Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score =. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries.
Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. However, unstructured data scattered across social media make detection a nontrivial task. In addition, HMDM and AAI are also proposed for firms to optimise FCMP.Īrtificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. From a managerial perspective, guidelines are provided for marketers to adopt advance technologies, such as AI, to optimise FMAC and HMDM to achieve competitive marketing performance.īelieving that “how to be competitive in marketing performance under data-rich-environment”, this research is the first to use the data of a firm manager to facilitate the understanding of FMAC, which provides a new direction for improving marketing performance. This study analyses how FMAC can enhance FCMP and contributes to resource-based views and technological capability theories. Moreover, adoption of artificial intelligence (AAI) enhances the relationship of FMAC-HMDM and FMAC-FCMP linkages. Multivariate analysis results show that FMAC significantly influences firms' competitive marketing performance (FCMP) with the presence of holistic marketing decision-making (HMDM) as a mediator. Structural equation modelling with maximum-likelihood estimation method was applied to verify the validity of the proposed research model. Furthermore, this research performed an empirical study by using operationalised questionnaire survey method to verify the hypotheses and reach its theoretical and managerial implications. This study analysed the data from 250 managers amongst large and medium-sized manufacturing and service-intensive firms. Thus, this study aims to develop and test a conceptual model that relates FMAC and its repercussions in the data-rich business environment. In spite of its significance, there is scant attention to conceptualising and empirically investigating FMAC and its consequences in a data-driven business context. The feedback from the managers who participated in the budget allocation process confirmed the utility of this approach.įew well-documented studies have explained the importance of researching firms' marketing analytics capability (FMAC). multichannel company producing scientific, biotechnology cameras. (4) This study uses a real-world case study approach to implement the proposed model in a U.S. (3) It includes operating costs and customer perspectives together to evaluate the channel structures. (2) The model is flexible to include omnichannel options as an alternative (e.g., buy-online-pickup-in-store). The prescriptive model proposed in this research, therefore, contributes to filling the literature gap in four main areas: (1) It incorporates market segment, customer wants, channel operating costs, and interrelationships among channels for effective resource allocation decisions. In addition, we have validated the model by implementing it in a multichannel company. A new resource allocation metric is proposed to measure the value of each channel structure in a company with multiple channels. The purpose of this paper is to explore the managerial implications of a customer-driven resource allocation methodology in multichannel retailing.
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