In the early development stages of products like Heap, traditional analytics are often ineffective due to small sample sizes, making it difficult to achieve statistical significance. Instead, individual user analysis emerges as a crucial tool, allowing companies to monitor user activity at a granular level to identify patterns and issues that may not be apparent through user feedback alone. This approach helps developers understand user behavior, such as which features are underutilized or difficult to navigate, and can guide improvements in product design and user onboarding. However, individual user analysis has limitations, such as the potential for non-representative samples and scalability challenges as the user base grows. While tools like Google Analytics are not suited for this purpose, session recording tools and event-based tracking like Heap offer valuable insights by automatically capturing user interactions without custom coding. This method provides a more complete picture by combining user conversations with tracking data, although it should be complemented by other data as the product evolves.