Using machine learning to determine drivers of bounce and conversion
Google partnered with SOASTA to train a machine-learning model on a large sample of real-world performance, conversion, and bounce data. Patrick Meenan and Tammy Everts offer an overview of the resulting modelable to predict the impact of performance work and other site metrics on conversion and bounce rates.
| Talk Title | Using machine learning to determine drivers of bounce and conversion |
| Speakers | Patrick Meenan (Facebook), Tammy Everts (SpeedCurve) |
| Conference | Velocity |
| Conf Tag | Build resilient systems at scale |
| Location | Santa Clara, California |
| Date | June 21-23, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
There has been a lot of historical work to look at the relationship between performance and conversions, but most of it has been after the fact or relied on linear models. Recently, Google partnered with SOASTA to train a machine-learning model on a large sample of real-world performance, conversion, and bounce data. Patrick Meenan and Tammy Everts offer an overview of the resulting model—able to predict the impact of performance work and other site metrics on conversion and bounce rates and answer questions like: The code used to generate the model is freely available. Patrick and Tammy demonstrate how to make the most use of it with your own performance data.
