A data scientist at Heap describes the development and impact of the Step Suggestions feature, part of the Heap Illuminate suite, which automates the creation of multi-step funnels for product analytics. This feature leverages statistical principles to suggest intermediate steps in user flows, helping businesses better understand where users drop off and prioritize improvements. By codifying criteria for effective funnel steps—ubiquity and divisiveness—the feature enhances the accuracy of funnel analysis, reducing reliance on the traditional "guess and check" method. The data scientist highlights the feature's success, noting that 38% of funnels receive useful suggestions, with many steps proving both ubiquitous and divisive, thereby offering actionable insights for product optimization. This automation not only speeds up the analytics process but also improves accuracy by avoiding common pitfalls such as adding unnecessary steps that users can skip. The article emphasizes the potential of high-throughput data science in solving numerous analogous problems across thousands of companies, showcasing the broader applicability and value of Heap's analytics tools.