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