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Activity Number: 701
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Marketing
Abstract #321127 View Presentation
Title: Using Random Forest to Create Adstock Predictors
Author(s): Kathleen Bell* and Scott Dachtyl and Rob Howie
Companies: LB Personifi and LB Personifi and Hallmark
Keywords: Random Forest ; Advertising ; Adstock ; Variable Reduction ; variable importance
Abstract:

A common technique in modeling is to use predictors related to recency and frequency of past behavior. In a non-time series context, the scale of such variables is limited by several constraints - in many cases the p:n ratio can be inappropriately lopsided and large p models can put a significant strain on computing resources. We introduce a novel adstock technique that uses random forest to collapse large series of past behavior into single predictors. The adstock technique preserves a large amount of the information otherwise present in a long string of variables and can function as a powerful single predictor. This methodology can be used to harness the full range of past individual behavior in models.


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

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