A variety of business processes can be represented and captured as event sequences, especially as product instrumentation becomes more comprehensive. However, low-level event data are challenging to wrangle and model, so analytics professionals typically aggregate data before visualization and estimation. In this talk I provide an overview of Sequence Analytics, an approach that embraces event sequences as a native data model. I introduce an expressive set of operations which can be performed on a sample of event sequences and show why these operations are challenging to express and to compute using SQL queries. I briefly describe some interesting implementation details, such as using state machines to reduce dimensionality for sequential data, and provide examples of visualizations and analyses.
Sean J. Taylor is co-founder and chief scientist at Motif Analytics. Previously he was a data scientist and head of Lyft's Rideshare Labs and spent seven years as a research scientist on Facebook's Core Data Science team. Sean's work is at the intersection of experimentation and causal inference, with a focus on applied problems and generating business value using the latest methods. He earned his...