Tackling Imbalanced Data for Improved Churn Prediction with Snowflake and Hex

In an age where customer preferences shift at the speed of light, keeping them engaged and committed to your brand is nothing short of a Herculean task. Traditional methods no longer suffice; what's needed is a data-driven, intelligent approach. This is where the magic of machine learning comes into play, allowing businesses to predict who's likely to walk away before they actually do. Our focus here is to build an effective and interactive churn prediction model using a Random Forest Classifier. But we won't stop there; we will tackle inherent challenges like imbalanced data and evaluate the model rigorously to ensure its effectiveness.

Part I: Data Retrieval with Snowflake:

One of the essential steps in any data science project is getting your hands on the right data. For this task, I turned to Snowflake, a cloud-based data warehousing platform that excels in scalability and performance. Using Snowflake's straightforward SQL interface, I was able to quickly query and retrieve the telecom churn dataset that serves as the backbone of this analysis.

The dataset is a goldmine of information, containing a plethora of variables like the number of weeks the account has been active (`AccountWeeks`), whether the contract was renewed (`ContractRenewal`), the amount of data used (`DataUsage`), and many more. This dataset not only gives us the 'who' and the 'what' but also sets the stage for understanding the 'why' behind customer churn.

Data Writeback to Snowflake and Repulling:

After performing initial manipulations in Hex, I wrote the data back to Snowflake using the `Writeback` functionality. This capability showcases the seamless integration between Hex and Snowflake, simplifying the ETL processes.

As the last step in our data manipulation cycle, I pulled this data back into Hex using the Snowflake connection. This makes sure that our Hex notebooks and Snowflake database are synchronized, enabling real-time analytics.

select * from "PC_HEX_DB"."PUBLIC"."CHURN_DATA."

Exploratory Data Analysis (EDA)

Before diving into machine learning models, it’s essential to make sure the data is clean and well-structured. I performed a null-check across all columns, confirming that the data is ready for the next steps.

Part II: Understanding Churn Rate and Data Imbalance

The Catch with Imbalanced Data:

If we want a model to be reliable, understanding its weaknesses is crucial. In churn prediction, we often face imbalanced data, where one class significantly outweighs the other. This imbalance can lead to biased models, as they may achieve high metrics by merely focusing on the majority class.

To truly understand how our model performs, we need to address this imbalance.

Snowpark Connection and Data Balancing

Before we could balance the data and proceed with feature engineering, we initiated a Snowpark session in Python. To assess the imbalance, we calculated the ratio of our target variable, `Churn`.

Tackling Imbalance with SMOTE

Since our dataset is imbalanced, particularly in the 'Churn' target variable, the immediate priority is to rectify this imbalance to ensure a fair training process for the model. This is where the Synthetic Minority Over-sampling Technique (SMOTE) comes into play.

SMOTE balances the dataset by creating synthetic samples in the minority class. It does this by identifying nearby instances in the feature space and generating new points along the lines connecting them. This enhances the model's ability to learn a more accurate decision boundary between classes.

Data Preprocessing with Snowpark ML

With the data balanced, it is time to get it ready for modeling. Here, Snowpark ML played a significant role.

Part III: Training, Evaluation, and Prediction

We chose the Random Forest Classifier as our predictive model. Essentially, it's an ensemble model that combines the predictions of multiple smaller models (trees).

In the context of churn prediction, a false negative—predicting that a customer won't churn when they actually do—is costly for the business. That's why we focus on recall as our key metric, as it measures the ability of the model to correctly identify all relevant instances, in this case, actual churns.

For feature importance, understanding which variables have the most impact on predictions can guide business strategies. For example, if 'contract renewal' is a highly important feature, then efforts could be directed towards customer retention programs that encourage renewals. Identifying important features not only refines the model but also aids in making data-driven business decisions.

In the Hex app developed by SnowFlake, I've taken prediction to the next level with an interactive panel. This allows users to dynamically input or alter user variables—like account weeks, contract renewals, and data usage—to instantly receive churn predictions along with a confidence score. This interactive feature makes it incredibly easy not only to make predictions but also to understand the likelihood of those predictions being accurate. It serves as a potent tool for identifying and prioritizing high-risk customers, offering a more targeted approach to customer retention.

Go ahead and experiment with your own custom user profiles in the interactive panel. It's a great way to see just how confident the model is in its churn predictions!

HEX App for Churn Rate

Conclusion: The Takeaway

Through this journey, we addressed the challenges of imbalanced data, deployed an effective machine learning model, and set up a reliable evaluation framework. We're not just predicting who will leave, but also identifying why they might do so, enabling a more proactive customer retention strategy.

So, what's next? Integration into live business environments and monitoring to refine the model as new data becomes available. We are equipped not just to face the future but also to shape it.

This comprehensive approach ensures that we're not just scratching the surface but diving deep into the mechanics of customer behavior, setting the stage for effective customer retention strategies.

#DataScience #Snowflake #Hex #ChurnAnalysis #MachineLearning #DataImbalance #RandomForest #ModelEvaluation