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GOAL: The company was dealing with millions of messy, duplicate customer records in disparate databases, which prevented the airline from knowing how many customers they had and confirming accurate contact information to market their loyalty program directly.
INSIGHT + ACTION: Using a proprietary matching algorithm, Beacon’s team consolidated, stitched together & de-duped millions of customer records to acquire customers directly and increase Loyalty membership.
TECH STACK: Data lake and data warehouse hosted on AWS (Redshift, S3). Leveraged Python for data pipeline as a service. SQL & Airflow for data wrangling. PowerBI deployed as their BI platform.
BUSINESS OUTCOME: By uncovering unique identifiers in the data and making direct connections to Azul's customers, Azul added ~2M new incremental loyalty members through newly enabled marketing techniques. This equated to an average lift in flights per active passenger of over 50% through customer data cleansing and identity resolution matching.
“Very early on, they figured out how to identify these indirect channel customers. For the first time in a long time we could uniquely market and cross-sell to our customers. Using the tools Beacon has developed for us, we now have models that allow us to target any segment of customers based on their propensity to buy any of our products or services. Our customer engagement has increased significantly since we are now offering each customer a product that is relevant to them, not a blast communication that spams our customers.”
- Abhi Shah, Azul CRO