Myntra is one of the fashion e-commerce platforms in India. It is known for its trending collection of clothing, footwear, and accessories. Myntra uses a lot of Data Science techniques that make it different from other e-commerce platforms. So, if you want to know how Myntra uses Data Science, this article is for you. In this article, I will introduce some ways how Myntra uses Data Science for its business as an e-commerce platform.
Here’s How Myntra Uses Data Science
Below are some ways how Myntra uses Data Science for its business as an e-commerce platform.
Elite Customer Identification
Identifying elite customers means identifying and prioritizing the most profitable customers. A customer is a profitable customer when the spending and loyalty of the customer are better than those of other customers.
Myntra uses data science techniques to identify eligible customers for its elite program. They use an algorithm based on the principles of game theory, which helps them identify customers who are likely to be high-value customers.
Myntra analyzes various data points, such as customer behaviour, purchase history, and demographics, to determine which customers should be eligible for the Elite program. Once identified, these customers receive special treatment, such as faster and easier returns, faster shipping, and access to exclusive products, offers, and discounts. It helps Myntra to prioritize valuable and loyal customers, retain them and build customer loyalty towards Myntra.
Recommending Similar Fashion Products
Myntra has developed a computer vision technique called ShopLook that recommends similar fashion products based on a complete image of a model wearing multiple fashion items. Their recommendation system identifies fashion items worn by the model, locates them, and recommends similar products for each item. This approach not only drives cross-selling and revenue, but also improves customer experience and engagement.
Below is how the recommendation system of Myntra works:
- it first identifies the human body parts in the image;
- then determines the pose of the model in the image;
- then identifies the specific fashion articles worn by the model and recommends similar products for each item;
- then creates a vector representation of each product to make product recommendations based on similarity;
The goal of Myntra’s fashion recommendation system is to provide a better user experience and boost sales and engagement.
Supply Chain Optimization
Myntra uses a supply chain optimization model to predict the number of SKUs needed to optimize the Fulfilment Index and the Utilization Index. These metrics measure the effectiveness of the supply chain in meeting customer demand and maximizing the use of available resources.
Their supply chain optimization model is based on historical data and uses machine learning algorithms to forecast future demand.
By optimizing the number of SKUs in stock, Myntra can improve its supply chain efficiency and reduce costs associated with overstocking or stock-outs.
Summary
So below are some ways how Myntra uses Data Science for its business:
- Myntra uses data science techniques to identify eligible customers for its elite program. They use an algorithm based on the principles of game theory, which helps them identify customers who are likely to be high-value customers.
- Myntra’s recommendation system identifies fashion items worn by the model, locates them, and recommends similar products for each item. This approach not only drives cross-selling and revenue, but also improves customer experience and engagement.
- Myntra uses a supply chain optimization model to predict the number of SKUs needed to optimize the Fulfilment Index and the Utilization Index. These metrics measure the effectiveness of the supply chain in meeting customer demand and maximizing the use of available resources.
I hope you liked this article on how Myntra uses Data Science for its business. Feel free to ask valuable questions in the comments section below.
