# 9.2 Cashback Science: Cashback science lies at the core of our rewards mechanism, leveraging data analytics, behavioral economics, and user insights to optimize cashback offerings for maximum impact and value. By analyzing user spending patterns, preferences, and engagement metrics, we tailor cashback rewards to align with user interests, drive desired behaviors, and enhance overall satisfaction. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://365-cashback.gitbook.io/365-cashback-token-white-paper/9.-token-pricing-and-cashback-science/9.2-cashback-science.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.