Optimizing New User Experience in Online Services

Abstract

"Well begun is half done". This proverb is especially true for a web product when it comes to creating a delightful and proactive user experience. This article describes our work in the last few years optimizing new user experience at LinkedIn, driven by application of data science and advanced analytical methods. Through mining the logs generated by new users in the past, we uncovered signals from their initial session that can predict their retention. We established the difference between creating models for predictions and creating models to inform product strategy. We found that persistent features, such as a user's number of connections, and having a confirmed channel of communication (email, phone or app), more strongly predict new user retention than most transient features such as how long they spend on the registration form or how many page views they have visited. We further constructed a true north metric (Quality Signup) to drive our Growth team towards the right focus as they iterated through multiple versions of new user onboarding flows. The strong positive correlation between the Quality Signup metric and long-term retention, as well as the positive impact we have seen on the product over the last two years, validate our strategy to drive product roadmap through data-informed metrics.