Maximizing Efficiency in Marketing Planning: Artificial Neural Network Regression and Data Imputation for Improving Business Forecasting

Marketing planning plays an important role in the success of any business in general. The ability to predict future earnings tends to allow us to make effective decisions and plan actions. These will help us work more smoothly in the future. However, traditional methods such as Linear Regression may...

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Bibliographic Details
Published in:	2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE) pp. 409 - 414
Main Authors:	Aonpong, Panyanat, Thipbumrung, Ratchai, Muangon, Weenawadee, Wongtaweesap, Opas
Format:	Conference Proceeding
Language:	English
Published:	IEEE 28-06-2023
Subjects:	Computer science data imputation Data models Deep learning Linear regression Machine learning neural network regression Planning Predictive models Training
Online Access:	Get full text
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Summary:	Marketing planning plays an important role in the success of any business in general. The ability to predict future earnings tends to allow us to make effective decisions and plan actions. These will help us work more smoothly in the future. However, traditional methods such as Linear Regression may limit the accuracy of predictions for various reasons. To fix this problem, we propose neural network regression with enhanced pseudo-input, working in tandem with business-predictive data models to fill in the missing information. The proposed approach involves training the model using datasets from 3 and 7 days with some missing data through deep learning regression to obtain more accurate prediction results. Comparing our proposed method with the classical linear regression method, our proposed method provided us with higher performance as evidenced by reduced losses.
ISSN:	2642-6579
DOI:	10.1109/JCSSE58229.2023.10202144