  The current work characterizes the users of a VoD streaming space through
user-personas based on a tenure timeline and temporal behavioral features in
the absence of explicit user profiles. A combination of tenure timeline and
temporal characteristics caters to business needs of understanding the
evolution and phases of user behavior as their accounts age. The personas
constructed in this work successfully represent both dominant and niche
characterizations while providing insightful maturation of user behavior in the
system. The two major highlights of our personas are demonstration of stability
along tenure timelines on a population level, while exhibiting interesting
migrations between labels on an individual granularity and clear
interpretability of user labels. Finally, we show a trade-off between an
indispensable trio of guarantees, relevance-scalability-interpretability by
using summary information from personas in a CTR (Click through rate)
predictive model. The proposed method of uncovering latent personas, consequent
insights from these and application of information from personas to predictive
models are broadly applicable to other streaming based products.
