Comprehensive Churn Prediction and Retention System

Problem Statement

As an AI/ML engineer, you are tasked with developing a comprehensive churn prediction and retention system for XYZ Bank. Your goal is to design and implement a solution that includes:

• Predictive Models for Churn Analysis: Analyzing customer data to predict which customers are at risk of leaving and identifying factors contributing to churn.
• NLP for Customer Feedback Analysis: Understanding reasons for dissatisfaction by analyzing textual feedback from surveys, complaints, and social media.
• Generative AI for Retention Strategies: Developing and testing personalized retention strategies and communication plans to reduce churn rates based on user feedback.

Solution

Solution Architecture

The solution architecture includes the following components:

1. Data Preprocessing: Cleaning and preparing the dataset for model training.
2. Predictive Models: Building and evaluating machine learning models for churn prediction.
3. NLP Analysis: Analyzing customer feedback to understand dissatisfaction.
4. Generative AI: Creating personalized retention strategies and communication plans.
5. System Integration: Integrating all components into a cohesive system for deployment.

Potential Impact

The solution will enhance customer retention by providing actionable insights and personalized strategies, ultimately reducing churn rates and improving overall customer satisfaction.

Dataset

You will work with the provided datasets, which include customer information, feedback, and other relevant data for analysis and model training.

Task Requirements

a) Data Understanding and Preprocessing

• Examine Datasets: Describe the structure and features of the provided datasets.
• Preprocessing: Implement necessary data cleaning, handle missing values and outliers, and perform exploratory data analysis (EDA).
• Feature Selection: Identify relevant features for churn prediction.

b) Predictive Models for Churn Analysis

• Algorithm Selection: Justify the choice of machine learning algorithms for churn prediction.
• Feature Engineering: Apply techniques to enhance model performance.
• Model Development: Develop and compare at least two different models.
• Evaluation: Use relevant metrics to evaluate model performance and identify key features contributing to churn.

c) NLP for Customer Feedback Analysis

• Text Preprocessing: Clean and preprocess textual feedback.
• Sentiment Analysis: Implement sentiment analysis to gauge customer satisfaction.
• Insights Extraction: Develop methods to extract key insights and reasons for dissatisfaction.

d) Generative AI for Retention Strategies

• Approach Proposal: Describe how generative AI will be used to create personalized retention strategies.
• Communication Plans: Develop tailored communication plans based on customer profiles and churn risk.
• Evaluation and Refinement: Implement methods to evaluate and refine generated strategies.
• Integration: Explain how this component will integrate with predictive models and NLP analysis.
• Chatbot Development: Build a chatbot for interaction based on user queries.

e) System Integration and Deployment

• Integration Architecture: Outline the architecture for integrating all components.
• Deployment Strategy: Propose a deployment strategy (e.g., cloud-based, on-premises).
• Real-time Processing: Describe handling real-time data processing and model updates.
• Scalability: Discuss scalability challenges and solutions.

f) Ethical Considerations and Privacy

• Ethical Implications: Discuss potential ethical issues related to churn prediction.
• Privacy Measures: Propose measures for customer privacy and data protection.
• Bias Mitigation: Address potential biases in the model and methods to mitigate them.

Deliverables

1. Jupyter Notebooks: Complete notebooks with the entire workflow, including data loading, EDA, feature engineering, model building, and deployment code with comments.
2. Python Scripts: Final implementation scripts.
3. Presentation: A maximum of 10 slides summarizing the approach, key findings, and recommendations.
4. Requirements File: requirements.txt listing all necessary libraries and their versions.

Directory Structure

• data/ - Contains the dataset used for training and testing.
• model/ - Stores the trained model, label encoders, and scalers.
• utils/ - Utility functions used throughout the project.
• 0-preprocess.py - Script for preprocessing the data.
• 1-train.py - Script for training the churn prediction model.
• 2-test.py - Script for testing the trained model.
• 3-api.py - FastAPI script to create an API for the churn prediction model.
• 4-chatbot.py - Script to deploy a chatbot that uses the churn prediction API.
• requirements.txt - List of dependencies required for the project.

Getting Started

Prerequisites

Ensure you have Python installed. Install required dependencies using:

pip install -r requirements.txt

Data Preprocessing

Before training the model, preprocess the data using:

python 0-preprocess.py

This script will clean the data and prepare it for model training.

Feature Selection, Encoding, and Scaling

Feature Selection

Feature selection involves selecting the most relevant features from the dataset to improve the performance of the model. In this project, feature selection is handled using:

• Pandas: For manipulating and selecting features.
• Scikit-learn: For feature selection techniques such as variance threshold, recursive feature elimination, etc.

Encoding

Encoding is the process of converting categorical variables into numerical values. In this project, encoding is done using:

• Scikit-learn Labelencoder: For label encoding of categorical features.

Scaling

Scaling is the process of normalizing the range of independent variables. In this project, scaling is performed using:

• Scikit-learn StandardScaler: For standard scaling to ensure all features contribute equally to the model.

Training the Model

Train the churn prediction model (Logistic Regression) which beats Tree based models on this task with:

python 1-train.py

This script will train the model and save it along with label encoders and scalers in the model/ directory.

Testing the Model

Test the performance of the trained model using:

python 2-test.py

This script will evaluate the model on test data and print the performance metrics.

Model Performance: Recall

The recall for the positive class (customers who are likely to churn) is 79%. Recall is a crucial metric in this context because it measures the ability of the model to identify actual churners. High recall means that most of the customers who will churn are correctly identified, which is vital for taking proactive measures to retain them.

Why Recall is Important

• Customer Retention: Identifying customers who are likely to churn allows businesses to intervene and potentially retain these customers through targeted marketing efforts.
• Cost Efficiency: The cost of retaining a customer is typically lower than acquiring a new one. High recall ensures that fewer churners are missed, reducing the overall cost of customer churn.
• Business Strategy: High recall provides actionable insights, allowing businesses to develop more effective strategies for customer retention and satisfaction.

Deploying the API

Deploy the model as an API with FastAPI:

python 3-api.py

The API will be accessible at http://127.0.0.1:8000.

Running the Streamlit Chatbot

Launch the chatbot interface using:

streamlit run 4-chatbot.py

The chatbot will interact with users and predict churn based on the model.

Limitations

The current version of the Marketing Guru Chatbot is not designed for open-ended conversations. It takes a specific scenario as input, converts it to appropriate model inputs, and returns a prediction. The chatbot does not handle general conversational inputs well; messages like "Hi" or "Who are you" are hardcoded responses for now.

Usage Advice

For the best experience, use the chatbot in a scenario-based manner: provide a specific customer scenario, and the chatbot will return the churn prediction. Future versions may include more advanced conversational capabilities.

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