Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model

Hyeonseok Moon, Taemin Lee, Jaehyung Seo, Chanjun Park, Sugyeong Eo, Imatitikua D. Aiyanyo, Jeongbae Park, Aram So, Kyoungwha Ok and Kinam Park
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Hyeonseok Moon: Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
Taemin Lee: Human Inspired Artificial Intelligence Research (HIAI), Korea University, Seoul 02841, Korea
Jaehyung Seo: Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
Chanjun Park: Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
Sugyeong Eo: Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
Imatitikua D. Aiyanyo: Human Inspired Artificial Intelligence Research (HIAI), Korea University, Seoul 02841, Korea
Jeongbae Park: Human Inspired Artificial Intelligence Research (HIAI), Korea University, Seoul 02841, Korea
Aram So: Human Inspired Artificial Intelligence Research (HIAI), Korea University, Seoul 02841, Korea
Kyoungwha Ok: AI Data Business Operation, Bizspring, Seoul 04788, Korea
Kinam Park: Human Inspired Artificial Intelligence Research (HIAI), Korea University, Seoul 02841, Korea

Mathematics, 2022, vol. 10, issue 10, 1-12

Abstract: Return on advertising spend (ROAS) refers to the ratio of revenue generated by advertising projects to its expense. It is used to assess the effectiveness of advertising marketing. Several simulation-based controlled experiments, such as geo experiments, have been proposed recently. This refers to calculating ROAS by dividing a geographic region into a control group and a treatment group and comparing the ROAS generated in each group. However, the data collected through these experiments can only be used to analyze previously constructed data, making it difficult to use in an inductive process that predicts future profits or costs. Furthermore, to obtain ROAS for each advertising group, data must be collected under a new experimental setting each time, suggesting that there is a limitation in using previously collected data. Considering these, we present a method for predicting ROAS that does not require controlled experiments in data acquisition and validates its effectiveness through comparative experiments. Specifically, we propose a task deposition method that divides the end-to-end prediction task into the two-stage process: occurrence prediction and occurred ROAS regression. Through comparative experiments, we reveal that these approaches can effectively deal with the advertising data, in which the label is mainly set to zero-label.

Keywords: deep learning; artificial intelligence; return on advertising spend; task decomposition; prediction model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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