Actionable Big Data: Optimizing Ad Campaigns to Maximize Brand Lift

In this talk, we’ll be talking about how true[X] is leveraging its data to increase the effectiveness of its ads for its advertising clients, as well as sharing some battle-hardened tips about working with operationalizing big data sets. Expect equal parts of data engineering and data science.

Speakers: Chris Johnson and Binbin Li

Chris: Since joining true[X] as one of the earliest engineers in 2008, Chris knew he was joining a special company. The vision was clear and ambitious: Fix brand advertising online. Throughout the journey of executing on that vision, Chris has been instrumental in architecting and iterating on the core systems that power true[X]. As the Vice President of Engineering at true[X], Chris and his team are responsible for the ad serving technology suite: several cooperative systems that include campaign management and reporting, data processing and analytics, and an ad server built from the ground up to meet the unique needs of an engagement-focused ad platform. Before joining true[X], Chris was the Ad Server Architect and Lead Software Engineer at Intermix Media (acquired by Fox Interactive Media). He holds a degree in Computer Science from the University of California, Santa Barbara.

Binbin: Binbin Li is the Director of Data Science at true[X]. In this role, he built a team of very talented data scientists and leads the efforts to develop machine learning models for better advertising. In particular, he and his team are responsible for building real-time predictive models to identify the right audience for the right advertisements and to optimize the performance of advertising campaigns measured by brand lift metrics. Prior to joining true[X], he worked at KPMG and SAS Institute, where he led the development of credit/debit card fraud detection models for some largest banks in the United States. Binbin holds a Ph.D. degree in Systems Engineering, with a focus on Optimization, from Boston University, and a Bachelor's degree in Automation from Tsinghua University in Beijing, China.

Dinner and drinks will be provided by GumGum.

Parking:

Santa Monica Library Parking Garage. An underground parking structure can be accessed from 7th Street between Santa Monica Blvd. and Arizona Ave. The first thirty minutes are free. Rates are $1 per hour for the first two hours and thirty minutes. After that, the rate is $1 per thirty minutes. Weekdays the daily maximum is $10. Weekends the daily maximum is $5. Parking on level P1 is restricted to stays of three hours or less. Parking spaces for those displaying disabled placards or plates are on P1. Visitors parking for three hours or longer should park on levels P2 or P3. The Library and GumGum do not provide validation for parking.

Street-level parking lot. A limited number of metered one-hour parking spaces are available. Entry to the lot is from 7th Street.

Meters. Located on the 7th street outside our office.