| Abstract |
In the era of globalization and the Internet, due to the unprecedented business pressure from competition, in order to make long-term benefits and achieve sustainable development, enterprises should select competitive markets for their products to maximize the benefits of limited resources. The key to corporate survival and development is to find the most profitable customer bases all over the world and develop products to meet their demands. Traditionally, market targeting relies on the decisions of a small number of senior managers in enterprises; however, due to novel and changing customer demands, the business environment has become increasingly complex. If previous traditional methods are adopted, enterprises may select the wrong markets, which can lead to complete destruction. Therefore, this study proposes a new market targeting method and replaces human decisions with artificial intelligence (AI) algorithms, in order to render market targeting more scientific and systematic, improve the quality of marketing decisions, maximize corporate profits, occupy the optimal market with limited resources, and achieve the goal of sustainable business. This study applied three AI algorithms, the naive Bayes algorithm, J48 algorithm, and OneR algorithm, for model training and analytical prediction of the testing datasets. According to the results, the model accuracies of the naive Bayes algorithm, J48 algorithm, and OneR algorithm are 100, 91.7, and 83.3%, respectively; the F-measures of the naive Bayes algorithm, J48 algorithm, and OneR algorithm are 1, 0.909, and 0.8, respectively, which indicates that the three algorithms have reliable predictions. The results show that AI algorithms can help enterprises in market targeting. |