Attention-based deep learning model to improving multi-criteria decision-making for customer loyalty

Abstract

Understanding the factors that influence product loyalty is crucial for businesses to effectively attract and retain customers. This study suggests a novel approach to assess the importance and weight of criteria that lead to product loyalty by considering the Halo effect in customer decision-making. The suggested method utilizes an attention-based deep learning model to analyze customer feedback collected through the Net Promoter Score (NPS) scale, incorporating the insights of a large number of customers. The proposed method overcomes the limitations of traditional methods that rely on expert judgments or data mining, providing a more comprehensive and customer-centric perspective. By considering the Halo effect, which can lead to biased perceptions of product features, the method offers a more accurate assessment of criteria weights and their impact on product loyalty. A case study focusing on mobile phone selection and loyalty is conducted to explain the applicability and efficiency of the suggested method. The outcomes are compared with the NPS index and several common multi-criteria decision-making (MCDM) techniques. The findings highlight the superiority of the suggested method in capturing the complex relationships between criteria and product loyalty, surpassing the limitations of expert-based approaches and outperforming traditional MCDM methods. The suggested technique provides valuable insights for companies seeking to enhance customer loyalty and optimize product development strategies. However, it is important to acknowledge limitations related to the reliance on customer feedback and the contextual specificity of the results.