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A new uncertain multi-objective programming model with chance-entropy constraint for advertising promotion.
Meiling Jin1, Fengming Liu2, Shize Ning3
1School of Management, Shanghai University, Shanghai, 200444 China.
Journal of Ambient Intelligence and Humanized Computing
|June 8, 2023
Summary
This study introduces a model for optimizing social media advertising. It helps advertisers maximize engagement and minimize costs by selecting Key Opinion Leaders (KOLs) for promotions.
Area of Science:
- Marketing
- Data Science
- Operations Research
Background:
- The COVID-19 pandemic shifted communication and consumption online, increasing reliance on social media and e-commerce.
- Effective online advertising promotion on social media is crucial for businesses.
Purpose of the Study:
- To develop a multi-objective uncertain programming model for optimizing social media advertising promotion.
- To determine optimal Key Opinion Leader (KOL) selection strategies to maximize engagement and minimize costs.
Main Methods:
- Formulated a multi-objective uncertain programming model with advertiser as decision-maker.
- Introduced a novel chance-entropy constraint combining entropy and chance constraints.
- Transformed the model into a clear single-objective model via mathematical derivation and linear weighting.
Main Results:
- Validated the model's practicality and effectiveness through numerical simulations.
- The developed model provides a framework for data-driven advertising decisions.
- Identified key factors influencing optimal KOL selection for online promotions.
Conclusions:
- The proposed model offers a robust approach to enhance online advertising effectiveness.
- Provides actionable insights for advertisers to navigate social media marketing challenges.
- Supports strategic decision-making in digital marketing campaigns.
Keywords:
Advertising promotionChance-entropy constraintKey Opinion Leader (KOL)Uncertain multi-objective programmingUncertain variablesMore Related Videos
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