Low-traffic experimentation can yield meaningful insights through strategic test design and adjusted significance thresholds, allowing teams to make informed product decisions despite limited statistical power. By focusing on simplified test designs, bold and noticeable changes, and leveraging targeted metrics, valuable data can be gathered even in low-traffic scenarios. While statistical significance may be challenging to achieve, qualitative feedback and iterative learning remain crucial, and inconclusive data can still inspire new hypotheses and iterations. Experimentation involves understanding customer needs and making data-driven decisions, and low-traffic testing is viable with strategic adjustments, helping product managers innovate and meet high customer expectations. Split offers a feature management and experimentation platform that supports teams in releasing impactful features efficiently, with a focus on risk reduction and enhanced team collaboration.