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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hyeonseok Moon & Taemin Lee & Jaehyung Seo & Chanjun Park & Sugyeong Eo & Imatitikua D. Aiyanyo & Jeongbae Park & Aram So & Kyoungwha Ok & Kinam Park, 2022. "Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model," Mathematics, MDPI, vol. 10(10), pages 1-12, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1637-:d:813271
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    References listed on IDEAS

    as
    1. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
    2. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers 2104.06259, arXiv.org.
    3. Mihai C. ORZAN & Adina I. ZARA & Stefan C. CAESCU & Mihaela E. CONSTANTINESCU & Olguta A. ORZAN, 2021. "Social Media Networks as a Business Environment, During COVID-19 Crisis Tunisia and Turkey," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 22(1), pages 64-73, January.
    Full references (including those not matched with items on IDEAS)

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