One of the most important strategic tasks for retailers is to reach a wider range of target customers in order to make their businesses profitable in the long run. In the present-day world, it can be performed through the adoption of new methods, such as enhanced online activity. This decision is advantageous because it facilitates the process of transitioning to different platforms, and its success depends on the adoption of artificial intelligence systems. Therefore, social media is gradually changing regular operations in the retail business by providing more opportunities for companies to increase profits while benefiting from suitable technological solutions.
Capatina, A., Kachour, M., Lichy, J., Micu, A., Micu, A. E., & Codignola, F. (2020). Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations. Technological Forecasting and Social Change, 151, 119794. Web.
This article narrates about the conditions for the efficient use of artificial intelligence-based software by retailers who promote their products online. They are connected to the human factor or, in other words, customer feedback in different areas, which can help match the essential expectations of people with companies’ strategies. In other words, the introduction of technological solutions in social media should be made with respect to their attitudes (Capatina et al., 2020). According to this source, the capabilities of artificial intelligence tools are determined by the initial perception and their features alongside the audience, image, and sentiment (Capatina et al., 2020). The conducted experiment proves the importance of these elements for software developers. Hence, its purpose was to present an instrument, which allows projecting the results of innovative approaches. This source is useful for showing the importance of careful planning when retailing online regardless of solutions and, therefore, relates to other publications.
Oosthuizen, K., Botha, E., Robertson, J., & Montecchi, M. (2020). Artificial intelligence in retail: The AI-enabled value chain. Australasian Marketing Journal, 29(3), 264-273. Web.
This article compares the situation in the field of retail before the introduction of artificial intelligence software and after the adaptation of social media for this purpose. It proves that this change is inevitable and states that the described trends increase competition among businesses (Oosthuizen et al., 2020). Meanwhile, as per this publication, it is not easy to establish the connection between companies and their potential buyers via online platforms because this practice is relatively new (Oosthuizen et al., 2020). It means that this task can be performed only if managers address the following aspects: gaining knowledge about technological solutions, assessing their suitability, and communicating with people. From this point of view, artificial intelligence systems used on social media for retail are not efficient if companies ignore these needs. This source complements the above publication, which highlights the importance of planning by specifying the areas, which should be analyzed.
Roggeveen, A. L., & Sethuraman, R. (2020). Customer-interfacing retail technologies in 2020 & beyond: An integrative framework and research directions. Journal of Retailing, 96(3), 299-309. Web.
This article is about the difference in the emerging artificial intelligence tools for increasing the efficiency of online retail. According to the authors, this task requires matching the needs of companies with the appropriate programs, which correspond to them (Roggeveen & Sethuraman, 2020). For example, if they are interested in drawing the attention of customers to their products, they should use search engagement technologies, whereas the process of purchasing is performed via transaction or acquisition instruments (Roggeveen & Sethuraman, 2020). In addition, there are solutions, which offer follow-up services for buyers, and they are no less important than other software (Roggeveen & Sethuraman, 2020). The conducted analysis, which is the purpose of the article, shows that it is critical to classify programs in order to benefit from them (Roggeveen & Sethuraman, 2020). This article underpins other sources by emphasizing the need to carefully select programs for retail businesses and customers.
Quijada, M. D. R. B., Arriaga, J. L. D. O., & Domingo, D. A. (2021). Insights into user engagement on social media. Findings from two fashion retailers. Electronic Markets, 31(1), 125-137. Web.
This article provides practical examples of the businesses, which are involved in social media activity for increasing their sales. Thus, the analysis of two fashion retailers is conducted to demonstrate their efficiency in Instagram (Quijada et al., 2021). Its purpose is to show the possible problems in this area and the methods of their elimination for future success. According to the findings, the principal challenges include little interaction with followers of their accounts and, consequently, inefficient communication (Quijada et al., 2021). It means that the suggested measures are to increase the engagement of the two parties in the process of promoting and realizing products and provide more information to customers. This evidence relates to other articles because it proves the need for careful planning and examination of features of social media platforms, which help determine their suitability for retailers’ goals.
Zhan, Y., Han, R., Tse, M., Ali, M. H., & Hu, J. (2021). A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry. Technological Forecasting and Social Change, 163, 120504. Web.
This article narrates about the need for improving online operations of retailers in social media in the pharmacy industry. According to it, the required measures should be applied to marketing initiatives, product development, and customer service (Zhan et al., 2021). In other words, the issues are connected to the inefficient promotion of offers and, consequently, their inadequate perceptions by potential buyers. From this point of view, the purpose of this article is to prove the importance of creating a new business model for operating in social media for increasing profits in the long run (Zhan et al., 2021). This conclusion is supported by the evidence from three organizations on Twitter: Boots, Lloyds, and Superdrug (Zhan et al., 2021). Even though their degree of success differs, the specified improvements are applicable to all of them. Thus, this article relates to other publications because it describes practical actions, which can be taken for improvements.
References
Capatina, A., Kachour, M., Lichy, J., Micu, A., Micu, A. E., & Codignola, F. (2020). Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations. Technological Forecasting and Social Change, 151, 119794. Web.
Oosthuizen, K., Botha, E., Robertson, J., & Montecchi, M. (2020). Artificial intelligence in retail: The AI-enabled value chain. Australasian Marketing Journal, 29(3), 264-273. Web.
Roggeveen, A. L., & Sethuraman, R. (2020). Customer-interfacing retail technologies in 2020 & beyond: An integrative framework and research directions. Journal of Retailing, 96(3), 299-309. Web.
Quijada, M. D. R. B., Arriaga, J. L. D. O., & Domingo, D. A. (2021). Insights into user engagement on social media. Findings from two fashion retailers. Electronic Markets, 31(1), 125-137. Web.
Zhan, Y., Han, R., Tse, M., Ali, M. H., & Hu, J. (2021). A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry. Technological Forecasting and Social Change, 163, 120504. Web.