Telco's Customer Churn Prediction using HeatWave AutoML

Introduction

In today’s hyper-competitive telecom landscape, customer churn—the likelihood of a subscriber leaving a service provider—has emerged as one of the biggest challenges. Acquiring new customers is expensive, while retaining existing ones directly boosts profitability. According to industry benchmarks, reducing churn by even 5% can increase profits by 25%–95%.

For telecom operators (telcos), predicting churn accurately and acting before customers leave is critical. Traditional approaches require a team of data scientists, complex machine learning pipelines, and weeks of effort. But with Oracle MySQL HeatWave AutoML, churn prediction can be simplified, automated, and scaled—all directly inside the database.

This blog explains how telcos can leverage HeatWave AutoML for churn prediction, from data ingestion to model deployment, and how it transforms customer retention strategies.

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What is HeatWave AutoML?

HeatWave AutoML is a fully automated machine learning capability built into the MySQL HeatWave service. It allows users to:

• Train ML models directly inside MySQL without moving data.

• Automate the entire ML pipeline—feature selection, model training, hyperparameter tuning, and model evaluation.

• Deploy models as SQL functions callable within queries.

This eliminates data movement, reduces latency, and allows DBAs, analysts, and application developers to create ML models .

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Why Churn Prediction for Telcos?

Telco customers generate a wealth of data—usage patterns, billing history, payment behaviors, complaints, and demographics. However, converting this raw data into actionable insights requires machine learning.

Key benefits of churn prediction in telecom:

1. Proactive retention – Identify customers at high risk of leaving before they switch providers.

2. Personalized offers – Recommend tailored plans, discounts, or loyalty rewards.

3. Optimized marketing spend – Target campaigns only at customers with high churn probability.

4. Customer satisfaction – Resolve pain points leading to churn (network quality, billing disputes, etc.)

Here is the Demo which i built using the Streamlit and python.

 

https://youtu.be/XULTaa3NSAM

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Churn Prediction Workflow with HeatWave AutoML

Let’s walk through the end-to-end workflow of building a churn prediction solution for a telco using HeatWave AutoML.

1. Data Preparation

The churn dataset typically includes:

• Customer Demographics – Age, gender, location, tenure.

• Service Usage – Call minutes, SMS count, data usage, roaming activity.

• Billing & Payments – Monthly charges, late payments, outstanding dues.

• Support Interactions – Complaints raised, resolution status.

• Churn Label – Binary variable (Yes/No).

Example table in MySQL:

CREATE TABLE telco_churn_data (

    customer_id VARCHAR(20) PRIMARY KEY,

    gender VARCHAR(10),

    tenure INT,

    monthly_charges DECIMAL(10,2),

    total_charges DECIMAL(10,2),

    contract_type VARCHAR(20),

    payment_method VARCHAR(50),

    internet_service VARCHAR(20),

    tech_support VARCHAR(10),

    churn VARCHAR(3) -- 'Yes' or 'No'

);

Data can be loaded using MySQL Shell Dump & Load or directly from Object Storage (OCI, AWS S3, Azure Blob, GCP) into HeatWave.

Once data is loaded into Heatwave then next step is data pre-processing which include data cleaning ,data scaling,normalization and making sure data is enrich .

you can use the jupyter notebook to visualize the data using seaborn, plotly etc.

basis your data understanding in the visualization you might need to add additional features to ensure model is covering the seasonality ,and hidden patterns likewise as an example Add recency-based features .

Basically your focus should be more towards:-

1.Data Completeness & Coverage

2.Data Quality & Cleaning

3.Feature Engineering Improvements

4. trying different Algorithms

Once you get new table with all the required features it's important to split into training set and testing set.

create table customer_churn.churn_data_train as select * from customer_churn.churn_data limit 0,8000;

create table customer_churn.churn_data_test as select * from customer_churn.churn_data limit 8001,10000;

2. AutoML Training

With the data in place, we use HeatWave AutoML to train a churn prediction model.

###Train the Classification Model

SET @model='classification_use_case_1';

CALL sys.ML_TRAIN('customer_churn.churn_data_train', 'Exited', JSON_OBJECT('task', 'classification'), @model);

This single command triggers:

• Feature Engineering – Detects categorical/numerical features and encodes them.

• Model Training – Trains multiple classification models (XGBoost, Random Forest, Logistic Regression, etc.).

• Hyperparameter Tuning – Optimizes model performance.

• Model Selection – Picks the best model based on accuracy and F1 score.

3. Model Evaluation

####Load the model

CALL sys.ML_MODEL_LOAD(@model, NULL);

####Score the model

CALL sys.ML_SCORE('customer_churn.churn_data_test', 'Exited', @model, 'accuracy', @classification_score, NULL);

SELECT @classification_score;

 4.Model Prediction

Once satisfied with the model score, call the model prediction it as a SQL function.

####Generate Predictions .

CALL sys.ML_PREDICT_TABLE('customer_churn.churn_data_test', @model, 'customer_churn.churn_data_test_predictions',NULL);

select * from customer_churn.churn_data_test_predictions limit\5\G;

above table will have the output of whether churn is there or not.

customer_id	churn_probability
C10001	0.82
C10002	0.15
C10003	0.67

5. Integration into Telco Systems

Telcos can integrate the model predictions into:

• CRM Systems – Flagging high-risk customers for agent follow-ups.

• Marketing Platforms – Triggering personalized offers.

• Dashboards – Visualization of churn trends by region, plan type, or tenure.

• Real-Time Alerts – Immediate notification when churn probability spikes.

Business Impact for Telcos

By deploying churn prediction with HeatWave AutoML, telcos gain:

1. Speed to Insight – Models trained in minutes vs. weeks of manual effort.

2. Cost Efficiency – No need for external ML platforms or complex ETL pipelines.

3. Actionable Intelligence – Real-time churn scores available inside MySQL queries.

4. Operational Simplicity – DBAs and analysts can run AutoML without ML expertise.

5. Scalability – Supports millions of subscribers with HeatWave’s distributed in-memory engine.

Example Scenario

Imagine a telco with 10 million subscribers. Historical data shows:

• Monthly churn rate: 3% (≈300,000 customers).

• Average revenue per user (ARPU): $25/month.

By identifying and retaining just 20% of churners (60,000 subscribers), the telco saves:

Best Practices

• Balance the dataset: Ensure churn and non-churn customers are proportionally represented.

• Regular retraining: Customer behavior evolves; retrain monthly or quarterly.

• Feature enrichment: Add network usage, complaints, and sentiment data for better predictions.

• Threshold tuning: Adjust churn probability cut-off (0.7, 0.8, etc.) based on business tolerance for false positives.

Conclusion

Customer churn is one of the biggest threats to telcos, but it is also a solvable problem. With MySQL HeatWave AutoML, telcos can predict churn accurately, act proactively, and retain revenue—all while simplifying operations and reducing costs.

The beauty of this approach lies in its accessibility: no data science team, no complex ML pipelines, just SQL and AutoML. For telcos, this means turning raw subscriber data into real-time retention strategies that directly impact profitability.

HeatWave AutoML doesn’t just predict churn—it transforms how telcos compete in the digital era.

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