What are the Advantages of Using Machine Learning in Retail?

CIO Review APAC | Tuesday, September 21, 2021

Machine Learning can help every organization increase its sales, improve its relationship with the customers, and stand out in the competition. 

FREMONT, CA: Consumer behavior is changing along with technological advancements. Naturally, retailers must make more extensive use of customer data to stay ahead of the competition. Big Data analytics is being used by retailers to utilize the most relevant customer insights as data volume increases rapidly. 

Artificial Intelligence and Machine Learning technologies have significantly impacted the retail industry in the last few years. Businesses that depend on online sales, in particular, are incorporating Machine Learning resources to boost sales and cut costs.

It is difficult to say which industries have transformed the most due to Machine Learning and Artificial Intelligence technologies, but the retail sector is certainly one of them. Machine Learning emerged as the most promising technology for retailers of all types and sizes.

Use Cases of Machine Learning in Retail 

Machine Learning is being used by many businesses to improve customer experience and increase sales. Here are some examples of machine learning applications in the retail industry:

Price Optimization

Machine Learning systems process a large quantity of data, allowing companies to see a holistic view of what is happening on the market. AI technologies, for example, enable retailers to monitor the activity of their resellers and determine whether any of them are breaching the Minimum Advertised Price.

It becomes possible to discover the clearest price buyers are willing to pay for a specific product due to extensive data analysis on customers and their solvency. Depending on this, businesses can adjust the assortment by customizing the product to more appropriate pricing or earning even more.

Personalized Offers

Personalization is a recent trend, and current shoppers are no longer interested in mass offers. In response to this trend, the Machine Learning system analyses the user's behavior, adds information about their previous purchases, Google search history, social media comments and likes, locates the client visits, and solvency, and makes the best recommendations about what kind of product will be suitable for them and when they need it.

Churn Rate Prediction

According to the study, this is a critical point because when a retailer loses one of its consumers, it loses potential profit and money spent on pursuing and developing connections with this buyer.

The company will now have to pay to convince a new client, five times the cost of keeping the old one. Machine Learning algorithms can detect scenarios that are likely to result in a client's loss, allowing the organization to take immediate steps to keep them. 

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