An effective lead scoring approach does not necessarily require machine learning, as demonstrated by Clearbit's experience, which emphasizes starting simple and evolving over time. Instead of getting caught up in the complexity of advanced data tools, Clearbit and Census advocate for beginning with a straightforward method, using a single metric to establish a baseline, and gradually refining the process through experimentation. As businesses grow, lead qualification methods must adapt, and at Clearbit, this involves continuously monitoring and adjusting their approach to ensure it remains effective. The process involves considering both fit and intent, with fit determining how well a potential lead matches the ideal customer profile, and intent indicating their interest level. Balancing these factors can help prioritize high-fit, high-intent leads, while remaining adaptable to changes in the business environment and data availability. This approach highlights the importance of combining data insights with human intuition and experimentation to effectively manage lead scoring.