Heuristic Simulated Annealing Modeling to Optimum Target Audience Identification in Digital Marketing: A Case Study of a Mining Industry Training Service Company

Document Type : Research Paper


1 School of Mining, College of Engineering, University of Tehran, Tehran, Iran

2 Payame Noor University


Digital marketing has become vital to businesses' marketing strategies in today's technology and social media era. However, the effectiveness of digital marketing campaigns largely depends on accurately identifying the target audience. This study aims to implement the simulated annealing initiative algorithm for digital marketing, as well as audience classification and optimum target audience selection. Traditional methods of target audience identification, such as demographic, geographic, and psychographic segmentation, are only sometimes effective in identifying the most responsive audience. Therefore, advanced techniques such as clustering, genetic, and simulated annealing algorithms have been proposed to identify the optimum target audience. The heuristic simulated annealing algorithm is one of the most promising techniques for optimum target audience identification. It is widely used in combinatorial optimization problems and applied in various fields such as engineering, economics, management, and computer science. In this research, a digital marketing campaign is implemented for a new line to sell training courses in empowerment and competency in human resource management within the mining industry. After conducting market research, we have identified five critical segments: age, gender, income group, place of residence, and level of university education. The number of customers at each customer journey stage was 740 people in brand development, email, and advertising campaigns, of which 620 people are in the "Awareness" stage, 431 people in the "Interest" stage, 261 people in the "Consideration" stage, 203 people in the "Intend" stage, 179 people in the "Purchase" and finally, 179 People were evaluated in the "loyalty" stage for the case of educational service company. The results show we should target 20% of our marketing efforts towards the 18-24 age group, 30% towards females, 20% towards high-income individuals, 10% towards rural areas, and 20% towards University education level in BSc. The best cost per conversion we obtain is 78.105×106 Rials. The results show that the simulated annealing algorithm can be valuable for identifying the optimum target audience in digital marketing campaigns. By considering the entire customer journey and allowing for more complex audience targeting, the algorithm can help companies optimize their marketing strategies and maximize their profits.


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