The new capability allows you to monitor transactions by a specific transaction ID (session ID, IP, user name, email, etc.) while handling data from distributed systems, where a request is passed through several different systems, each with its own transaction ID.
Over the past two months, we have worked with beta customers on a variety of use cases, including:
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Tracking transactions in a payment processing platform
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Following typical user sessions, detecting anomalous checkout transactions and catching checkout drop off in e-commerce websites
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Tracking renewals, upgrades and new signup transactions
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Monitoring phone registrations failures over a specific period
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Tracking on-boarding of new users in SaaS products
The last use case is reflective of what SaaS companies care most about: truly understanding the behavior of users on their website that drive long-term engagement. We’ve used our new transaction analytics capabilities to better understand how users find our site, the process by which they get to our Sumo Logic Free page, and how quickly they sign up. Our customer success team uses Transaction Analytics to monitor how long it takes users to create a dashboard, run a search, and perform other common actions. This enables them to provide very specific feedback to the product team for future improvements.
Many of our customers are already using tools such as Google Analytics to monitor visitors flow on their website and understand customer behavior. We are not launching this new capability to replace Google Analytics (even if it’s not embraced in some countries as Germany). What we bring on top of monitoring visitors flow, is the ability to identify divergence in state sequences and understand better the transitions between the states, in terms of latency for example. You probably see updates that some companies are announcing on plugins for log management platforms to detect anomalies and monitor user behavior and sessions. The team’s product philosophy is that we would like to provide our users all-rounded capability that enables them to make smart choices without requiring external tools, all from their machine data within the Sumo product.
It was a fascinating journey working on the transaction capability with our analytics team. It’s a natural evolution of our analytics strategy which now includes: 1) real-time aggregation and correlation with our Dashboards; 2) machine learning to automatically uncover anomalies and patterns; and 3) now transaction analytics to rapidly uncover relationships across distributed events.
We are all excited to launch Transaction Analytics. Please share with us your feedback on the new capability and let us know if we can help with your use cases. The transaction searches and the new visualization are definitely our favorite content.
