Data Science Applications in Retail | Intellipaat

By analyzing its customers' purchasing habits, the company discovered that it could not only assign a level of likelihood that the customer was pregnant, but also predict a likely due date.

Target discovered how to data-mine its way into a woman's womb.

Using that information, the retailer began to mix foetal items into offers sent to those customers, in addition to regular coupons.

Almost every industry has been impacted in some way by the rise of data science technologies. The retail industry is no different.

Data science offers retailers an excellent opportunity to leverage their customer data and turn it into actionable insights that will increase revenue. That is, after all, the ultimate goal, isn't it?

Obtaining a Data Science Training is vital for upskilling and staying current in the workplace.

Retail Recommendation Engines Using Data Science

• Detection of Fraud
• Augmented Reality Powered
• Price Optimization in Personalized Marketing
• Intelligent upselling and cross-selling
• Inventory control
• Analysis of customer sentiment
• Predicting trends using social media
• Real estate administration
• Prediction of customer lifetime value

Let us go over the various ways in which the retail sector is making the best use of data in detail:

1. Recommender systems

It is a system that filters information and predicts user preferences while they are surfing the web. It has proven to be an excellent tool for retailers in predicting customer behavior.

Customers can identify trends and increase their sales and thus revenue by receiving recommendations.

Recommendation engines manage and adjust themselves based on the choices made by customers. There are three primary recommendation techniques:

• Filtering in collaboration

Based on the preferences of many other users, this type of system predicts what you might like. It is assumed that if A prefers Gionee and B prefers Gionee and Vivo, then A might like Vivo too.

• Filtering based on content

This system focuses on the products themselves, rather than other users, and recommends products with similar attributes or characteristics.

• System of hybrid recommendation

This is a system in which the results of the previous two techniques are combined.

2. Detection of Fraud

Deep Neural Networks (DNNs) and other Data Science and Machine Learning techniques are being used to detect fraud in business transactions.

Because of the increase in online transactions, shopping, banking, filing insurance claims, and so on, fraud has become a major problem for these companies, and they are investing heavily in detecting and preventing fraud.

Traditional approaches to fraud detection are rule-based, resulting in a race between criminals devising new methods and sellers' fraud detection systems. Our modern approach uses the massive amount of data collected from online transactions to predict fraud transactions, whereas the traditional approach is rigid.

3. Augmented Reality Power

Topshop, a multinational corporation, has been experimenting with new technologies to incorporate augmented reality into the shopping experience.

Customers can select clothes and see how they appear without actually wearing them. This speeds up decision-making and saves customers' time and effort.

In their 2013 catalogue presentation, IKEA introduced image recognition and augmented reality for the first time. Customers can virtually place catalogue items in their homes and scan them to see how they look.

They can also choose the colours and sizes that best suit them, and they don't even have to leave the house to do so.

4. Individualized Marketing

Retailers use this system to integrate personalized recommendations based on their users' browsing history, previous purchases, likes, and dislikes. Additionally, it enables retailers to create highly targeted campaigns that increase ROI.

All of this is possible if retailers have data and can extract meaningful insights from it.

Typically, data science comes to the rescue in this situation. Predictions about what customers will do next can be made by leveraging customer data from various customer data platforms.

Vedic hair care, for example, implemented a marketing strategy to engage the audience by providing them with customized products. They recommend products based on their customers' concerns and preferences.