Journal Article

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

So, Aram, Aiyanyo, Imatitikua D., Ok, Kyoungwha, Moon, Hyeonseok, Seo, Jaehyung, Park, Chanjun, Eo, Sugyeong, Park, Kinam, Lee, Taemin, Park, Jeongbae

2022

Overview

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.

• Publisher: MDPI AG
• Subject: Advertising / artificial intelligence / Bias / Costs / Data acquisition / Data collection / Deep learning / Experiments / prediction model / Queries / return on advertising spend / Simulation / task decomposition / Time series / User behavior