To Bid or Not To Bid: Machine Learning in Ad Tech

Abstract: In Ad Tech real-time-bidding (RTB), decisions on whether and how much to bid for an incoming request (person who just opened a web browser) must be made quickly, frequently, and intelligently. Bid too little or not at all, and you will miss the opportunity to show this person a video advertisement on behalf of one of your clients. Bid too much, and you might win, but will it be profitable? That depends on whether the person engages with the video you show her, or not. And that likelihood is determined by machine learning.

Join Justin Fortier, Principal Data Scientist, and Michael Lubavin, Lead Software Engineer, to learn how ViralGains, based here in Boston, has leveraged the power of machine learning to optimize these decisions to help place targeted video advertisements to web users on behalf of our Fortune 500 clients. We will share both the data science and the software engineering / big data perspectives of how we are solving this business problem at scale in microseconds, and how we are measuring incremental performance from machine learning. We hope you can join us!

Bio: Justin Fortier is the Principal Data Scientist at ViralGains, a video advertising technology platform headquartered in downtown Boston. He is passionate about all things data science/ machine learning / deep learning / AI-related, and Justin has been leveraging advanced analytics and predictive modeling to drive explosive business growth in seven enterprises within six different industries over the past 25 years. He says ad tech is the most exciting and challenging of all the industries in which he's worked (Insurance; Retail; Food Services; E-mail Marketing for Small Businesses; Medical Products), and he is excited to share his passion with you at ODSC East this year.

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