Reut Nissan Rosen

iOS 14.5 Era – new challenges requires creative solutions

Reut Nissan Rosen

Reut Nissan Rosen

iOS 14.5 Era – new challenges requires creative solutions

Reut Nissan Rosen

Bio

Head of Product Data Science at Lightricks. I Hold an MSc in Statistics and a BSc in Statistics and Economics from the Hebrew University. I love data and I am a big fan of creating impact using ML skills. A correct combination of data points is our strongest source of information and the best way to tell a story.

Bio

Head of Product Data Science at Lightricks. I Hold an MSc in Statistics and a BSc in Statistics and Economics from the Hebrew University. I love data and I am a big fan of creating impact using ML skills. A correct combination of data points is our strongest source of information and the best way to tell a story.

Abstract

As iOS 14.5 came into our lives, many attribution questions arose. Information regarding our spend efficiency per network, which was used for optimizing marketing campaigns, is not available anymore. Today, it isn’t easy to make meaningful and accurate decisions on spending ads budget effectively. Every company that works with mobile apps in iOS has felt the dramatic change this version made to the marketing. This session will discuss the challenges with lack of attribution, share ideas and ways to approach the problem, point out ML methods for overcoming this issue, and raise options and methods for evaluating our attribution models with no “ground truth.”

Abstract

As iOS 14.5 came into our lives, many attribution questions arose. Information regarding our spend efficiency per network, which was used for optimizing marketing campaigns, is not available anymore. Today, it isn’t easy to make meaningful and accurate decisions on spending ads budget effectively. Every company that works with mobile apps in iOS has felt the dramatic change this version made to the marketing. This session will discuss the challenges with lack of attribution, share ideas and ways to approach the problem, point out ML methods for overcoming this issue, and raise options and methods for evaluating our attribution models with no “ground truth.”

Planned Agenda

8:45 Reception
9:30 Opening words by WiDS TLV ambassadors Or Basson and Noah Eyal Altman
9:40 Dr. Kira Radinski - Learning to predict the future of healthcare
10:10 Prof. Yonina Eldar - Model-Based Deep Learning: Applications to Imaging and Communications
10:40 Break
10:50 Lightning talks
12:20 Lunch & Poster session
13:20 Roundtable session & Poster session
14:05 Roundtable closure
14:20 Break
14:30 Dr. Anna Levant - 3D Metrology: Seeing the Unseen
15:00 Aviv Ben-Arie - Counterfactual Explanations: The Future of Explainable AI?
15:30 Closing remarks
15:40 End