the quality ceiling for all downstream systems that read from the transcript.
Diarization error rate (DER): The percentage of audio incorrectly attributed to the wrong speaker. Speaker diarization assigns utterances to individual speakers in a multi-speaker recording.
Code-switching: The phenomenon where a speaker alternates between two or more languages within a single conversation. Systems not designed for code-switching may struggle with language detection and transcription accuracy in multilingual environments.
Audio-to-LLM pipeline: The end-to-end process of routing audio through an ASR transcription layer and then applying a structured LLM prompt to extract specific data fields from the resulting transcript. Gladia's audio-to-LLM feature handles both steps within a single API call.
Text-based sentiment analysis: An NLP method that classifies sentiment polarity expressed in transcribed text by analyzing word choice and context. Sentiment is derived from the transcript, not from raw audio features like pitch or tone.
First-call resolution (FCR): A contact center metric measuring the percentage of calls resolved without a repeat contact. Higher FCR correlates with lower ACW costs and higher customer satisfaction.
Async (batch) transcription: A transcription workflow where audio is processed after recording rather than in real time. Async workflows allow full-context processing, which improves accuracy, speaker attribution, and multilingual consistency compared to streaming transcription.
