How do you handle putting more data into Braze from customer DB vs costs of data points?

That’s a great question, and it’s something we actively balance at Spark Networks as well. More data isn’t always better—relevant data is.

In my experience, I focus on ingesting only the data that directly enables personalization and segmentation. Instead of pulling in multiple data points, I look for ways to derive insights from fewer inputs. For example, rather than collecting three separate attributes, I might bring in just one and use Liquid logic in Braze to determine the right messaging variation.

Keeping the data structure simple upfront makes it easier to manage, reduces costs, and allows for faster iteration. Over time, as we refined our strategy and saw clear value in additional data points, we scaled up selectively and are now looking at integrating Snowflake.

Ultimately, the key is prioritizing the data that directly impacts engagement and conversions, rather than overloading the system with information that won’t drive actionable outcomes.

Thanks Daphne, makes a lot of sense. Do you use predictive model scores (like churn probability, etc.) as an attribute to trigger communications?