In a recent exploration of non-traditional data approaches for identifying buyer personas, a study utilized customer service ticket data from an airline to build a model that clusters customers into distinct groups, offering insights into their behaviors and preferences. The traditional methods of identifying buyer personas are often costly and biased, whereas the use of machine learning (ML) allows for a more scientific and data-driven approach. Despite the challenges in operationalizing ML models, such as the difficulty in explaining results to non-technical stakeholders and the complexity of clustering, visual tools and collaborative efforts with business intelligence teams can help interpret the data effectively. The study revealed three customer clusters: passengers who mostly did not board flights, those who boarded with minimal interactions, and unsatisfied customers with unresolved service agreements. By analyzing these clusters, companies can calculate metrics like Lifetime Customer Value and Customer Acquisition Cost, and tailor marketing strategies to enhance customer engagement and revenue, ultimately fostering more personalized connections with customers.