Propensity Modeling Using BigQuery Machine Learning

Propensity modeling helps businesses predict the likelihood of certain customer behaviors, such as purchasing or churning. By leveraging BigQuery ML (BQML), Google’s machine learning capabilities within BigQuery, data teams can efficiently develop and deploy propensity models directly within their data warehouse. Here’s a concise guide on creating a propensity model using BQML, along with its applications and benefits.

What is Propensity Modeling?

Propensity modeling estimates the probability of a particular action or behavior based on historical data. In marketing, it’s used to predict actions like purchase likelihood, customer retention, or ad clicks, making it invaluable for tailoring outreach strategies. BigQuery ML facilitates this by offering a SQL-based interface to build models, bypassing the need for external machine learning platforms.

Why Use BigQuery ML for Propensity Modeling?

Using BQML has distinct advantages:

1. Simplicity: SQL-based modeling allows teams to build models without extensive knowledge of coding languages like Python or R.

2. Integrated Workflow: Direct access to datasets within BigQuery avoids moving data across platforms.

3. Scalability: BigQuery’s infrastructure supports large datasets, making it ideal for enterprises.

4. Speed and Efficiency: With BQML’s optimized performance, you can train and evaluate models faster than with traditional setups

Steps to Create a Propensity Model in BigQuery ML

To create a propensity model, you’ll follow these steps:

1. Data Preparation: Prepare and load a dataset into BigQuery. The dataset should include key features like customer demographics, engagement metrics, and a target variable for the behavior you want to predict (e.g., purchase likelihood).

2. Define Target Variable: Set a binary target variable, such as will_purchase (1 for likely to purchase, 0 for not likely). This becomes the prediction goal for your model.

3. Feature Engineering: Develop additional features that may influence the target variable. Examples include recency (days since last purchase), frequency (number of recent interactions), and engagement scores.

4. Create the Model: BigQuery ML allows you to create a model using SQL. For a logistic regression propensity model, run a query similar to:

   CREATE OR REPLACE MODEL `your_dataset.propensity_model`

   OPTIONS(model_type='logistic_reg') AS

   SELECT

     age, gender, location, previous_purchases, engagement_score, will_purchase

   FROM

     `your_project.your_dataset.customer_data`

5. Evaluate the Model: Check your model’s performance using BigQuery’s evaluation functions to view metrics like accuracy and AUC (Area Under Curve):SELECT

     roc_auc, accuracy

   FROM

     ML.EVALUATE(

       MODEL `your_dataset.propensity_model`,

       (SELECT * FROM `your_project.your_dataset.customer_data`)

     )

6. Make Predictions: Run predictions on new or existing data:SELECT

     customer_id,

     predicted_will_purchase,

     predicted_will_purchase_probs[OFFSET(1)] AS propensity_score

   FROM

     ML.PREDICT(

       MODEL `your_dataset.propensity_model`,

       (SELECT * FROM `your_project.your_dataset.new_customer_data`)

     )

7. The propensity_score will indicate the likelihood of each customer’s behavior, allowing for targeted actions.

Applications of Propensity Modeling

Propensity models have wide-ranging applications:

• Targeted Marketing: Focus campaigns on customers most likely to engage or purchase.

• Retention Efforts: Identify at-risk customers and develop proactive retention strategies.

• Cross-Selling and Upselling: Anticipate which customers are open to related offers, maximizing revenue opportunities.

Best Practices

• Feature Selection: Prioritize features that align with customer behavior for better accuracy.

• Regular Model Updates: Retrain models periodically to capture evolving customer patterns.

• Data Privacy Compliance: Ensure all customer data complies with privacy regulations like GDPR.

Conclusion

BigQuery ML simplifies the process of building and deploying propensity models directly within a data warehouse, making it an essential tool for data-driven decision-making. By providing actionable insights from within BigQuery, it empowers organizations to personalize engagement and optimize resource allocation efficiently. Propensity modeling with BQML is a powerful approach to understanding customer behaviors, helping businesses drive smarter, more targeted strategies.