The emergence of ecommerce during the age of the internet remains one of the good things today. People with the luxury of sitting at their homes can order anything from anywhere and get it delivered to their doorsteps just with a click. However, e-commerce websites have grown so well-liked that millions of people browse them and place orders for their products. These many people have produced so much data that it can no longer only be analysed by their staff. There enters data science that helps them out.

Why data science in e-commerce

Global data production, capture, copying, and consumption are all expected to rise sharply, reaching 64.2 zettabytes in 2020. Global data creation is anticipated to increase to more than 180 zettabytes over the following five years, up until 2025, as per Statista. The famous saying - Data is the new gold holds ground till people are able to gain insights out of the data and create value for themselves.

Every e-commerce company seeks an advantage over competitors when meeting customer expectations because consumer habits can change in the blink of an eye. Traits like common sense, intuition, and gut sentiments while useful, are obviously insufficient to make predictions. Effective business understanding of products, services, processes, and customers is made possible by data science algorithms.

Moreover, by gathering and integrating data on customer web behaviour, life events, what prompted a purchase of a good or service, how customers engage with various channels, etc., data science in e-commerce enables businesses to understand the customers better.

How it helps

The collaboration of e-commerce with data science is a win-win situation as it enhances customers' shopping behaviour and provides them with personalised recommendations. Along similar lines, the e-commerce firms gain the upper hand to strategise their marketing tools better and understand customers' tastes and preferences, which in turn enhance the profitability of their businesses. Therefore, it's time to understand the applications of data science in e-commerce:

Product recommendation system: Websites generally have a recommendation system that tracks the things one purchases, the pages visited, the products one is more interested in, and many more. Thereafter, data science comes into the picture and evaluates this data to produce personalised suggestions. Simply put, based on their browsing behaviour, past purchases, and other information, each user of these e-commerce sites would get a unique set of personalised suggestions. There are various recommendation systems, including content-based recommendations that make suggestions based on the content their customers are interested in, collaborative recommendations that compare consumers' preferences to those of other users who might be interested in similar items, etc. For example, the Amazon recommendation engine uses predictive modelling.

Price optimisation for customers: Customers are price-sensitive and often hop onto different e-commerce sites to get the best deal for themselves. This forces the businesses to walk the tight rope and maintain a balance between providing attractive pricing for their customers and making good enough profits to further fuel innovations. Price optimisation algorithms consider several factors, including consumer purchasing behaviours, rival pricing, price flexibility, customers' demographic and physiographic data, etc. This is how e-commerce companies can determine the best rates for their goods, ensuring that they are both profitable and sufficiently reasonable for customers to purchase them.

Feedback analysis: Customers' feedback forms the backbone for any business to move ahead on its growth trajectory path. Sentiment analysis techniques are ideal for determining how customers feel about the business and whether any concerns exist that can be resolved. Businesses can employ text analysis, natural language processing, computational linguistics, and other tools to assess their customers' overall perspectives and determine whether they are positive, negative, or neutral. If there is a terrible feeling, they might then attempt to identify the issue and work to fix it. 

Fraud detection: Anything online remains vulnerable to fraud. Data analytics can easily capture the anomalies that occur in credit card and financial purchases after analysing historical data, and if found to be fraudulent, it can timely freeze the user account. Similarly, clustering algorithms can also be used to identify patterns of questionable behaviour, such as purchasing and returning items repeatedly, purchasing the same item in large quantities, etc.

To conclude, with data science in e-commerce, enterprises may boost sales, develop a personal connection with customers, retain valuable customers, lower fraud, and increase profits.