Online business is one of the most prosperous areas of commercial activity. This is due to the development of technologies that allow easy organization of online stores and various external factors. Corporations also often use this area to generate additional income by distributing their merchandise. However, even in the case of massive organizations like Google, online stores are not organized in an ideal way. Several technical and economic nuances can be introduced into the online business of these corporations to improve it and generate more income. This paper aims to analyze possible recommendations for improving the Google Merchandise Store.
Personalization and Adaptation
One of the essential advantages of an online business is the ability to process digital data. Online resources can be tailored to the user, providing them with precisely the goods and services they are looking for. Google, as a corporation, has many resources that can be brought to bear on improving online business. First of all, the organization has a platform such as Google Analytics, which allows them to collect and use a massive amount of data to improve the user experience by tailoring it to the needs of a particular person (O’Brien, n.d.). In addition, the Google Merchandise Store can also be linked to a person’s search queries to display the unique products on the first page that match the user’s wishes. Such a strategy will reduce search time by linking user-friendly services, ultimately increasing sales efficiency.
Product visualization is an extremely important parameter since a person perceives information through the provided images in many ways. Therefore, promoted brands should capture the user’s attention and give them all the information to form their solution (O’Brien, n.d.). However, at the moment, one of the sections of the Google Merchandise Store has a significant flaw in this area. While some products can be comprehensively represented by a series of simple images of objects lying on a solid background, this is not the most beneficial approach in the context of clothing. Although the user often can see the critical elements of a particular item of clothing, they do not have the opportunity to know how this item looks on a living person.
At the moment, the apparel section of the Google Merchandise Store doesn’t show any human models, making it difficult to visualize the clothes. This aspect can be replaced by an actual human model or a 3D visualization that allows users to view a piece of clothing from all sides in a virtual space. This approach will enable people to get more information about the products they are interested in, which ultimately can increase the likelihood of a successful purchase.
One of the characteristics of Google as a corporation is its extremely high level of technology. Therefore, there is an opportunity to use these company resources to increase online business efficiency. At the moment, a general approach is machine learning, which processes user data and allows for predicting future revenue (Azizi & Hu, 2019). Such data allows quickly analyzing the store’s current state and adjusting to it, thereby achieving an optimal condition. Although most of these approaches rely on automation, studies show that semi-automated systems provide more accurate predictions (Ye et al., 2019). Thus, the use of such predictive algorithms within the Google Merchandise Store is possible due to the company’s extensive resources and can positively affect the level of sales.
Therefore, three main recommendations were put forward to improve the Google Merchandise Store online business. First, using measures to personalize the store to the user’s needs is proposed, linking the site to analytical platforms and search queries. Secondly, more accurate visualization measures are needed, especially for clothes. Finally, it is suggested to use the company’s resources to implement machine learning technologies with predictive functions to analyze the current state of the business and make decisions based on the data collected. The combination of these solutions, coupled with the use of the resources of Google as a company, can bring significant positive results.
Azizi, V., & Hu, G. (2019). Machine learning methods for revenue prediction in Google Merchandise Store. In Hui Yang, et al. (Eds.), INFORMS international conference on service science (pp. 65-75)., Cham, Switzerland: Springer.
O’Brien, S. (n.d.). Ecommerce merchandising 101: The ultimate condensed playbook. Web.
Ye, Z., Feng, A., & Gao, H. (2019). Prediction of customer purchasing power of Google Merchandise Store. In Jianxin Li, et al. (Eds.), International conference on advanced data mining and applications (pp. 839-852)., Cham, Switzerland: Springer.