The Application of K-MEANS Algorithm-Based Data Mining in Optimizing Marketing Strategies of Tobacco Companies

With the continuous development of data mining technology, more and more industries are applying data mining techniques to optimize their marketing strategies. In response to the persistent decline in tobacco sales and the gradual erosion of customer base in a particular enterprise in recent years, this study employs data mining technology to enhance the current tobacco marketing strategy. Firstly, in response to the current shortcomings of the company, a marketing optimization design scheme was proposed and a customer classification evaluation index system was constructed. Subsequently, homomorphic encryption technology and enhanced peak density thinking were employed to enhance the conventional K-means algorithm. The enhanced algorithm was then utilized in the customer clustering and partitioning scheme, with the objective of investigating the underlying information present in customer consumption data. The performance of the algorithm was tested, and the results showed that the mean square error of the improved K-means algorithm was about 0.1, with an average absolute error of about 0.05. The highest detection rate in the validation set was 0.95, and the lowest false alarm rate was 0.07. Both experts and customers were highly satisfied with the marketing strategy under the enhanced K-means algorithm. In summary, the clustering analysis method used in this study can effectively uncover the hidden value behind various types of customer data, thereby helping companies to make better marketing strategies.

@article{Ma2024,
  title     = {The Application of K-MEANS Algorithm-Based Data Mining in Optimizing Marketing Strategies of Tobacco Companies},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mingqian Ma},
  doi       = {10.14569/IJACSA.2024.0151186},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151186}
}