Top Applications of Data Science in E-commerce
One good thing because of the internet is the emergence of E-commerce websites. Now you can sit at home and order whatever you want which will be delivered to your door! Want a new phone? Order it online! Want new shoes? Just type in your size and get them delivered! You can even order vegetables for dinner and get them before you begin cooking! These are the joys of E-commerce in modern times. But E-commerce websites have become so popular that millions of people visit these sites and order their products. This huge data created by all these people cannot just be analyzed by their employees anymore. They need to take help of data science.
Let’s take about the number of reviews on Amazon, for example – How in the world is Amazon supposed to analyze the millions of reviews on all their products unless they use a sophisticated data analytics algorithm? And what about automatic recommendations? Amazon tells you all that you might like to buy based on your individual taste. That’s also data science! So let’s discuss these recommendation systems in detail and also see the various other applications of data science in E-commerce.
Do you notice that Amazon or Flipkart or any other E-commerce site provides you various options about things 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 technology called recommendation systems that track what kinds of products you buy, which pages you click on, what products you are interested in etc. and then analyze this data using data science to provide you with recommendations based on this profile. So everyone using these E-commerce sites would receive individual personalized recommendations based on their browsing patterns, purchase history, etc. There are different types of recommendation systems such as content-based recommendations that provide recommendations based on the content you are interested in, collaborative recommendations that provide you with recommendations by comparing you with users who might be interested in similar items, etc.
Customer Feedback Analysis
Happy customers are paying customers for E-commerce companies. So they cannot afford to ignore their customer feedback unless they want to go bankrupt. Most companies fail because they do not pay adequate attention to customer feedback and improve their flaws in time. However, this is easier said than done, especially for large E-commerce companies that sell thousands of products and have millions of customers. But here also, data science can come to their rescue. Techniques like sentiment analysis are perfect for understanding how the customers feel towards the company and if there are any complaints that can be resolved. Companies can use natural language processing, computational linguistics, text analysis, etc. to understand the general sentiment of their customers and find out if the sentiment is good, bad, or neutral. Then if there is bad sentiment, they can try to understand what the problem is and work on resolving it.
Prices are an extremely important factor in E-commerce. After all, would you buy earphones on Amazon that you think are too expensive? Or maybe you feel that Flipkart gives a better deal on those earphones and you buy them from there. So E-commerce websites need to make sure that their prices are attractive and cheap enough that customer will buy their products but also costly enough that they will still make profits. This is a very tight rope to walk and Data Science helps E-commerce websites using price optimization. Price optimization algorithms consider various parameters such as the buying patterns of the customer, competitor pricing, flexibility in the price, location of the customer, etc. In this way, E-commerce websites can find out the optimal prices of their products so that they are affordable enough that people will buy them, and they also provide profit.
Customer Lifetime Value Prediction
All the customers have a lifetime value for E-commerce companies which means how much profit they provide the company over their entire association. So companies can use Data Science to calculate the Customer Lifetime Value and understand the value of a customer to their business. This is done by analyzing the customer’s purchases, online interests, product preferences, and other behavior on the E-commerce website. Then the company can understand which customers are below zero consumers which cost the company more than they are worth and which customers are the optimal customer segments. Once, these things are clear, companies can focus on reducing their below zero consumers and target their profitable customers for maximum reach and profitability.
When something is entirely online, there are high chances of fraud as well. This is true in the case of E-commerce websites when some users try to commit credit card fraud or maybe constantly buy products only to return them later. However, data science helps these companies to catch fraud and suspicious customer behavior in order to minimize their losses. Data analytics can catch the anomalies that occur in credit card history and financial purchases because of credit card fraud and freeze the user account. Clustering algorithms can also be used to catch out on the cluster patterns of suspicious behaviors such as buying things and return them multiple times, buying the same product in bulk, etc. In this way, data science can be used to manage fraud which has increased more and more with the increase in the number of customers in E-commerce websites.
Every company that sells some products needs to have an inventory of all the items they possess, the most popular items, etc. so that they can supply the customer demand. This is also true in the case of an E-commerce website. An E-commerce company could never function if an item showed as available on the website but was actually unavailable or the most popular items had low stocks while there were huge stocks of items that never sold! So inventory management is extremely important, especially for large E-commerce companies like Amazon, Flipkart, etc. These companies sell thousands of items to millions of people every day, and so they need efficient data analytics algorithms to keep their inventory up to date. These data analytics algorithms can understand the correlations between demand and supply and then create strategies to increase sales by always ensuring that in-demand items are available.
All the products sold by E-commerce websites have a warranty attached to them. But what would happen if the websites provided a very long warranty time? They would start losing money as their customers would return the items they buy! And if the warranty time is too less, then the E-commerce website would have some very unhappy customers with faulty items that cannot be returned! This is why it is so important to have an ideal warranty time that is enough for genuine customers to return their faulty items but not enough to commit fraud. Data science can help in finding the patterns for the issues in the items, the number of customers who return these items, any suspicious or fraud cases in these 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 their sales, establish a personal bond with their customers, reduce fraud, and become insanely profitable! Data analytics can help these companies match their supply to the demand and cash on the current trends in the E-commerce market. After all, that’s one of the reasons that Amazon is one of the largest and most famous E-commerce company in the world.