AI For Growth: Improving Marketing Attribution With Machine Learning (Max Sklar, Foursquare)

33:29
 
Share
 

Manage episode 209910884 series 2350489
By Discovered by Player FM and our community — copyright is owned by the publisher, not Player FM, and audio streamed directly from their servers.
Want to learn how to drive business ROI for your company with data science, machine learning, and artificial intelligence? Watch our AI For Growth series: https://www.topbots.com/ai-for-growth/ TOPBOTS executives Mariya Yao and Marlene Jia interview top technology leaders from global companies to learn how they’ve successfully applied modern automation techniques to improve sales, marketing, product, and customer experience. Learn winning strategies from executives who have adopted AI for their enterprises and bring them back to your company. Today we speak with Max Sklar, Head of Machine Learning Attribution at Foursquare. User attribution, especially between offline and online worlds, is a persistent challenge for marketers. Using novel machine learning techniques on top of Foursquare’s incredible data trove of consumers’ physical behavior, Max enables enterprise customers to identify when online campaigns have driven offline revenue and convert real-world foot traffic into long-term digital customers and social media fans. Read the transcript and summary of this interview on TOPBOTS: https://www.topbots.com/improving-marketing-attribution-machine-learning-interview-max-sklar-foursquare/ TOPBOTS educates business leaders on high-impact applications of modern machine learning and AI techniques and helps leading organizations adopt and implement emerging technologies. We run the largest publication and community for enterprise AI professionals to learn about the latest machine learning and automation solutions and exchange insights with each other. Through education and community, we inspire you to think creatively about how AI can be used to improve lives, revolutionize industries, and build a better society – but only if we use it wisely.

5 episodes available. A new episode about every day averaging 30 mins duration .