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Activity Number: 341 - SPEED: Classification and Data Science
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #330707
Title: Lookalike Audience Modeling
Author(s): Sam Hawala*
Companies: Resonate-Networks
Keywords: Marketing Analytics; Singular Value Decomposition; Cosine Similarity

Lookalike Audience Modeling (LAM) is a marketing analytics methodology used to ultimately generate more leads and increase their conversions. The algorithm discovers similarities between internet users based upon the domain level URLs that they've visited. After a dimensionality reduction step through SVD, we calculate cosine-similarities between users of interest and users at large, based on internet behavior during a fixed period of time. We show that the recall rate is over 90%. We also present clients' key performance indicators.

Authors who are presenting talks have a * after their name.

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