A large financial company with tens of thousands of merchants, did not have real‐time, time‐ stamped visibility of orders and revenue by merchant and buyer. Post‐processing information was accomplished as a time‐consuming batch job run periodically throughout the day. Executives wanted a real‐time view so they knew the health of the business on‐demand. They also wanted to have the flexibility to self‐serve and check on trends over time. IT Operations wanted to be able to correlate orders and revenue with transaction performance.
They did not have visibility of transaction time per merchant making it difficult to prove they were maintaining their SLAs for each and could not easily justify budget for improvements to their architecture. Executives and IT staff did not want to have to modify their applications to log this information because of the cost, complexity, and system overhead created by such an effort.
- One view showing transactions and revenue
- Insight to justify further infrastructure investment based on revenue
- Geographic information (By state, country, etc)
- A way to understand if, when, and where there are payment issues for fast remediation
A financial company wanted to correlate merchant revenue with payment transaction performance. They also wanted to track purchasing patterns by merchant, geography, card type, user, and order item—all information available on the wire.
Because the payment processing engine and the transactions were based on XML over HTTPS they used ExtraHop's streaming payload analysis. An Application Inspection Trigger was written, tested, and deployed in less than 30 minutes. The AI Trigger was designed to extract the unique User ID, Merchant ID, unique Order ID, and Order Amount while measuring each transactions round‐trip and time to last byte served to the client. Visualization and analysis was grouped by Merchant ID. In one executive business analytics dashboard they were able to show all unique orders by merchant, the total revenue by merchant and the transaction times with SLA thresholds. Because of ExtraHop's rich trending capabilities they could begin to determine if there was a correlation with performance and order volume in aggregate and by individual merchant. If transaction times began to degrade, they now had the capability to drill down and identify any component within the transaction and application delivery chain that was causing the degradation for rapid remediation.
ExtraHop's cross‐tier wire data analysis was used to inform executives of any unusual drop off in revenue or transactions by merchant. This information could now inform a rapid response workflow where their customer service team could call to see what they could do to assist that merchant with additional advertising or cash back promotion services to jumpstart revenue. The fact that they also had a real‐time baseline meant they could also track and report back efficacy of those promotions. At the same time, IT could leverage ExtraHop's wire data to easily justify continual application, network, and infrastructure improvements ensuring revenue protection and growth. According to the CIO this was one of the few times in his career that he felt IT truly aligned to the business.