To understand if our members liked the faster experience, we looked at how many user sessions were created in each segment and how many members visited the application. For example:
In each network quality class, the improvement in site speed gradually improved across the board with the members on fast networks seeing the least improvement, while the ones on slow networks saw the largest gains.
Subsequently, the number of unique members visiting the application and the number of sessions both improved proportionally.
One segment stood out to us as an anomaly: the number of sessions did not grow proportionally with site speed improvement for the average class. We concluded through further analysis that this is most likely due to the “lighter” nature of the experience not being “preferred” by this class of members over a slower experience. This is likely where the trade off between site speed and features becomes critical.
This analysis proved two hypotheses:
Members are more engaged on a faster application. As the experience gets faster, user engagement goes up. This is especially true for members already on slow networks.
Network quality plays a significant role in understanding our members’ experience. By appraising the network quality of a user, we could customize their experiences by providing them what they prefer, suitable to the network they are on.
Network quality as a service: Customizing content delivery
The experiments and analyses above helped us better understand how members’ network quality, among other factors, influences their experience on the application. We can then use this knowledge to customize the content delivery to their needs and enrich our members’ experiences on LinkedIn by undertaking the effort to provide network quality as a service within LinkedIn.
Defining and measuring metrics for network quality is fairly straightforward to implement and employ. The challenge arises in circumstances where measurement might either be antiquated or simply infeasible. To handle such scenarios, we have built a deep learning pipeline using RUM data to be able to predict the network quality of every connection to LinkedIn.
Stay tuned for Part 2 of this series to be focused on delivering customized content to our members.
The entire story leading to network quality as a service has been a multi-team effort spanning many quarters. This has involved many engineers and managers across the Performance Engineering, Edge SREs, Traffic Infra Dev, Data Science, Flagship Web, LinkedIn Lite, and AI/ML teams at LinkedIn.
I would like to specifically thank Ritesh Maheshwari and Anant Rao for supporting us through this journey. Special mention to Brandon Duncan for putting up with us through the process and being supportive of such exploratory efforts!