How is Data science applied to increase sales in e-commerce - sample case

Recommender system in e-commerce

Data collection: An online retailer collects data on customer purchases and website interactions, such as product views and searches.

Data preprocessing: After cleaning and preparing the data, the retailer has information on 10,000 customers and 50,000 products.

Exploratory data analysis: The retailer creates visualizations of the customer and product data, and finds that there are two distinct groups of customers: those who buy primarily clothing and those who buy primarily electronics.

Segmentation: The retailer uses clustering to segment the customers into these two groups, with 6,000 customers in the clothing group and 4,000 customers in the electronics group.

Model validation: The retailer compares the characteristics of each group, such as average purchase amount, and finds that the segmentation is valid.

Recommendation engine: The retailer develops a recommendation engine that suggests products to customers based on their past behavior and preferences. The engine takes into account both the customer's segment (clothing or electronics) and their recent purchases.

Testing and evaluation: The retailer conducts A/B testing of the recommendation engine, and finds that customers who receive recommendations have a 10% higher conversion rate compared to those who do not.

Refinement: Based on the results, the retailer decides to continue with the recommendation engine, but also to refine the algorithm to take into account customer ratings and reviews of products.

Deployment: The retailer deploys the recommendation engine on their website, and continually monitors the results over time.