Amazon is the world’s largest online retailer and a cloud service provider which was established in 1995 by Jeff Bezos (Majed et al., 2018). Customer-centricity has been stressed as one of the company’s basic pillars ever since its start. Knowing your client in a digital context is one of the key ideas in the customer data dynamics of the company (McKeown & Durkin, 2016). The internet was just beginning to take off and had enormous potential when Amazon was founded. Bezos saw this opportunity and made the choice to seize it quickly. Amazon employed the three sub-concepts of customer data dynamics and volume in a number of ways using the idea of understanding the customer.
When Amazon first opened for business, there was a big demand for books. A physical bookshop had a large number of customers, while an online bookstore was not yet available, indicating a smaller number of customers in that area. Amazon, though, was aiming for the low-volume sector. Entering a low-volume market can be accomplished by creating a demand that does not currently exist because customers are unaware of the invention. Customers received a service from Amazon that they had no idea they required. Majed et al. (2018) said that this was how Amazon viewed innovation. Through innovation and a customer-centered approach, Amazon was able to compete successfully with physical stores that had previously benefited from a number of market advantages (Majed et al., 2018).
Amazon has steadily and systematically established its business success. The company has achieved great success because of its capacity to identify market trends and diversify its business. Amazon builds and improves its recommendation engine using Big Data collected from customers as they explore. The organization can control what customers wants to buy if it has more information about the customers’ behavior. Additionally, once the shop is aware of what the client might desire, it can make persuading them to buy it easier, such as by suggesting other items rather than having the client browse the entire catalog. Collaborative filtering is the foundation of Amazon’s recommendation engine system. It means that the system determines what it thinks the customer wants by creating a profile of who the customer is and then showing them things that people with similar profiles have bought (Hewage et al., 2018). While users are on the site, Amazon collects information about each and every one of them. Along with tracking your purchases, the business keeps track of an individual’s browsing activity, mailing address (from which Amazon may infer the customer’s household income with surprising accuracy), and whether the individual writes reviews or feedback.
By modifying the website and its underlying algorithms, Amazon can show customers the products that are most likely to meet their needs. It uses this information to develop a customized recommendation system. The thorough, collaborative filtering engine (CFE) on Amazon maintains track of similar books that other customers have bought and suggests the same to you for purchase (Hewage et al., 2018). Always putting the needs of the client first, Amazon started off by offering the greatest possible customer service. It utilized big data by gathering, enhancing, and analyzing it to enhance the performance of the site and the back-end procedure in order to accomplish this purpose. The effective utilization of big data by Amazon is the best illustration of the potential of data science. This also explains why the business is always looking to hire the top data scientists.
Hewage, T. N., Halgamuge, M. N., Syed, A., & Ekici, G. (2018). Big Data Techniques of Google, Amazon, Facebook and Twitter. J. Commun., 13(2), 94-100.
Majed, S. Z., Nuraddin, S. H., & Hama, S. V. S. (2018). Analyzing the amazon success strategies. Journal of Process Management. New Technologies, 6(4), 65-69. Web.
McKeown, N., & Durkin, M. (2016). The Seven Principles of Digital Business Strategy. Reed Business Education.