Next Article in Journal
Developed Gorilla Troops Technique for Optimal Power Flow Problem in Electrical Power Systems
Next Article in Special Issue
Enterprise Profitability and Financial Evaluation Model Based on Statistical Modeling: Taking Tencent Music as an Example
Previous Article in Journal
Fisher, Bayes, and Predictive Inference
Previous Article in Special Issue
A Panel Threshold Model to Capture the Nonlinear Nexus between Public Policy and Entrepreneurial Activities in EU Countries
Article

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

1
Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
2
Human Inspired Artificial Intelligence Research (HIAI), Korea University, Seoul 02841, Korea
3
AI Data Business Operation, Bizspring, Seoul 04788, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Adriana Davidescu and Friedrich Schneider
Mathematics 2022, 10(10), 1637; https://doi.org/10.3390/math10101637
Received: 17 March 2022 / Revised: 18 April 2022 / Accepted: 24 April 2022 / Published: 11 May 2022
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. View Full-Text
Keywords: deep learning; artificial intelligence; return on advertising spend; task decomposition; prediction model deep learning; artificial intelligence; return on advertising spend; task decomposition; prediction model
Show Figures

Figure 1

MDPI and ACS Style

Moon, H.; Lee, T.; Seo, J.; Park, C.; Eo, S.; Aiyanyo, I.D.; Park, J.; So, A.; Ok, K.; Park, K. Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model. Mathematics 2022, 10, 1637. https://doi.org/10.3390/math10101637

AMA Style

Moon H, Lee T, Seo J, Park C, Eo S, Aiyanyo ID, Park J, So A, Ok K, Park K. Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model. Mathematics. 2022; 10(10):1637. https://doi.org/10.3390/math10101637

Chicago/Turabian Style

Moon, Hyeonseok, Taemin Lee, Jaehyung Seo, Chanjun Park, Sugyeong Eo, Imatitikua D. Aiyanyo, Jeongbae Park, Aram So, Kyoungwha Ok, and Kinam Park. 2022. "Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model" Mathematics 10, no. 10: 1637. https://doi.org/10.3390/math10101637

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop