The situation at a fictitious Sample Outdoors company
In Part 1: Speeding up machine data analysis of this series, the data scientists at the Sample Outdoors company were able to acknowledge issues reported on Sat July 14th, using their logs across the application stack. They were able to get insights into the potential root causes of this issue.
Many customers were impacted on Sat July 14th and the customer support center was flooded with emails from complaining customers. The Sample Outdoors company risked adverse publicity and also feared losing current and potential customers. One way to remediate the problem was to provide coupons with appropriate savings to these customers for a future purchase. Sat July 14th was one of the busiest days during Sample Outdoors biggest mid-year sale, and huge amount of customers were impacted. Sample Outdoors wanted to prioritize these savings to the specific customers that had contacted the support center via emails.
In order to do this, the Sample Outdoors company needed to get a consolidated view of all the customer orders that were attempted and the customers who were impacted. The Sample Outdoors company already had information about the attempted orders available for analysis. They now wanted to add the customer emails to be able to get enough information about the customers and the size and details on their orders to offer them the appropriate savings in coupons.