Abstract
Traditional content marketing methods resort grossly to market requirements but barely obtain relatively accurate marketing prediction under loads of requirements. Machine learning-based approaches nowadays are widely used in multiple fields as they involve a training process to deal with big data problems. In this paper, decision tree-based methods are introduced to the field of content marketing, and decision tree-based methods intrinsically follow the process of human decision making. Specifically, this paper considers a well-known method, called C4.5, which can deal well with continuous values. Based on four validation metrics, experimental results obtained from several machine learning-based methods indicate that the C4.5-based decision tree method has the ability to handle the content marketing dataset. The results show that the decision tree-based method can provide reasonable and accurate suggestions for content marketing.
Highlights
IntroductionContent marketing is normally regarded as a management process responsible for identifying, anticipating, and satisfying customer requirements profitably in the context of content via electronic channels [1]. e designation of research methods in content marketing has received plenty of attention for more than 100 years
Four validation metrics, we conduct several important experiments on the bank content marketing dataset for the purpose of verifying the performances of C4.5 and other comparison methods. e main contribution of this paper is that we introduce a decision tree-based method to the content marketing field to efficiently improve the marketing capability
Among plenty of machine learning-based methods, decision tree has received more attention from content marketing managers as it intrinsically follows the process of human decision making
Summary
Content marketing is normally regarded as a management process responsible for identifying, anticipating, and satisfying customer requirements profitably in the context of content via electronic channels [1]. e designation of research methods in content marketing has received plenty of attention for more than 100 years. Content marketing strategies are determined based on different developments technologies, market requirements and expectations, growth in knowledge, and so on [2]. Machine learning-based methods have received more attention in the past several decades because they have the ability to analyze historical data and predicting future potential behaviors and activities more effectively [3, 4]. Due to the fast development of machine learning, the current content marketing strategies tend to be digital-driven, meaning that companies put more attention to the consumer concentration on the Internet or historical habitats [6]. In this regard, the data scale and dimensions become huge, meaning that the traditional marketing methods are not suitable. The data scale and dimensions become huge, meaning that the traditional marketing methods are not suitable. e major motivation of this paper is to investigate the potential of machine learning methods in dealing with the high dimensionality of attributes of marketing data
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