Understand Every Call with Post-Call Analysis

4 min read

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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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