Purpose-Customer acquisition and retention methods are the most critical issues for any enterprise. By identifying potential customers and targeting them through marketing activities, enterprises can minimize marketing costs and maximize transaction probability. However, because market surveys are labor- and time-consuming, and data mining is ineffective for obtaining competitor data, enterprises may be unable to understand real-time changes in market trends and consumer preferences. The paper aims to discuss these issues. Design/methodology/approach-This study developed a mechanism that automatically searches for potential customers in virtual communities. In addition, a common product attribute (CPA) model was developed based on the five dimensions of the theory of consumption values and a questionnaire survey was conducted to verify the corresponding relationships. Subsequently, the authors quantified and applied the relationship between the proposed CPA model and consumption values theory. Findings-During the experiment, functional and social values yielded more accurate predictions. Contrary to our expectations, emotional value yielded an inaccurate prediction of potential customers. The overall precision was 0.74, with a threshold of 0.5. Research limitations/implications-Due to each industry including the distinctive characteristics and attributes regarding its products, the methods and models were only adopted in food industry for testing effectiveness. Practical implications-Considering the food industry as an example, this study adopted the case study method to screen potential customers based on 400 articles from virtual communities, and combined a latent semantic analysis method with a backpropagation neural network to verify the effectiveness of the proposed method. Originality/value-By adopting the proposed enterprise-product profile model, enterprises can compile basic information related to their products and industry. The proposed system can be used by enterprises to identify potential customers in areas with potential for market development.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences