Posting a random idea/use case using Neo4J for customer churn under telcom domain. We all know that predicting a customer churn is important for any company and making necessary things to retain works in almost all industries. We specifically focus on telcos, and we are brainstorming with data for improving the churn model that is built using XGBoost. Traditional ML algos work well , give a decent rate of accuracy. But we see the potential of GraphDBs here, but the customer's data is all in traditional RDBMS format, the challenge is how do we use it with neo4j? Pushing huge data everyday would be huge effort and time consuming, Any effective ways are there?
Any thoughts are welcome!