Machine Learning in Retail

By Esteban R.

Ever since the purchasing markets have been expanding, the demand for a better system that needs to be placed into play has been crucial. People tend to have different taste and preferences, so at the end of the day they will ultimately make the decision on what to spend their money. As new systems are applied to company’s ways of doing business in retail, the better off those companies will understand the needs of their customers. We must first understand the science behind these new systems how they will help companies in the end. These systems are using artificial intelligence to provide computer with the proper learning abilities to learn the proper algorithms. Nonetheless, these new systems known as machine learning. From here, these machine-learning systems will better understand the customer habits, purchase history, consumer demand, and market trends.

First, the companies that are implementing these new machine learning are taking the risk to help themselves benefit. One example is that companies understand which target market will have the most profit. One an article by Daniel Gutierrez explains how machine learning helps build algorithms in order to determine what age group will be appropriate for the each product. This is where a company will determine whom they want to sell too. A second example, retail stores can also determine quantity and shelf placement, and price. With support from research by John O’bourk where he states that, “retails can tap into the power of machine learning to improve their relationship with their customers.”(2016). Here companies will figure out the best price to set for specific goods. Last, companies can only filter out so much, yet they only rely on data that is useable and useful. Therefore, some when companies are using machine learning they set a certain standard in order to be successful. With the research of John O’bourk he says, “The ability to automate everything is can cover so much ground that simple task like basic customer service, we can take machine learning, and be able to predict customer needs.”(2016). This is important because being able to predict customer needs, then the company will be able to determine what exactly they need to produce. Some companies have a head start on machine learning.

Companies that are already ahead in the retail market game are those that are using machine learning. For instance, Amazon is one of the top companies that uses machine learning; in fact, they are advance enough to the point where they provide a service that can help other companies. In an article by Ujjwal Ratan where he explains what this Amazon web service is “Readmission records demonstrate patterns in data that can be used in a prediction algorithm.”(2016). This software works by having a company input as much data as they can, setting the what type information need to be returned, having the service generate the algorithms that are going to filter out outliers, and determining whether a company would like real-time querying or when needed. It is important to keep in mind that no matter what the prediction of the machine learning algorithms say. The customer has the final say in making the transaction. This could be a number of factors, from the challenges that companies face to the limitations that machine learning can handle. In an article by Giles Pavey where he says, “Some things are far more complex to predict. Economic, social, legislative and technology changes can have dramatic impacts on customers’ behaviors.”(2015). One can understand better what the challenges a company can face. For example, if everyone is moving to the latest technology advances and that company’s budget cannot afford to follow, then they must figure something else out. This is why machine learning is curial to have in retail, yet it uncertain because each transaction has its own challenges and limitations.

To conclude, companies must implement machine learning, to better understand the customer habits, purchase history, consumer demand, and market trends. After, they realize all of benefits that come with machine learning they can start improving the tools and software they have. Once the tools are in place, they can go ahead an understand that the challenges and limitations that can arise. Finally, it will be up to the customer whether they want to buy the product or the service since they are the ones with the currency.

Work Cited
Gutierrez, Daniel. (2016). How to use machine learning to further Retail Analytic Capacity. InsideBigData.
O’Rourk, John. (2016). How machine learning will improve Retail and customer service. DataInformed.
Pavey, Giles. (2016). How machine learning is changing retail. Dunhumby.
Ratan, Ujjwan. (2016). Readmission Prediction through Amazon Machine Learning. Amazon.

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