As data scientists, we understand how valuable it is to harness this data's power through data science. Data science is a key strategic practice for any business that uses scientific methods, processes, algorithms, and systems to extract knowledge from data and use this data to make major decisions.

Retail is a subset of business in which a company sells a product or service to an individual consumer for their own use. The fact that the end-user is the buyer qualifies the transaction as a retail transaction. When it comes to the transaction itself, it can take place through various sales communicators, such as online, direct, and so on.

The retail connection is rapidly evolving; the retailer analyzes data and creates a scenario for the customer. As a result, a customer is easily influenced by the tricks devised by retailers. Walmart, Target, and other retailers are good examples. A detailed explanation on DS techniques can be found in a data science course in Mumbai. 

In this article, we will look at the 6 Data Science use cases in retail. We will look at the key points of these cases and then go into more detail.

1. Price reduction

Assume you are a customer; what will be important to you while shopping? It's price, quality, and a variety of other factors, but what if you can get better quality at a lower price? So, everything depends on price; 70% of all consumers believe that the primary reason for purchasing a product is its price. Yes, it also applies to retailers; according to the producer's mindset, price is determined by the number of materials used during production and the type of customer who will purchase that product. The tools for data analysis take this issue to a new level of consideration.

If you are aware, there are a variety of optimization tools in data science that assist retailers in identifying the customer's personal approach. Among them are:

• Segmentation of customers

• Shopping for a mystery

• Price flexibility, competitor pricing, and so on. 

2. Individualized Marketing

Marketing strategy is the most effective method for attracting customers and is profitable for retailers. When we talk about its process, it collects customer transaction data. This technique can predict future decisions and choices on a large scale. When they create marketing scenarios, knowledge of the items with likes, dislikes, and previews is more beneficial.

Typically, the analysis is carried out using a rule-mining algorithm. It extracts the useful from the data; a special function accepts the data, divides it according to various factors, and discards the useless or unnecessary data.

Understanding the data science workflow will enable your marketing team to communicate effectively with the data scientist. Machine learning, regression, and clustering are examples of data science techniques that have transformed marketing from a creative branch to one that uses science to understand and influence user behavior:

3. Detecting Retail Fraud 

We all know that gaining customers' trust is the most important factor influencing the growth of industries. What if there is a case of customer fraud? Then industries destroyed customer trust and faced massive losses due to these activities.

Data Science in retail aids in the protection of the company's reputation. For retailers, detecting fraud is becoming a difficult problem. Following some financial losses, businesses are now seeking assistance from new digital technologies such as machine learning and neural network concepts. This allows them to monitor all activities and detect any fraudulent activity constantly.

4. Using Social Media 

In today's world, everyone uses social media because it is the most effective means of communication between utilities. Retail industries are using these communication bridges or technologies worldwide for marketing. Retailers benefit from a large amount of customer data provided by social media, which aids in discovering patterns, customer behavior, and trends.

Data scientists and researchers at social media companies benefit from direct access to the code base and the ability to update it regularly for their own purposes. They are not limited to hacking together instrumentation and linking onto existing processes to imbue meaning. If they want a piece of data collected on a regular basis, they can write code to collect it.

5. Using Augmented Reality

In the context of Data visualization, AR (Augmented Reality) can become a little more complex and dynamic. While the camera displays an image of a specific domain, the domain is marked with specific points (either in a Marker or Markerless mode) so that when a specific point in the domain is because of the camera, the AR system can detect the Specific Point and become aware of what that Specific Point is.

If you've ever heard the phrase "Try before you buy" in an advertisement, it's because many retail companies have used it for marketing. AR, or augmented reality, gives the customer a real-time experience with the product. AR has quickly emerged as a critical technology for retailers.

6. Commercialization

For example, if you are a shopkeeper, you will promote your product to customers for purchase. Merchandising is the activity that assists you in promoting the product when a customer purchases the item. Merchandising has evolved into an essential component of the retail industry.

It employs a technique in which the machine learning algorithms manipulate the customer's decision and encourage them to purchase more products. Retail is built on three pillars: assortment, experience, and value, which means knowing what products to sell, how to sell them, and at what price.

For further information on data science tools methods used in various industries, join the data science certification course in Mumbai which is instructor-led online training for working professionals looking to improve their skills.