What are the applications of data science in e-commerce? One good thing is the advent of e-commerce sites thanks to the internet. Now you can sit at home and order anything you want, and it will be delivered to your doorstep! Want a new phone? Order online! Looking for new shoes? Just enter your size and you’re ready for delivery! You can even order veggies for dinner and get them before you start cooking! These are the joys of modern e-commerce. But e-commerce sites have become so popular that millions of people visit them and order their products. The huge amount of data created by all these people can no longer be analyzed by their employees. They need to use data science, so let’s take a look at the application of data science in e-commerce.
Let’s take the number of reviews on Amazon as an example – how exactly is Amazon supposed to analyze millions of reviews for all of its products, unless they use sophisticated data analysis algorithms? What about auto-referrals? Amazon will tell you everything you might want to buy based on your personal tastes. It’s data science too! So, let’s discuss these recommender systems in detail and take a look at various other applications of data science in e-commerce.
An example of data science in e-commerce: a recommendation system
Have you noticed that Amazon or Flipkart or any other e-commerce site gives you various options about what you want to buy or are interested in? So how do these sites know what you want? Are they magicians? No, they only use the magic of data science! E-commerce websites use a technique called a referral system to track the kinds of products you buy, the pages you click on, the products you’re interested in, and more, and then analyze this data using data science to provide you with recommendations based on this profile. So everyone who uses these e-commerce sites will receive personalized recommendations based on their browsing patterns, purchase history, etc. There are different types of recommendation systems, such as content-based recommendations, which provide recommendations based on what you are interested in, and collaboration provides you with recommended recommendations by comparing you to users who may be interested in similar items, etc.
Customer feedback analysis
What are the applications of data science in e-commerce? Happy customers are paying customers of e-commerce companies. So, unless they want to break the bank, they can’t ignore customer feedback. Most companies fail because they don’t pay enough attention to customer feedback and don’t improve their defects in a timely manner. However, this is easier said than done, especially for large eCommerce companies that sell thousands of products and have millions of customers. But here, too, data science can come in handy. Techniques such as sentiment analysis are great for understanding how customers feel about the company and if there are any complaints that can be resolved. Companies can use natural language processing, computational linguistics, text analytics, and more to understand the overall sentiment of their customers and determine whether the sentiment is good, bad, or neutral. Then, if there is a bad mood, they can try to understand what the problem is and work to solve it.
Price optimization
Application of Data Science in E-commerce: Price is an extremely important factor in e-commerce. After all, would you buy headphones on Amazon that you think are too expensive? Or you may feel that Flipkart offers a better deal on these headphones, and then you buy from there. As a result, e-commerce sites need to make sure that their prices are attractive enough and cheap enough for customers to buy their products, but also expensive enough so that they can still make a profit. It’s a very tight rope and data science helps e-commerce websites with price optimization. The price optimization algorithm takes into account various parameters such as the customer’s buying pattern, competitor pricing, price flexibility, customer’s location, etc. In this way, e-commerce sites can find out the best prices for their products, making them affordable enough that people will buy them, and they also offer profits.
Customer lifetime value projections
All customers have a lifetime value to the e-commerce company, which means how much profit they provide to the company throughout the association. As a result, companies can use data science to calculate customer lifetime value and understand the value of customers to their business. This is done by analyzing the customer’s purchases, online interests, product preferences, and other behaviors on the e-commerce site. The company can then understand which customers are consumers below zero, which customers cost more than the company’s value, and which customers are the best customer segments. Once these things become clear, companies can focus on reducing sub-zero consumers and targeting profitable customers for maximum impact and profitability.
Data science in e-commerce example: fraud identification
The likelihood of fraud is also high when something is completely online. This is the case in the case of e-commerce sites, when some users try to commit credit card fraud or may keep buying products before returning them. However, data science can help these companies spot fraud and suspicious customer behavior to minimize losses. Data analytics can catch anomalies that occur in credit card history and financial purchases due to credit card fraud and freeze user accounts. Clustering algorithms can also be used to catch clustering patterns for suspicious behaviors such as multiple returns, bulk purchases of the same product, and more. In this way, data science can be used to manage fraud, which increases with the number of customers in E. – Commercial websites.
Inventory management
What are the applications of data science in e-commerce? Every company that sells certain products needs to stock up on all the items they have, the most popular ones, etc., so that they can meet the needs of their customers. The same is true in the case of e-commerce sites. If an item is shown as available on the website but is actually unavailable, or if the most popular item is out of stock and there are a large number of items that have never been sold, the e-commerce company will never be able to function! So inventory management is very important, especially for big e-commerce companies like Amazon, Flipkart, etc. These companies sell thousands of items to millions of people every day, so they need efficient data analytics algorithms to keep their inventory up to date. These data analytics algorithms can understand the correlation between demand and supply, and then develop strategies to increase sales by always ensuring that in-demand items are available.
Data Science in E-Commerce: Warranty Analytics
All products sold on e-commerce sites come with a warranty. But what if the website offers a long warranty? They will start losing money because their customers will return what they bought! And if the warranty period is too short, then the e-commerce site will have some very dissatisfied customers who have problems with the goods and cannot be returned! That’s why it’s so important to have an ideal warranty time that’s long enough for genuine customers to return their defective items, but not enough to commit fraud. Data science can help find patterns of problems with items, the number of customers who have returned those items, if there are any cases of suspicion or fraud among those customers, etc., so that companies can set a warranty time that is convenient for both them and their customers.
Using all these applications of data science, e-commerce companies can increase sales, create personal connections with customers, reduce fraud, and make insane profits! Data analytics can help these companies match supply with demand and cash based on current trends in the e-commerce market. After all, this is one of the reasons why Amazon is one of the largest and most well-known e-commerce companies in the world.
