Unlocking the Power of Propensity Modeling in AI Services

Propensity Model as a service

Propensity modeling is a set of predictive modeling approaches used to forecast the behavior of a target audience. These models analyze past user behavior to make predictions about future actions, and they find applications in various use cases, such as estimating churn rates and predicting feature adoption.

Here are some key points related to propensity modeling and AI services as discussed in the provided text:

  1. AI Services Definition: According to Vikas Raturi, a senior ML engineer at Intuit, AI services refer to delivering AI capabilities through a communication interface. The primary goal of AI services is to reduce development costs by enabling the reuse of AI capabilities across different use cases.
  2. AI Service Delivery Formats: AI services can take various forms, including trained model objects, batches of predictions, or serving endpoints. In enterprise settings, they may not necessarily involve a complete service but could be integrated as part of a larger system.
  3. Common Characteristics of Propensity Models: Most propensity models share certain characteristics:
    • They are trained on a wide range of user demographic and behavioral data.
    • While the underlying algorithm can be reused, different use cases often require separate models.
    • Propensity model predictions typically include customer identifiers and propensity scores.
    • Scalability is crucial for both training and inference tasks since these models are evaluated for the entire population.
  4. Common Feature Set: To streamline development and reduce costs, the text mentions the creation of a comprehensive feature set. This feature set, comprising more than 1000 features, is designed to serve various propensity model use cases, saving time and effort that would otherwise be spent on feature engineering.
  5. Kernel in Propensity Models: Each propensity model may require a separate model due to differences in use cases and business needs. However, the core kernel of these models can be reused. Hyperparameters and metrics may vary depending on the specific use case. For example, a churn prevention model might be optimized for higher precision or recall based on customer needs.
  6. Propensity Model as a Service: In the context of a propensity model as a service, different use cases leverage a common kernel that is optimized for propensity modeling. These models often treat the propensity problem as a time-to-event problem and may include optimization modules to generate action items based on customer behavior and propensity scores.
  7. Success Metrics for AI Services: The success of AI services is often measured by the quality of service delivery and user experience. Identifying the correct customer base, collecting feedback, and iterating based on user input are crucial factors contributing to the success of AI services.

In summary, propensity modeling involves predicting user behavior based on historical data, and AI services aim to deliver AI capabilities efficiently through various interfaces. These services can take different forms, including reusable models and optimized kernels for specific use cases. User experience and customer feedback play a vital role in the success of AI services.