- Home
- AI Conversations
- Post-Call Analysis
Call sentiment analysisEvery call ends with clean, structured data.
Define the fields that matter to you — budget, timeline, property type, callback preference — and TalkerIQ extracts them from every AI conversation automatically. Plus post-call analysis of sentiment, outcome and a summary, all synced to your CRM without anyone typing a word.
Post-call analysis · 30 seconds after hang-up
Your extraction fields
- Budget
- $400k–$450k
- Timeline
- Within 60 days
- Decision maker
- Yes
- Property type
- Condo
- Current agent
- Not mentioned
Summary
Pre-approved buyer relocating for work, wants a two-bed condo near downtown. Booked a viewing tour for Saturday; prefers texts over calls.
What is post-call analysis?
Post-call analysis is the automatic processing of a phone conversation after it ends, turning what was said into data. TalkerIQ transcribes each AI call, tags the outcome, scores caller sentiment as positive, neutral or negative, writes a summary and extracts the custom fields you define, such as budget or timeline, straight into your CRM.
- Sentiment
- Positive, neutral or negative on every AI call
- Outcome tags
- Connected, voicemail, transferred, appointment booked
- Custom fields
- Text, number, yes/no, single choice
- CRM sync
- HubSpot, GoHighLevel, Zoho, Pipedrive, Follow Up Boss
- Triggers
- Smart retries, campaign paths, webhooks
Sound familiar?
We made thousands of calls last month. If you asked me how many of those leads were ready to buy, I couldn't tell you without listening to every one.
Calls are full of the answers you need to prioritise. But a recording isn't data — until someone turns what was said into fields you can filter.
The good stuff is buried in transcripts
Budget, timing, objections — all said out loud, none of it in a field.
CRM fields stay empty
Reps don't fill them in, or fill them in differently, so reports can't be trusted.
Bad calls hide in the volume
At campaign scale, you can't spot the conversations that went wrong.
Follow-up ignores what was said
Hot leads and cold ones get the same next step because nothing reads the call.
How it works
From conversation to database, automatically: how does post-call analysis work?
Your reps stop taking notes. Your data gets better anyway.
Define your fields
In the agent builder, add fields with a name, a type and a plain-language description of what to listen for.
Your team · setupThe AI has the conversation
It can be scripted to ask for the fields that matter most, so gaps stay rare.
AI agentThe call is analysed
Transcript, summary, outcome tag, sentiment score and every field value — or left empty if never mentioned.
AIIt syncs to your CRM
Mapped to your fields on the contact's timeline, minutes after the call ends.
AutomaticIt drives the next step
Outcomes feed smart retries, campaign paths, analytics and webhooks.
AI + automation
Extract
Ask for the fields. Get the fields.
Define exactly what to capture: text, numbers, yes/no or single-choice picklists. "What's their budget?" "Are they the decision maker?" The AI fills each field from the conversation itself — and leaves it empty rather than guessing when the caller never said.
- Four field types: text, number, boolean and single choice
- Plain-language descriptions instead of rules or regex
- Change a definition any time — new calls pick it up immediately
Score
See how every call went at a glance: what is call sentiment analysis?
Call sentiment analysis reads the tone of the conversation and labels it. Every AI call is scored positive, neutral or negative and tagged with its outcome, using the same call disposition codes as your reps — connected, voicemail, transferred, appointment booked. Spot unhappy customers instantly, and see sentiment trends across whole campaigns.
- Sentiment scoring on every AI call
- Outcome tags alongside summary, transcript and recording
- Campaign analytics that segment leads by what they actually said
Call notes · written for you
Summary
Existing customer; water heater is leaking again two weeks after the repair. Wants a technician before Friday and asked whether the visit is covered by the warranty.
Next steps
- Confirm warranty coverage
- Call back before 5 PM today
- Offer a Thursday visit
Act
Data that decides the next step.
Extracted data lands on the contact in your CRM, mapped to your fields. Outcomes feed the rest of TalkerIQ — smart call retries plan follow-ups from them, high-intent leads move to a faster path, outreach pauses after a negative conversation — and webhooks notify your own systems.
- CRM sync to HubSpot, GoHighLevel, Zoho, Pipedrive and Follow Up Boss
- AI smart retries and campaign paths driven by outcomes
- Every channel — inbound, outbound campaign and website widget calls
One lead, five days, zero dropped follow-ups
- Rep calls · no answerVoicemail dropped in one click; disposition loggedDay 1 · 10:04
- Follow-up text goes out"Sorry we missed you — reply with a good time"Day 1 · 10:05
- AI retries at the best timeSmart retry picks evening; local number shownDay 2 · 18:20
- Lead replies by textAI SMS agent answers pricing questions instantlyDay 3 · 08:12
- Meeting bookedOn the rep's calendar, CRM deal stage updatedDay 3 · 08:15
- Rep joins with full contextCall recordings, texts and AI summary in one timelineDay 5
Compare
Manual QA, AI call summary or post-call analysis: what's the difference?
All three help you learn from calls. Only post-call analysis turns every call into fields you can filter.
| Manual QA review | AI call summary | Post-call analysis | |
|---|---|---|---|
| Calls covered | A sample | Every call | Every AI call |
| Output | Scorecard notes | Readable recap and next steps | Structured fields, outcome tag and sentiment score |
| Filter and report on it | Hard | Search the text | Yes — by field, outcome or sentiment |
| Triggers the next step | No | Next steps for a person | Yes — retries, campaign paths, webhooks |
| Best for | Quality and compliance spot checks | Follow-up by your team | Prioritising leads and running campaigns at scale |
A campaign morning
What does post-call analysis do for a busy sales team?
A real-estate brokerage running an AI campaign to old buyer enquiries. Names are illustrative.
- Fields defined
Broker Elena adds four fields: buying timeline, pre-approved (yes/no), budget, and property type (house, condo, townhouse).
- The AI campaign runs
The AI outbound calling agent reaches past enquiries and asks the questions naturally.
- A call becomes a record
One caller says she's pre-approved and wants a condo within three months. All four fields fill, sentiment is positive, outcome "appointment booked".
- Best leads first
Elena filters to "pre-approved = yes" and timeline under six months, and her agents call those leads first.
- The rest follow up on their own
Negative-sentiment calls pause outreach; "not ready" leads get a gentle text in a month. See real estate phone systems.
What changes
What teams notice once calls become data.
manual note-taking
Fields, summary and outcome are filled for you on every AI call.
field filled the same way
Consistent data you can filter, report on and trust.
to CRM
Contact records update shortly after the call ends.
follow-up
Next steps follow what the lead actually said, not a fixed sequence.
What's included
Everything in post-call analysis.
Custom field extraction
Text, numbers, yes/no and picklists, filled from the conversation.
Sentiment scoring
Positive, neutral or negative on every call, with trends in analytics.
Outcome tags
Connected, voicemail, transferred, appointment booked.
Summaries & transcripts
A concise summary, full searchable transcript and the recording.
Automatic CRM sync
Mapped to your fields on the contact's timeline.
Webhooks
Send outcomes and extracted data to your own systems.
Drives follow-up
Feeds AI smart retries and campaign branching.
Every channel
Inbound, outbound campaign and website widget calls.
No guessing
Fields the caller never mentioned stay empty.
People and AI, one phone system
The AI fills the fields. Your team works the best leads first.
Post-call analysis runs on every AI voice agent and website voice widget call, as part of TalkerIQ AI voice and SMS agents. See what each plan includes on TalkerIQ pricing.
Decides what matters and acts on it
- Defines the fields and what counts as high intent
- Gets hot leads routed to them faster
- Reviews negative calls before they become churn
Listens, extracts, scores and syncs
- Fills every field from what the caller said
- Scores sentiment and tags the outcome
- Updates the CRM and triggers the next step
AI qualifies · your team closes
- Name
- Priya N.
- Need
- 40 seats
- When
- Next month
- Score
- Hot
FAQ
Post-call analysis questions
Still unsure? Talk to our team.
Reviewed by the TalkerIQ product team · Updated
What does TalkerIQ produce after each call?
A call summary, the full transcript and recording, an outcome tag (connected, voicemail, transferred, appointment booked), a sentiment score, and the values of every custom extraction field you've defined.
What kinds of custom fields can I extract?
Text, numbers, booleans and single-choice fields — for example budget range, preferred callback time, current provider, or whether the caller is the decision maker. You describe the field in plain language and the AI fills it from the conversation.
How do I define what gets extracted?
In the agent builder, add extraction fields with a name, type and a plain-language description of what to look for. Every call that agent handles then produces those fields. Change the definition any time and new calls pick it up immediately.
What if the caller never mentions a field?
Fields the conversation didn't cover stay empty rather than being guessed. You see exactly what was learned on each call, and the AI can be scripted to ask for the fields that matter most.
Does the extracted data reach my CRM automatically?
Yes. TalkerIQ maps extracted fields and call activity to connected CRMs — HubSpot, GoHighLevel, Zoho, Pipedrive and Follow Up Boss — so the contact record updates without manual entry.
Can post-call analysis trigger follow-up actions?
Yes. Outcomes and extracted data feed TalkerIQ's campaign engine — for example routing a high-intent lead to a faster follow-up path or pausing outreach after a negative conversation — and can fire webhooks to your own systems.
How accurate is call sentiment analysis?
Call sentiment analysis is a reliable guide to how a conversation felt overall, but it is a label, not a verdict. TalkerIQ shows the sentiment score next to the summary, transcript and recording, so your team can check any call that matters before acting on it.
What is conversation intelligence?
Conversation intelligence is software that analyses calls to find patterns: what customers ask, how they feel and what leads to a sale. TalkerIQ post-call analysis provides the building blocks — outcome tags, sentiment, summaries and custom fields on every AI call — feeding campaign analytics and your CRM.
Keep exploring
Turn your next call into clean data.
Book a 30-minute walkthrough. Tell us the fields you need and we'll show them filled from a live test call.