Predictive modeling is becoming an essential part of data-driven decision-making in modern businesses. Among the most practical applications is the propensity to buy model, which estimates the likelihood that a customer will make a future purchase based on historical behavior. These models are widely used in marketing, sales, and customer retention strategies to help teams focus on high-value opportunities and personalize customer engagement.
Using Snowflake Notebooks, data teams can build and operationalize these models entirely within the Snowflake ecosystem. This approach enables predictive workflows to remain close to the data, simplifying development, improving security, and reducing friction between insight and action.
What is a Propensity to Buy Model?
A propensity to buy model uses customer-level data such as purchase frequency, average transaction value, and recency to predict the probability of a future purchase. Each customer is assigned a score that reflects this likelihood. These scores can be used to prioritize sales outreach, segment marketing audiences, or trigger personalized experiences across digital channels.
The core value of this model lies in its ability to focus attention on customers who are most likely to take action. Instead of treating all leads or customers equally, the model helps teams allocate resources where they will have the most significant impact.
Why Build It in Snowflake?
Building predictive models directly in Snowflake offers several advantages:
- Data stays in one place. There is no need to move or replicate data to external tools, which streamlines workflows and reduces risk.
- Scalability is built in. Snowflake’s architecture handles large volumes of data without compromising performance.
- Notebook integration allows for code-based exploration, model training, and results analysis in one environment.
Snowflake Notebooks make it possible to conduct feature engineering, train models, and explain predictions using familiar languages like SQL and Python, all within the same platform where data already lives.
Understanding Model Explainability with SHAP Values
One of the key requirements in modern predictive analytics is explainability. Stakeholders need to understand why a model makes a certain prediction. This is where SHAP values come in.
SHAP (Shapley Additive Explanations) is a method that quantifies the contribution of each feature to a prediction. For a given customer, SHAP values can show how metrics like purchase frequency or average spend are influencing the score. This makes it easier to build trust in the model and support business decisions with clear, interpretable insights.
Bringing Predictions to Life with BI Tools
Once the model is built and scores are calculated, results can be surfaced in business intelligence platforms like Power BI. This allows business users to explore the outputs without needing to understand the underlying code or models.
A typical dashboard might include:
- A market-level overview of average propensity scores across regions or product categories
- Feature-level insights into what drives customer likelihood to buy
- Customer-level views that show individual scores and explanations
These visualizations help bridge the gap between technical model outputs and business action.
Getting Started with a Propensity to Buy Workflow
Implementing a propensity to buy model in Snowflake typically involves the following steps:
- Data preparation: Ingest and clean customer transaction data within Snowflake.
- Feature engineering: Create behavioral features like recency, frequency, and monetary value.
- Model development: Use Snowflake Notebooks to train a classification model and generate propensity scores.
- Explainability: Apply SHAP or similar methods to interpret the model’s output.
- Operationalization: Publish results to BI tools or use them to trigger automated workflows.
This end-to-end workflow remains within the Snowflake environment, enabling faster development and more secure handling of sensitive customer data.
Propensity to buy models are a powerful way to identify high-intent customers and deliver more targeted, effective engagement. By using Snowflake Notebooks, data teams can move from raw data to actionable insight without leaving the platform. This accelerates the modeling process and makes it easier to scale and explain predictive analytics across the business.
Talk with us today to explore how this workflow can fit into your data strategy.
