Introduction
In the world of advanced technologies, it is becoming easier to find a specific item to purchase. The newest search engines and analyzing machines facilitate the process by providing a buyer with multiple suggestions. However, there is a downside in the particular tools intended to make people’s life simpler and allow them to escape the mountains of unnecessary information.
Amazon’s Tool for Making Suggestions to their Customers
Every website selling products online has an integrated machine for generating new suggestions in accordance with a customer’s search data, and Amazon is no exception to the rule. On the website, there is a page “Your Amazon.com,” which contains a range of products similar to the ones customers used to search and buy before. Another section based on search requests is the one with products popular among the other buyers of an item you have just purchased. It offers additional software for your laptop, or any other device searched by customers. The popularity of this list of products arises from the trust of the people who made the same choice.
“Reason and Rationality” of the Suggestions Tool
Even though the coordination of search engines and item suggestions compilation within one website is a common practice, it does not necessarily provide both reason and rationality. To receive customer’s support and increase the company’s profit, it has to consider many nuances in the process. The success of a website refers to the appropriate suggestions for a customer that make him buy a product and the consequent satisfaction from its usage.
To provide the service, the tool has the task of gathering big data. The term “big data” means “larger, more complex data sets” (“What is Big Data?”: “The Definition of Big Data.”). It refers to the increased amount of data a person cannot process without special machines. The big data contributes to more precise suggestions as they solely depend on the amounts of processed information. The more data the tool manages to process, the more accurate suggestions the customer receives.
It is essential to make sure that the tool works correctly and provides the result that cannot be a simple coincidence. The task here is to make the correct conclusions “based on the correlation between X and Y” (Anderson). For instance, if a customer is searching for new headphones, the suggestion to buy a stereo system is unlikely to substitute his or her original intention. Therefore, the company needs to be confident in the results of data processing.
In case of Amazon, the reason for developing special tool lies in the fact that a customer takes less time to make a decision on a purchase of an item if he sees some help from the platform creators. Considering all the misinterpretations of his intentions by a search tool and the consequent wrong suggestions, a customer tends to doubt the rationality of such sections and its use to him or her. Thus, the company should work on the accuracy of their suggestions to make their clients feel more comfortable about the choice of items and eager to return for more suggestions, and therefore purchases.
Limitations and Consequences of Amazon’s Suggestions Tool Usage
These tools work perfectly only in theory, whereas in practice, the company faces numerous challenges. The search for a method to provide the quality, not the quantity of processed data is a question of great importance. This issue is leading to the appearance of some limitations on the suggestion tool usage and the decrease of a customer’s satisfaction with the purchased goods and worsening the company’s reputation.
First of all, the question is in the objectivity of received information. The gathered and processed data are not objective as they are “creations of human design” (Crawford). The approach that works for the search electronics cannot work for books. If one buys a children’s book about animals on Amazon, he or she might not be interested in horse breeding. Thus, the system does not work correctly when it comes to human factors.
Besides, one of the main disadvantages of Amazon search tools is their lack of ethical reasoning. This situation leads to biased results as their creators do not consider social circumstances. The wrong interpretation of the website statistics might “facilitate the spread of false information” (Karoff). The buyers eventually might see the inappropriate goods in their recommendation list that do not relate to the initial search request. It creates an unnecessary mess in the suggestions section and decreases the customer’s trust in the company.
Numerous thinkers critique the online platforms with suggestions like Amazon for their inaccuracy and inability to provide the necessary solutions. Thus, the suggestions on websites do not consider vital issues such as their customers’ cultural background. They only recognize some consumption patterns and base their suggestions of this distorted information (Kitchin). The common mistake for Amazon tool is that it does not take into account human factors. A person can visit a page, but it does not mean he or she wants to buy an item in this category. The platform does not consider the time of visit of a customer. Therefore, some items which might have been interested for him or her, are irrelevant by the time of creating suggestions.
Conclusions
Search engines aimed at promoting customers’ trust in websites such as Amazon are still not a perfect solution for potential buyers. Due to the lack of human factors, numerous discrepancies tend to appear. Big data solutions facilitate the search process, but they still cannot substitute small data research conducted by people. The usage of search tools on Amazon provides the website with a higher profit but does not necessarily facilitate the buying process for its customers.
Works Cited
Anderson, Chris. “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.” WIRED. Web.
Crawford. Kate. “The Hidden Biases in Big Data.” Harvard Business Review. Web.
Karoff, Paul. “Embedding Ethics in Computer Science Curriculum”. The Harvard Gazette. 2019. Web.
Kitchin, Rob. “Big Data, New Epistemologies and Paradigm Shifts.” Big Data & Society. 2014. Web.
“What is Big Data?“: “The Definition of Big Data.” Oracle. 2014. Web.