Digital teams often rely on web traffic bots to validate website performance before going live. These bots simulate diverse visitor behaviors, helping developers understand how different user types will interact with their platforms. As explained in the blog, bots can stress-test features like login, checkout, or contact forms. However, if their activity goes undetected in analytics, it can result in skewed data, making performance seem better—or worse—than it is. This is especially critical when analyzing conversion metrics, which are easily influenced by automated traffic. The blog recommends deploying detection solutions that can distinguish bot behavior from genuine user patterns. Advanced tools such as browser fingerprinting, session validation, and heatmap monitoring help isolate bot traffic. Web traffic bots also support AI and ML projects, providing high-quality training data. When managed correctly, these bots enhance testing and reduce human workload, but they must be handled with full transparency for accurate reporting.