Using NLP & Machine Learning to understand and predict performance

Title: Using NLP and Machine Learning to understand and predict web page quality and conversion performance

Abstract:

As the world leader in providing tools to create landing pages and drive conversions for marketing teams and agencies, Unbounce is constantly striving to give our customers the best information we can so they can make data-driven decisions to design and target their pages. As a step along that path, we can ask a simple question for a webpage dedicated to getting people to convert (i.e. fill out a form and click):

What’s a good conversion rate?

The answer, is that it depends on the page and what that page is about. Utilizing techniques in Natural Language Processing and Machine Learning, we will show how to automatically categorize pages, use this categorization to find the most similar pages, and eventually answer that question.

Bio:

Thomas Levi (https://twitter.com/tslevi) started out with a doctorate in Theoretical Physics and String Theory from the University of Pennsylvania in 2006. His post-doctoral studies in cosmology and string theory, where he wrote 19 papers garnering 650+ citations, then took him to NYU and finally UBC. In 2012, he decided to move into industry, and took on the role of Senior Data Scientist at POF. In 2015, he became Director of Data Science at Unbouce. Thomas has been involved in diverse projects such as behaviour analysis, social network analysis, scam detection, Bot detection, matching algorithms, topic modelling and semantic analysis.

Schedule:

• 6:00PM Doors are open, feel free to mingle
• 6:30 Presentation start
• ~7:45 Off to a nearby restaurant for food, drinks, and breakout discussions

Getting There:

By transit there a number of high frequency buses (check Google Maps or the Translink site for your particular case) that will get you there. For the drivers, there is a fair bit of street parking (free and pay) in the area, especially after 6.