AI-powered Call Analysis Tool for Call Centers
AI Powered call analysis tool helps companies analyse the calls, extract insights and improve their product and service. Build your own tool with this step-by-step guide using OpenAI & Cursor
In today's fast-paced world, customer service is at the heart of any successful business. As companies strive to provide exceptional customer experiences, the role of call centres has become more critical than ever.
Despite the rise of AI technologies, the human connection remains irreplaceable, and customers often expect a personal touch in their interactions.
As a person who is leading a team that calls customers, I frequently face challenges in understanding what truly happens during call centre interactions. Many insights are embedded in the conversations between executives and clients, but they often remain undiscovered. This lack of visibility raises numerous questions about the quality of the conversations: Was the client genuinely uninterested, or did the agent fail to deliver a convincing pitch?
Analyzing every call manually is impractical, given the sheer volume of interactions, leading to missed opportunities for improvement and action.
Managing and analyzing thousands of call centre interactions daily is no small feat. Manual reviews are time-consuming, error-prone, and often miss nuanced customer feedback.
I have been facing this problem as well, and I wanted to explore what I can do.
The Solution: An AI-powered Call-analysing tool
I did a proof of concept for a small application that:
Analyzes the call
Understands the language and transcribes the call
Converts the text to English
Extracts insights from the conversation
Creates a report detailing the call's outcome, feedback for counsellors, etc.
Check out the video below:
If you haven’t read my previous articles, please check them out below:
Key Features and Benefits
Time-Saving: Automating the transcription and analysis process drastically reduces the time spent on manual reviews.
Actionable Insights: Provides clear, actionable feedback to improve agent performance and enhance customer satisfaction.
Scalability: Capable of handling large volumes of calls, making it suitable for both small businesses and large enterprises.
Building the Call Analyzer Application with Cursor
I developed this application using Cursor with the help of Cursor Agent. If you haven't heard about Cursor, you can check out my blog for more details:
I built the application using a couple of prompts. I'm sharing the prompt here to help you build your version. However, keep in mind that you may need to make some improvements, as Cursor can make errors.
This isn’t the perfect prompt, you can follow it up with other prompts like build a beautiful front-end app , add more variables etc..
I enhance the prompt and then ask it to build it. I test the application as I build it and guide the Cursor Agent to build the application accordingly.
I want to build a voice analysis tool that can analyze the conversation and get insights out of conversations.
1. The input will be a file
2. I want you to know the language of the conversation like English, Hindi, Telugu
3. I want you to transcribe the conversation of both the speakers.
4. I want you to convert the text into English.
5. What was the conversation about?
6. What did the speakers say?
7. What was the outcome of the conversations? Was the speaker interetested in Studyabroad or not?
In the file, there will be two speakers. One voice will be of the client and the other will be of the counsellor.
Bucket the outcome of the conversation into different categories like:
1. Interested in Studyabroad
2. Not interested in Studyabroad
3. Speaking in Hindi
4. Others
Technology stack:
1. Python - Flask
2. OpenAI Whisper
3. OpenAI GPT-4o-mini ( latest model)
For Whisper - Reference Code:
```
from openai import OpenAI
client = OpenAI()
audio_file = open("/path/to/file/speech.mp3", "rb")
transcription = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file,
response_format="text"
)
print(transcription.text)
```
For GPT-4o-mini - Reference Code:
```
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "developer",
"content": "You extract email addresses into JSON data."
},
{
"role": "user",
"content": "Feeling stuck? Send a message to help@mycompany.com."
}
],
response_format={
"type": "json_schema",
"json_schema": {
"name": "email_schema",
"schema": {
"type": "object",
"properties": {
"email": {
"description": "The email address that appears in the input",
"type": "string"
},
"additionalProperties": False
}
}
}
}
)
print(response.choices[0].message.content);
```
Tech Stack
Cursor Agent: An advanced Coding Agent that can build you applications with Cursor
OpenAI's Whisper API: For transcribing audio to text.
OpenAI's GPT-4o-mini: For extracting key insights and summarizing information.
Current Challenges and Solutions
While the tool looks promising, it's important to acknowledge its current limitations:
Accuracy and Language Processing
The tool occasionally struggles with transcription accuracy, particularly with diverse accents and industry-specific terminology. I can experiment with multiple other models to improve accuracy.
Cost Considerations
Using OpenAI's APIs comes at a price - specifically, $0.006 per minute of processed audio. For large call centres, this can represent a significant operational expense. One can explore multiple other open-source models to bring down the cost.
You can check out the pricing in the below link.
https://openai.com/api/pricing/
Conclusion
Whether you're managing a small team or a large call centre, the ability to systematically analyze and improve customer interactions is becoming increasingly crucial. This tool represents a step toward more efficient, data-driven customer service management, helping teams improve their performance while saving valuable time and resources.
If you think, a product like this would help you improve your operations, let me know and I would be happy to work on making this a product.