Geckoboard faced a challenge when its VP of marketing, Simon Whittick, generated an overwhelming number of leads, causing sales representative Alex Bates significant stress and inefficiency due to the sheer volume. To address this, Geckoboard implemented an automated lead scoring model, which used machine learning and data enrichment tools like Clearbit and MadKudu to prioritize leads based on firmographic and demographic data. This system allowed Alex to focus on high-potential leads, significantly improving conversion rates and reducing his workload. The automated lead scoring model assigned points to leads based on various attributes, allowing Geckoboard to quickly identify and prioritize promising prospects. This approach not only improved sales efficiency but also helped smaller teams manage large volumes of data effectively, ensuring that marketing efforts did not overwhelm sales capabilities. By integrating behavioral data and continuously refining the scoring model, Geckoboard was able to predict a high percentage of conversions from a small subset of signups, ultimately enhancing the collaboration between sales and marketing teams and allowing Alex to regain work-life balance.