By pulling data from the platform's backend, I considered a broad range of factors when beginning my analysis. I had access to demographic information, which provided deeper insights into the lifestyles of the users Poolhouse aimed to serve. Additionally, I accessed engagement metrics, such as 'first seen' dates, 'lastseen' dates, and total web sessions on the platform. These metrics were instrumental in calculating the churn rate and gaining a clearer understanding of the factors influencing churn. With these data points in mind, I proceeded to create a data dashboard to obtain a comprehensive view of the contributing factors.