An airline company sought assistance in identifying their buyer persona by utilizing non-traditional data such as flight and contact information through Machine Learning (ML). Despite the abundance of customer data that airlines possess, they often underutilize it, particularly in the context of evolving consumer behaviors post-COVID-19. Clustering, a method to mine data for similarities, provides a strategic advantage but requires data centralization and expertise with tools like PySpark. No-Code solutions like Datagran simplify this process, enabling businesses to integrate multiple data sources and apply ML models without extensive coding. The platform allows users to create pipelines for data processing, training, testing, and predicting, facilitating the visualization of results to identify valuable customer clusters. These insights can be operationalized through integrations with business applications, illustrating how companies can leverage ML to enhance marketing strategies, even with small teams or limited technical resources.