Ecommerce websites are booming, almost everything is getting into digital platform. To boost the experience of ecommerce websites, the companies such as Flipkart, amazon use data science concepts as tool. Artificial intelligence, Machine learning, etc are proving to be handy for them to enhance the customer satisfaction and, increase their revenue. We will look how companies are boosting the experience of e-commerce with data science.
This blog touches on how data science is helping to enhance the ecommerce websites. When we look at the recent years, the performance and the user experience of these websites have improved drastically. The predictions made using the machine learning model are helping them to understand the market better and plan their strategies. Here are few applications of data science in ecommerce,
- Customer segmentation
As in case of offline stores, the customers cannot get the experience of a helper or salesperson who can help the customer with the product in case of confusion or doubt in the online platform. In order to provide the similar experience as the physical stores do, the machine learning models are used for customer targeting which helps to convert the prospective customers to buy the product.
- Optimal pricing
The pricing is one of the important factors which decides whether the customer will buy a product or not. A machine learning model which considers various factors helps to decide the dynamic pricing of any product, which makes it to stay competitive with the offline stores. Few of these factors are the stock available, the demand, product age, etc.
- Search result optimization
Not all the customers who visit the website will have good knowledge about the products available, most of them might not even use the proper keywords to search any product, in that case using only keyword as primary factor for search results fail. Machine learning algorithm identifies the search pattern, interests of the customer, purchase preferences, etc and provides relevant search results.
- Product recommendations
To enhance the marketing strategy recommender systems, act as significant method, by understanding the pattern and the purchasing trend the recommender system suggests the customers, the products which they are more likely to buy. This has proven to be one of the major factors of revenue building.
- Customer support
Automated customer support such as chat bot which uses artificial intelligence and NLP to understand the customer queries and resolve them in the similar way as a human does. This will help customers not to wait for call connection with the customer executive and thus enhances the user experience.
- Demand forecasting
By understanding the data patterns using the data generated previously, the demand for a product for the given time is calculated using the concept of time series forecasting which help these websites to understand the demand and supply for each product. Machine learning model also helps to give some useful insights in the data.
Data science concepts such as machine learning, artificial intelligence, NLP, etc have not only improved the customer experience in the ecommerce websites but has also helped these companies to increase their revenue without investing much on understanding the customer patterns. Data analysis has helped them to understand the insights such as frequent customers, frequently bought products, etc. As the ecommerce websites are set to grow substantially, even the application of data science and AI will grow with it. Many data scientists and data analysts are working in these companies and are the ones who get paid really well to boost e-commerce with data science.