What are conversation analytics (and how to use them with AssemblyAI)

Every customer call, sales meeting, and support interaction your company handles generates insight. The problem? Most companies analyze less than 5% of their conversation data. The other 95% disappears into storage, never reviewed, never learned from.

Conversation analytics is the technology that changes this equation entirely.

Instead of randomly sampling a handful of calls for QA, conversation analytics automatically transcribes, analyzes, and extracts actionable insights from voice conversations at scale. Every call. Every meeting. Every interaction. And it does it in minutes, not weeks.

This guide covers what conversation analytics actually involves, which features matter most for different use cases, and how to implement a complete conversation analytics pipeline with AssemblyAI's API. Whether you're building a conversation intelligence platform from scratch or adding analytics capabilities to an existing product, you'll walk away with working code and a clear architecture.

What are conversation analytics?

Conversation analytics (also called conversation intelligence or CI) is AI-powered technology that automatically transcribes and analyzes voice conversations to extract measurable business insights. It transforms unstructured audio—phone calls, meetings, sales demos, support tickets—into structured, searchable, actionable data.

The core capabilities include:

• Automatic transcription with speaker labels — converting audio to text and identifying who said what
• Sentiment analysis — detecting positive, negative, and neutral sentiment at the sentence level
• Topic detection — categorizing conversations into standardized topics automatically
• Entity extraction — pulling out names, organizations, locations, products, and other key data points
• Summarization — generating concise summaries, action items, and coaching notes
• Pattern recognition — identifying trends and anomalies across thousands of conversations

There's an important distinction worth making here. Conversation analytics analyzes human-to-human interactions—the calls and meetings that already happen in your business. Conversational AI (chatbots, voice assistants) creates automated conversations. Think of conversation analytics as the analysis layer that sits on top of your existing voice data, turning it into intelligence you can act on.

Why it matters now

The business impact of conversation analytics is well-documented. Companies using conversation intelligence platforms report 15% higher sales win rates, and a recent industry survey found that over 70% of companies saw measurable improvements in customer satisfaction after implementation. Teams that previously reviewed 1-3% of calls manually can now analyze 100% automatically—with a McKinsey report confirming that AI can push reviewed call coverage from 3% to 95%.

And the efficiency gains are just as significant: organizations report a 90% reduction in manual documentation tasks and 50% faster conversation review and analysis. These aren't incremental improvements. They're a fundamentally different way of operating.

Key features of a conversation analytics pipeline

A complete conversation analytics pipeline starts with accurate transcription and layers intelligence on top. Here's what each feature does and how to implement it with AssemblyAI's API.

Transcription with speaker diarization

This is the foundation. Without accurate transcription, nothing else works—your sentiment analysis, entity detection, and topic categorization are only as good as the words they're analyzing.

Speaker diarization identifies who said what in a conversation, separating speakers and labeling their turns. For conversation analytics, this is critical: you need to know whether the agent or the customer expressed frustration, or which participant raised a specific objection.

AssemblyAI's speaker diarization achieves a 2.9% error rate in identifying the number of speakers—the lowest in the industry. Here's how to enable it:

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    speaker_labels=True,
)

transcript = aai.Transcriber().transcribe("https://example.com/call.mp3", config)

for utterance in transcript.utterances:
    print(f"Speaker {utterance.speaker}: {utterance.text}")

The speech_models parameter selects Universal-3 Pro as the primary model with Universal-2 as the fallback. Universal-3 Pro delivers a 94.07% word accuracy rate on real-world audio—the highest among all providers, including OpenAI, Deepgram, Microsoft, and Amazon. That accuracy matters because every downstream analytics feature depends on getting the words right in the first place.

Sentiment analysis

Sentiment analysis scores each spoken sentence as POSITIVE, NEUTRAL, or NEGATIVE, along with a confidence score. This is critical for customer satisfaction tracking, escalation detection, and QA scoring at scale.

Instead of listening to calls to figure out where things went wrong, you can automatically flag conversations where sentiment drops sharply or where a customer expresses repeated frustration.

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    sentiment_analysis=True
)

transcript = aai.Transcriber().transcribe(audio_file, config)

for sentiment_result in transcript.sentiment_analysis:
    print(sentiment_result.text)
    print(sentiment_result.sentiment)  # POSITIVE, NEUTRAL, or
NEGATIVE
    print(sentiment_result.confidence)
    print(f"Timestamp: {sentiment_result.start} - 
{sentiment_result.end}")

But here's where it gets interesting. You can combine sentiment analysis with speaker diarization to track per-speaker sentiment throughout a conversation. This means you can see exactly when a customer's sentiment shifted from neutral to negative, and correlate it with what the agent said immediately before.

config = aai.TranscriptionConfig(
    sentiment_analysis=True,
    speaker_labels=True
)

transcript = aai.Transcriber().transcribe(audio_file, config)

for sentiment_result in transcript.sentiment_analysis:
    print(f"Speaker {sentiment_result.speaker}:
{sentiment_result.sentiment}")
    print(f"  Text: {sentiment_result.text}")
    print(f"  Confidence: {sentiment_result.confidence}")

Each sentiment result includes the speaker field when diarization is enabled, so you can build dashboards that track agent versus customer sentiment separately—an essential capability for coaching and compliance monitoring.

Topic detection (IAB Taxonomy)

Topic detection automatically categorizes conversations using the IAB Content Taxonomy—a standardized language for content description that includes 698 comprehensive topics. This lets you understand what your conversations are actually about without building custom classifiers.

For contact centers, this means automatically routing conversations to the right team. For sales organizations, it means tracking which product features or competitive objections come up most frequently. For compliance teams, it means flagging conversations that touch on regulated topics.

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    iab_categories=True
)

transcript = aai.Transcriber().transcribe(audio_file, config)

# Get topic labels for specific parts of the transcript
for result in transcript.iab_categories.results:
    print(result.text)
    print(f"Timestamp: {result.timestamp.start} - {result.timestamp.end}")
    for label in result.labels:
        print(f"  {label.label} ({label.relevance})")

# Get overall topic summary for the entire audio
for topic, relevance in transcript.iab_categories.summary.items():
    print(f"Audio is {relevance * 100}% relevant to {topic}")

The output includes both segment-level topic labels (which parts of the conversation discussed which topics) and an overall summary of topic relevance across the entire audio file. This makes it straightforward to aggregate topic data across hundreds or thousands of conversations to identify trends.

Entity detection

Entity detection extracts structured data from conversations—names, organizations, locations, dates, medical information, financial data, and more. If someone mentions a company name, a product, a dollar amount, or an email address during a call, entity detection identifies and classifies it automatically.

This is how you connect conversation data to your CRM, automatically tag calls with relevant account information, or track competitor mentions across all customer interactions.

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    entity_detection=True
)

transcript = aai.Transcriber().transcribe(audio_file, config)

for entity in transcript.entities:
    print(entity.text)
    print(entity.entity_type)
    print(f"Timestamp: {entity.start} - {entity.end}")

Universal-3 Pro delivers the lowest missed entity rates on real-world audio across dates, locations, and medical terms compared to every major provider—including a 7.50% missed entity rate on dates and times versus 12.29% for OpenAI and 18.69% for Deepgram. When your analytics pipeline depends on correctly identifying who was mentioned, which product was discussed, or what dollar amount was quoted, entity accuracy directly impacts the quality of your insights.

Summarization via LLM Gateway

Raw transcripts are valuable, but most teams need summaries they can act on quickly: call summaries for CRMs, action items for follow-up, coaching notes for managers, compliance documentation for auditors.

AssemblyAI's LLM Gateway is a framework for applying large language models to spoken data. It unifies your Voice AI application stack across speech-to-text, speech understanding, and LLM-powered insights into one platform. You can generate customized summaries, extract action items, analyze call performance, or answer specific questions about conversations.

import requests
import time

base_url = "https://api.assemblyai.com"
headers = {"authorization": "YOUR_API_KEY"}

# Step 1: Transcribe the audio
data = {
    "audio_url": "https://assembly.ai/wildfires.mp3",
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": True
}

response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
transcript_id = response.json()['id']
polling_endpoint = base_url + "/v2/transcript/" + transcript_id

while True:
    transcription_result = requests.get(polling_endpoint,
headers=headers).json()
    if transcription_result['status'] == 'completed':
        break
    elif transcription_result['status'] == 'error':
        raise RuntimeError(f"Transcription failed: 
{transcription_result['error']}")
    else:
        time.sleep(3)

# Step 2: Summarize via LLM Gateway
prompt = "Provide a brief summary of the transcript in bullet point 
format."

llm_gateway_data = {
    "model": "claude-sonnet-4-6",
    "messages": [
        {"role": "user", "content": f"{prompt}\n\nTranscript:
{transcription_result['text']}"}
    ],
    "max_tokens": 1000
}

response = requests.post(
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
    headers=headers,
    json=llm_gateway_data
)

result = response.json()["choices"][0]["message"]["content"]
print(result)

The power of LLM Gateway is in its flexibility. You can change the prompt to extract whatever you need: highlight what went well and what didn't during a sales call, summarize only the pricing discussion, analyze agent compliance with a specific script, or generate coaching feedback. Since it works with multiple LLM providers including Claude, GPT, and Gemini, you can choose the model that best fits your specific needs.

Speaker identification

Default speaker diarization labels speakers as "Speaker A," "Speaker B," and so on. But for production conversation analytics, you often need to map those generic labels to real names or roles—so you can attribute speech to "Agent: Sarah Johnson" and "Customer" rather than abstract letters.

AssemblyAI's speaker identification feature handles this through the Speech Understanding API:

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    speaker_labels=True,
    speech_understanding={
        "request": {
            "speaker_identification": {
                "speaker_type": "role",
                "speakers": [
                    {"role": "Agent", "name": "Sarah Johnson"},
                    {"role": "Customer"}
                ]
            }
        }
    }
)

transcript = aai.Transcriber().transcribe("your_call.mp3", config)

for utterance in transcript.utterances:
    print(f"{utterance.speaker}: {utterance.text}")

This is especially useful in contact center environments where you always know the agent's identity but not the customer's. The model uses audio context to match speakers to the roles you've defined, eliminating the need for post-processing logic to figure out who's who.

Building a complete conversation analytics pipeline

The real power of conversation analytics comes from combining all of these features in a single API call. You don't need to make separate requests for transcription, sentiment, entities, and topics—enable everything at once and process the results together.

Here's a comprehensive configuration that creates a full analytics pipeline:

import assemblyai as aai
from assemblyai.types import SpeakerOptions

aai.settings.api_key = "YOUR_API_KEY"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    speaker_labels=True,
    speaker_options=SpeakerOptions(
        min_speakers_expected=2,
        max_speakers_expected=5
    ),
    sentiment_analysis=True,
    entity_detection=True,
    iab_categories=True,
    keyterms_prompt=[
        "Acme Corp", "Premium Support Plan",
        "account number", "case number",
    ],
)

transcript = aai.Transcriber().transcribe("your_call.mp3", config)

# Speaker summary
speakers = set(u.speaker for u in transcript.utterances)
print(f"Detected {len(speakers)} speakers")

# Sentiment breakdown
sentiments = [r.sentiment for r in transcript.sentiment_analysis]
print(f"Positive: {sentiments.count('POSITIVE')}")
print(f"Neutral: {sentiments.count('NEUTRAL')}")
print(f"Negative: {sentiments.count('NEGATIVE')}")

# Key entities
for entity in transcript.entities:
    if entity.entity_type in ["organization", "person_name",
"location"]:
        print(f"{entity.entity_type}: {entity.text}")

# Top topics
for topic, relevance in transcript.iab_categories.summary.items():
    if relevance > 0.5:
        print(f"Topic: {topic} ({relevance * 100:.1f}% relevant)")

A few things to note about this configuration:

• The speaker_options parameter lets you hint at the expected number of speakers, which improves diarization accuracy for known scenarios (like a two-person sales call versus a multi-party meeting).
• The keyterms_prompt parameter boosts recognition of domain-specific terms that matter for your analysis—company names, product names, internal terminology that general models might miss.
• All features run in a single API call, so you don't pay for multiple transcription passes or deal with synchronization between separate requests.

How to request a summary or sentiment analysis as part of the API call

One common question developers ask is how to request specific analytics features as part of a single API call. The answer is straightforward: you simply enable the features you need in your TranscriptionConfig and they all process in parallel.

For the built-in sentiment analysis model, set sentiment_analysis=True. For more nuanced or customized sentiment analysis—like detecting frustration versus anger, or scoring agent empathy—use LLM Gateway after transcription to apply LLM-powered analysis with custom prompts tailored to your exact requirements.

Both approaches can be part of the same workflow, giving you standardized sentiment scores for dashboards and aggregation plus rich, customized analysis for deeper investigation.

Integrating conversation analytics with business tools

Conversation analytics data is most valuable when it flows into the tools your teams already use. Here's how to connect AssemblyAI's output to common business analytics and operational systems.

Webhooks for production scale

For production deployments processing large volumes of calls, polling for results isn't practical. AssemblyAI supports webhooks that notify your system as soon as transcription and analysis are complete:

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    speaker_labels=True,
    sentiment_analysis=True,
    entity_detection=True,
    iab_categories=True,
    webhook_url="https://your-app.com/webhook/assemblyai"
)

transcript = aai.Transcriber().transcribe("your_call.mp3", config)

The webhook receives the complete transcript with all analytics results as a JSON payload, and you can process it immediately—pushing sentiment scores to your BI dashboard, entities to your CRM, topics to your routing engine, and summaries to your ticketing system. AssemblyAI supports up to 20,000 webhook POST requests per 5-minute window with 200+ concurrent transcriptions, so the system scales with your call volume.

Structured output for analytics platforms

The analytics results from AssemblyAI's API are structured JSON, which means they integrate naturally with data warehouses, BI tools, and analytics platforms. A typical integration flow looks like this:

1. Audio files arrive (from your call recording system, meeting platform, or phone system)
2. Submit to AssemblyAI with analytics features enabled
3. Receive structured results via webhook
4. Transform and load into your data warehouse (Snowflake, BigQuery, Redshift)
5. Visualize and analyze in your BI tool (Tableau, Looker, Power BI)

Because every data point includes timestamps, speaker labels, and confidence scores, you can build remarkably granular analytics—like tracking how customer sentiment changes minute-by-minute across all calls to your support team, or identifying which agent talk tracks correlate with positive outcomes.

Use cases by industry

Conversation analytics applies differently depending on the industry and use case. Here's how leading organizations are applying these capabilities.

Contact centers and customer service

Contact centers are the highest-volume use case for conversation analytics. The applications include:

• Automated QA scoring — Score 100% of calls instead of the traditional 1-2% manual sample. Combine sentiment analysis, script compliance checking, and entity detection to generate objective quality scores for every interaction.
• Compliance monitoring — Automatically flag calls where required disclosures were missed or where agents discussed regulated topics without proper disclaimers.
• Agent coaching — Use per-speaker sentiment data to identify moments where agents handled difficult situations well (or poorly). Generate specific, timestamped coaching notes instead of vague feedback.
• Escalation prediction — Track sentiment trajectories in real time or post-call to identify customers at risk of churn or escalation before they reach a breaking point.

For contact center deployments, transcription accuracy is foundational. With a 2.9% speaker diarization error rate—the lowest in the industry—AssemblyAI ensures that agent and customer speech is correctly attributed, which directly impacts the reliability of every downstream metric.

Sales teams and revenue operations

Sales organizations use conversation analytics for:

• Deal intelligence — Automatically extract competitor mentions, pricing discussions, and objection patterns from sales calls. Feed this data into your CRM to give account executives and managers real-time visibility into deal health.
• Win/loss analysis — Compare conversation patterns across won and lost deals to identify the talk tracks, objection responses, and discovery questions that correlate with closed-won outcomes.
• Coaching at scale — Analyze talk-to-listen ratios, question frequency, and sentiment patterns across your entire sales team. Identify top performers' behaviors and replicate them through targeted coaching.
• Forecast accuracy — Use entity detection and topic analysis to independently verify deal stage progression based on what's actually discussed in calls, rather than relying solely on rep-reported pipeline updates.

Meeting intelligence

For meeting-heavy organizations, conversation analytics transforms meetings from ephemeral events into searchable, structured assets:

• Automated summaries and action items — Generate meeting summaries and extract action items via LLM Gateway immediately after each meeting ends.
• Searchable meeting archives — Transcribe and index every meeting so teams can search across months of meetings for specific topics, decisions, or discussions.
• Participation analysis — Track speaking time distribution across meetings to identify who's contributing, who's being talked over, and whether meetings are genuinely collaborative.

Healthcare

Healthcare organizations apply conversation analytics to clinical documentation and quality improvement:

• Clinical documentation — Transcribe doctor-patient encounters and generate structured clinical notes. AssemblyAI's Medical Mode delivers 18% improvement in word accuracy on medical audio, with the lowest missed entity rates on drug names, dosages, and diagnoses.
• Doctor-patient speaker separation — Speaker diarization accurately separates provider, patient, and staff voices across the full visit.
• Quality measurement — Analyze patient interactions to track adherence to clinical guidelines, patient education delivery, and communication quality.

AssemblyAI enables covered entities and their business associates subject to HIPAA to use the AssemblyAI services to process protected health information (PHI). AssemblyAI offers a Business Associate Addendum (BAA).

Media and content

• Speaker attribution — Accurately identify and label speakers in interviews, podcasts, and broadcasts for content production and compliance.
• Content analytics — Use topic detection to automatically categorize and tag audio/video content for discovery and recommendation engines.
• Brand monitoring — Track mentions of brands, products, and competitors across media content at scale using entity detection.

Why accuracy is the foundation of conversation analytics

If the words are wrong, the analytics are wrong too.

This sounds obvious, but it's the single most important principle in conversation analytics. Every feature in your pipeline—sentiment analysis, entity detection, topic categorization, summarization—operates on the transcript. If the transcript says "I'm definitely canceling" when the customer actually said "I'm definitely not canceling," your sentiment analysis will flag the wrong emotion, your churn prediction will fire incorrectly, and your agent coaching will give the wrong feedback.

This is why transcription accuracy isn't just a "nice to have" spec to compare on a features page. It's the foundation that determines whether your entire conversation analytics system produces reliable insights or expensive noise.

Universal-3 Pro delivers best-in-class accuracy with a 94.07% word accuracy rate on real-world audio. That lead over competitors matters most on the hard cases—accented speech, noisy environments, overlapping speakers, domain-specific terminology—exactly the conditions where production conversation data actually lives. It's also optimized for real-world entity recognition, with the lowest missed entity rates on dates, times, locations, and medical terms compared to every major provider.

AssemblyAI processes millions of hours of audio without outages, backed by infrastructure that delivers 99.9% uptime with SOC 2 Type 2 and ISO 27001 compliance. When your conversation analytics pipeline is processing every call in your contact center, reliability isn't optional.

Getting started with conversation analytics

You need three things to start building conversation analytics with AssemblyAI:

1. An API key — sign up for free (no credit card required)
2. An audio file or URL — any recorded call, meeting, or conversation
3. A few lines of code — install the Python SDK with pip install assemblyai

From there, you can have a working analytics pipeline in under 10 minutes:

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    speaker_labels=True,
    sentiment_analysis=True,
    entity_detection=True,
    iab_categories=True,
)

transcript = aai.Transcriber().transcribe("your_audio.mp3", config)

# You now have: full transcript, speaker labels, sentiment scores,
# detected entities, and topic categories — all from one API call.
print(f"Transcript: {transcript.text[:200]}...")
print(f"Speakers: {len(set(u.speaker for u in transcript.utterances))}")
print(f"Sentiment results: {len(transcript.sentiment_analysis)}")
print(f"Entities found: {len(transcript.entities)}")

For production scale, use webhooks instead of polling, and take advantage of AssemblyAI's unlimited concurrency with support for 200+ concurrent transcriptions and 20,000 webhook requests per 5-minute window. The API scales with your volume without requiring infrastructure changes on your side.

AssemblyAI offers usage-based pricing with no minimum commitments or annual contracts. You pay per second of audio processed, with all speech understanding features included—sentiment analysis, entity detection, topic detection, and speaker diarization don't cost extra on top of the transcription.

The future of conversation analytics

Conversation analytics is shifting from post-call analysis to real-time insight and action. The next wave isn't just about understanding what happened in a conversation after it ends—it's about shaping conversations as they happen.

Real-time streaming transcription with sub-300ms latency already makes live agent coaching possible. Imagine a system that detects dropping customer sentiment mid-call and surfaces a coaching prompt to the agent in real time. Or one that recognizes when a customer mentions a competitor and instantly pulls up the relevant competitive battlecard.

Voice agents with real-time conversation control are the next frontier. AssemblyAI's Voice Agent API already combines industry-leading speech understanding with LLM reasoning and voice generation in a single WebSocket connection. As these systems mature, the line between "analyzing conversations" and "participating in them intelligently" will blur.

But the foundation remains the same: accurate speech-to-text, reliable speaker identification, and structured intelligence extraction. Get the fundamentals right, and you're positioned for whatever comes next.

Ready to build conversation analytics into your product? Get started with a free API key and have a working pipeline running in minutes. No credit card required.

Frequently asked questions

What are conversation analytics and how do I use them?

Conversation analytics is AI-powered technology that automatically transcribes and analyzes voice conversations to extract measurable business insights. It combines speech-to-text transcription with speaker diarization, sentiment analysis, topic detection, entity extraction, and summarization to transform unstructured audio into structured, actionable data. To use conversation analytics with AssemblyAI, sign up for a free API key, install the Python SDK, and enable the features you need in your TranscriptionConfig—all features can run in a single API call on any audio file or URL.

How do I analyze tone and sentiment of customer calls?

With AssemblyAI, you analyze tone and sentiment by enabling sentiment_analysis=True in your transcription config. The model scores each spoken sentence as POSITIVE, NEUTRAL, or NEGATIVE with a confidence score. To attribute sentiment to specific speakers, enable speaker_labels=True alongside sentiment analysis—each result will include a speaker field so you can track agent versus customer sentiment separately. For more nuanced analysis (like detecting frustration versus anger, or scoring agent empathy), use LLM Gateway to apply custom prompts to the transcript.

Can conversation analytics combine speaker diarization with sentiment analysis?

Yes. AssemblyAI lets you enable both speaker_labels and sentiment_analysis in the same API request. When both are enabled, each sentiment analysis result includes a speaker field that tells you which speaker expressed that sentiment. This lets you build per-speaker sentiment tracking, compare agent versus customer sentiment trajectories, and identify exactly when and why a customer's tone changed during a conversation.

How do I integrate transcript analysis with business analytics tools?

AssemblyAI's API returns structured JSON results including timestamped sentiment scores, classified entities, topic labels, and speaker-attributed utterances. For production integration, use webhooks to receive results as soon as processing completes, then transform and load the data into your data warehouse (Snowflake, BigQuery, Redshift) for visualization in BI tools like Tableau, Looker, or Power BI. The structured format maps directly to database schemas, making it straightforward to build dashboards tracking sentiment trends, topic distributions, entity frequencies, and agent performance metrics.

What's the difference between conversation analytics and conversational AI?

Conversation analytics analyzes human-to-human interactions that already occur in your business—phone calls, meetings, support interactions—to extract insights and patterns. Conversational AI (chatbots, voice assistants, voice agents) creates automated conversations. They serve different purposes: conversation analytics is the analysis layer that turns existing voice data into intelligence, while conversational AI is the interaction layer that automates conversations directly. Many organizations use both, with conversation analytics monitoring the quality of both human and AI-powered interactions.

How accurate does speech-to-text need to be for reliable conversation analytics?

Accuracy is the single most critical factor for conversation analytics because every downstream feature—sentiment analysis, entity detection, topic categorization, summarization—operates on the transcript. If the transcript is wrong, the analytics are wrong. AssemblyAI's Universal-3 Pro delivers a 94.07% word accuracy rate, with the lowest missed entity rates on real-world audio for dates, locations, and medical terms. This accuracy difference matters most on production audio with background noise, accents, overlapping speakers, and domain-specific terminology—the exact conditions where conversation analytics data lives.