This study is dedicated to evaluating the impact of digital inbound marketing in achieving sustainable competitive advantage in the machine-made carpet industry. The innovation of the research is in examining the effect of digital inbound marketing in achieving sustainable competitive advantage, which has not been done before, especially in the carpet industry. To identify the effective factors, research conducted domestically and internationally was reviewed, and the most important factors affecting digital inbound marketing in order to achieve sustainable competitive advantage in the machine-made carpet industry were classified into 16 sub-criteria including: digital products and services, digital operation process, digital business model, redundancy, digital marketing visibility, situational awareness, sound risk management decisions, digital marketing collaboration agility, recovery efficiency, contingency planning, knowledge management, operating costs, return on investment, time between the start and completion of the production process, and customer satisfaction. These factors are grouped into 5 main criteria including digital inbound marketing, absorbability, responsiveness, retrieval capability, and digital inbound marketing performance, and are analyzed in Savin Machine Carpet Company. In the first stage, 30 experts with relevant expertise and experience were identified and a questionnaire including factors affecting digital inbound marketing to gain sustainable competitive advantage was designed and validated. This questionnaire led to the confirmation of all criteria. Then, the impact of these factors on digital inbound marketing was examined and the results of the KMO test were equal to 0.748, which indicates the suitability of the correlations for factor analysis. In the next stage, the correlation between the variance of the variables in question and other variables was evaluated. Finally, factor analysis of the research questionnaire was presented and the standard factor loading to examine the strength of the relationship between each factor (latent variable) and the observable variables (questionnaire items) was obtained in other cases, except in the cases of correct risk management and knowledge management decisions, which were greater than 0.3. Thus, the factor structure of the questionnaire was confirmed. Based on the results of factor loading analysis, it can be seen that recovery efficiency is the most effective factor, followed by emergency planning. After that, collaboration, then visibility and redundancy are considered as the most important and influential factors.