Understand Every Call with Post-Call Analysis
4 min read
Turn Every Conversation into Insightful Action
What Is Post-Call Analysis?
The new Post-Call Analysis feature on AICaller.io is a powerful tool that lets businesses go beyond basic analytics. It doesn’t just show if a lead answered or not—it lets you ask your own questions about the call, and get real answers.
How? After each AI call, AICaller automatically runs the call transcript through ChatGPT, based on your custom prompts, and returns clean, actionable responses.
This is no generic post-call tagging—it’s hyper-specific intelligence tailored to your own use case.
✅ Built for sales teams, marketers, agencies, and support teams who want data-driven decision-making without listening to every call.
How It Works (Backend Explained)
Here’s a step-by-step breakdown of how the feature operates:
1. You Define the Questions
In your dashboard, you can input custom questions relevant to your campaign or call script. For example:
“Does the user want to buy or sell?”
“What issue did the customer report?”
“What location is the user interested in?”
Each question is saved with:
A Column Name (for exporting or syncing to CRM)
A Result Type (e.g. text, number, yes/no)
2. AI Call Happens
Your AI voice agent completes the call with a prospect or lead. During the call, AICaller records:
The full call transcript
Metadata like call duration, response, and sentiment (if enabled)
3. Post-Call ChatGPT Prompting
Immediately after the call ends:
The transcript is sent to ChatGPT
For each question you set up, GPT is prompted in a structured format:
Prompt to GPT:
Here is the transcript of a sales call. Based on it, answer: “Does the user want to buy or sell?”
GPT then returns a clean text result like:
→ “Buy” or “Sell”
This happens in seconds, at scale, with zero human input.
4. Answers Get Stored in Your Dashboard
Each answer gets:
Stored under its custom column name
Displayed in your analytics dashboard
Available for CSV export, CRM sync, or automated actions
This makes it incredibly powerful for:
Tagging lead intent
Segmenting based on interest
Triggering workflows
Retargeting or re-calling high-potential leads
Real-Life Example: Real Estate Use Case
Problem: A used car company needed to know:
Is the caller looking to buy or sell car?
What city or location are they from?
How old is the car? (also as preference)
Old Method:
Agents had to listen to 100s of calls manually and label them.
Now with Post-Call Analysis:
They entered those 3 questions into AICaller once. From then on:
GPT analyzed every call transcript
Returned answers like:
Buy
Location: San Diego
“2 years”
All auto-tagged under the contact’s profile
Impact:
✅ Saved 20+ man-hours/week
✅ Improved lead response time
✅ Segmented campaigns with 40% better targeting
🔍 Why This Changes Everything
This isn’t a basic "summary" feature. This is interactive post-call intelligence that turns every AI conversation into structured business data.
Imagine:
A SaaS company tagging calls by feature interest
A recruitment firm extracting desired job role and salary range
An eCommerce brand capturing objections for abandoned calls
The applications are endless—and you define the intelligence.
📌 Related Reads:
💼 Who Should Use This?
This is ideal for:
Sales Teams – Understand objections, qualify leads
Real Estate Firms – Extract buyer/seller intent, location
Agencies – Automate reporting for multiple clients
Support Teams – Capture reason for calls, urgency, satisfaction
If your team relies on phone conversations to convert or support—this is your shortcut to smart scaling.
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