Leveraging Data and AI to Personalize Member Experiences for Credit Union

Background 

BDO Digital had the privilege of partnering with a mid-sized Credit Union to enhance their member segmentation strategy. With a diverse member base of over 100,000 individuals, the Credit Union recognized the need to leverage data and AI to better understand their members and identify new member segments. Our team was thrilled to collaborate with them on achieving their goals. 
 

Challenge 

The Credit Union faced a significant challenge in enhancing their member segmentation strategy. With a diverse member base, they needed to leverage data and AI to better understand the unique needs and preferences of their members. The Credit Union was seeking to identify new member segments that were previously unknown and deliver personalized marketing efforts and tailored product offerings to increase member engagement and satisfaction. 
 

Solution 

To begin the project, we collaborated closely with the Credit Union to develop a comprehensive Data and AI Blueprint. This involved understanding their business objectives, analyzing their existing data infrastructure, and identifying areas where Data and AI could drive actionable insights.  

By working closely with the Credit Union's team, we were able to create a tailored blueprint that aligned with their specific needs and goals. 

Next, we focused on implementing the Credit Union's data strategy in Amazon Web Services (AWS). Leveraging the power and scalability of AWS, we helped the Credit Union migrate their data to a secure and efficient cloud environment. This allowed them to centralize their data, making it easily accessible for analysis and segmentation purposes. By utilizing AWS's advanced data management and analytics tools, we ensured that the Credit Union had a robust foundation for their member segmentation efforts. 

With the data infrastructure in place, our team is working closely with the Credit Union to identify new member segments. Leveraging advanced analytics techniques and machine learning algorithms, we are starting to analyze the vast amount of data available to uncover hidden patterns and insights. By combining demographic, transactional, and behavioral data, we were able to identify distinct member segments that were previously unknown to the Credit Union.