From Big Data to business analytics: The case study of churn prediction

Abstract The success of companies hugely depends on how well they can analyze the available data and extract meaningful knowledge. The Extract-Transform-Load (ETL) process is instrumental in accomplishing these goals, but requires significant effort, especially for Big Data. Previous works have failed to formalize, integrate, and evaluate the ETL process for Big Data problems in a scalable and cost-effective way. In this paper, we propose a cloud-based ETL framework for data fusion and aggregation from a variety of sources. Next, we define three scenarios regarding data aggregation during ETL: (i) ETL with no aggregation; (ii) aggregation based on predefined columns or time intervals; and (iii) aggregation within single user sessions spanning over arbitrary time intervals. The third scenario is very valuable in the context of feature engineering, making it possible to define features as “the time since the last occurrence of event X”. The scalability was evaluated on Amazon AWS Hadoop clusters by processing user logs collected with Kinesis streams with datasets ranging from 30 GB to 2.6 TB. The business value of the architecture was demonstrated with applications in churn prediction, service-outage prediction, fraud detection, and more generally — decision support and recommendation systems. In the churn prediction case, we showed that over 98% of churners could be detected, while identifying the individual reason. This allowed support and sales teams to perform targeted retention measures.