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Digital personal finance company that started 2011 by developing a lending solution to address the mounting student debt problem in the US
Unaware of a technical issue on their website causing an increase in incomplete loan applications and was blind to the magnitude of the phenomenon
Prevented $9M in potential revenue loss prevented
SoFi is a digital personal finance company that started 2011 by developing a lending solution for students with lower loan rates in an attempt to address the mounting student debt problem in the US. Now with over 1,500 employees, the San Francisco-based company has branched out into mortgages, personal loans, investing and banking—all with a digital-first approach.
SoFi was not aware of a technical issue on their website causing an increase in incomplete loan applications and was blind to the magnitude of the phenomenon. In fact, the issue went completely under their radar until Glassbox started to capture and visualize all of their user sessions.
With Glassbox’s tag-less deployment, SoFi was capturing 100% of sessions and all events and elements, including user activity and technical elements occurring behind the scenes.
Glassbox’s anomaly detection engine, based on machine learning, alerted the SoFi team to 546 sessions in a one-week period in which users received an error message and never completed their loan application.
SoFi leveraged the Cashbox dashboard in Glassbox to capture the value of the user input in the loan application form. The team was able to aggregate all loan applications and track the following insights:
By recognizing and understanding the full scope of the problem and true monetary impact of this technical error, the business team and the IT teams flagged this issue as a top priority.
By sharing session replays of the affected sessions with the production support team, the root cause of error was identified and fixed within the same day.
“Glassbox gives us the most accurate view of how our members’ experience our app and saves us countless time on squashing bugs, finding friction, and gives us confidence in how we plan our UX roadmap.”
Software Development Lead, SoFi
If the glitch had continued to go unnoticed, the failed sessions would add up to over 28,000 abandoned applications annually. With an average success rate of 38.2%, it’s estimated this error could have caused a loss of 10,845 loan deals. Based on a hypothetical monthly loan value of $900* per loan, SoFi prevented a potential annual loss of over $9 million by fixing this error.
*actual loan value has been changed for demonstration purposes
in potential revenue loss prevented
