Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model
Abstract
:1. Introduction
2. Related Work
3. Proposed Method
3.1. Data Pre-Processing
3.1.1. Feature Extraction
3.1.2. Data Clustering
3.2. Prediction Model
Input Sequence Encoding
3.3. LSTM-Based Prediction Model
3.4. One-Stage Framework
3.5. Two-Stage Framework
3.5.1. Occurrence Prediction Model
3.5.2. Occurred ROAS Regression Model
3.5.3. Why Task Decomposition?
4. Experimental Results
4.1. Data Details
4.2. Training Details
4.3. Evaluation Details
4.4. Main Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Name | Description | Data Type | Class |
---|---|---|---|
stat_date | Dates of data collection | string | cluster |
adgroup | Ad group id of the data query | string | cluster |
ad_platform | Ad platform id of the data query | string | categorical |
ad_program | Ad program id of the data query | string | categorical |
device | Device that data is collected (Mobile or PC) | string | categorical |
impr | Ad dwell time of the customer | integer | numeric |
click | Number of clicks occurred by the customer | integer | numeric |
rgr | Number of “Sign in” occurred by the customer | integer | numeric |
odr | Number of orders | integer | numeric |
cart | Number of “Add to cart” occurred by the customer | integer | numeric |
conv | Number of conversion occurred by the customer | integer | numeric |
cost | Cost occurred by the customer | integer | numeric |
rvn | Revenue occurred by the customer | integer | numeric |
keyword | Keyword used in searching ad | string | keyword |
Train | Validate | Test | |
---|---|---|---|
# of data points | 334,963 | 7807 | 6980 |
# of zero labels | 324,672 | 7483 | 6745 |
# of non-zero labels | 10,291 | 324 | 235 |
# of ad groups | 6764 | 141 | 141 |
MSE | MAE | Non-Zero MSE | Non-Zero MAE | Mapped F1-Score | Mapped Precision | Mapped Recall | |
---|---|---|---|---|---|---|---|
One-stage | 0.68400 | 0.11545 | 21.64975 | 2.81465 | 0.00513 | 0.00271 | 0.04782 |
One-stage + Up-scaling | 0.72601 | 0.39744 | 19.30089 | 2.32553 | 0.06116 | 0.03155 | 1.00000 |
Two-stage | 0.68830 | 0.12501 | 19.66112 | 3.23538 | 0.27578 | 0.17062 | 0.71875 |
Two-stage + Up-scaling | 0.81438 | 0.26652 | 11.72011 | 2.02405 | 0.40642 | 0.25850 | 0.95000 |
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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
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 StyleMoon, 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