“From prediction to prevention — how I used GenAI to take customer churn analysis to the next level.”

Project Overview

In this project, I combined traditional AI with the power of Generative AI to not only predict customer churn, but also to understand it and prevent it.

This idea builds upon a previous AI project where I trained a machine learning model to predict which customers are at risk of leaving a business. Now, I’ve added GenAI capabilities to make those predictions explainable, actionable, and interactive.

Problem Statement

Predicting that a customer might leave is helpful — but what should we do about it? How do we explain churn to business teams, and recommend actions to retain valuable customers?

GenAI to the Rescue

In this capstone project, I used Generative AI to turn raw churn predictions into an intelligent assistant. Here’s what it can do:

1. Explain Churn Predictions

Using few-shot prompting, the assistant can describe in natural language why a specific customer is at risk of churning.

Example prompt:
“Why might this customer churn? Explain in plain English.”

2. Generate Structured Retention Plans

With structured output (JSON mode), it generates:

Churn risk level
Recommended retention offer
Best communication channel
Suggested follow-up actions

jsonCopyEdit{
  "risk_level": "High",
  "offer": "20% off for 3 months",
  "channel": "Email",
  "next_step": "Follow-up call in 3 days"
}

3. Answer Business Questions with RAG

Using Retrieval-Augmented Generation (RAG), the assistant can answer real questions like:

“Why did churn spike in March?”
“What policies affect premium customers most?”

It retrieves answers from internal documents like strategy PDFs, policy documents, and past customer interaction logs.

Tech Stack

Python for model development & orchestration
Pandas/Scikit-learn for churn prediction
OpenAI + LangChain for GenAI prompts and RAG
Streamlit for the assistant UI
FAISS for vector search in RAG
Kaggle Notebook for project showcase

Example Use Case

Scenario: A telecom company wants to reduce churn.
They upload their customer data, and the system:

Flags risky customers
Explains why each might churn
Generates a tailored retention strategy
Answers internal business questions via natural language

All in one assistant.

Impact

This project transforms churn analysis from a predictive task into a decision-making tool. It empowers business leaders to:

Act before it's too late
Understand customer pain points
Tailor strategies to individual needs

Limitations & Next Steps

RAG works best when clean, organized internal documents are available
Currently supports only English prompts
Next steps include adding multi-language support and real-time feedback loop into the assistant

Final Thoughts

This project was part of the Gen AI Intensive Course with Google, and allowed me to explore how GenAI capabilities like RAG, structured generation, and few-shot prompting can solve real-world problems.

Stay tuned for more GenAI applications at CodeOps Blog!

Fighting Churn with GenAI: Building an Intelligent Retention Assistant