E-Commerce & Retail

Machine Learning can have a powerful impact on
E-Commerce & Retail in numerous ways.

Companies are using it to increase revenue and sales by better understanding customers needs, marketing strategies, forecasting and inventory, and more.

Use Cases


Retargeting is the process of using ads on external sites to reach previous visitors. Machine Learning enables e-commerce owners to better retarget users by looking at data to find out what had worked to convert similar profiles in the past through retargeting. Algorithms can understand customer behavior to suggest highly relevant retargeting campaigns.

Inventory Management

Inventory management can be painstaking if done manually - eventually impacting accurate sales predictions. Machine Learning can make forecasting future demands a lot more precise. This will help with easy supply chain management, but it also ensures better understanding of customer behavior.

Check out our YouTube Video to watch a use case video about how to use Machine Learning to predict solutions for Inventory Management.

Trend Analysis

Before putting products up for sale on an e-commerce website, you must analyze their trend. Is it a bestselling product, a normal seller or outdated? Understanding these trends play a large role in product inventory and returns.

Fraud Protection

Machine Learning can identify patterns in data to catch anomalies. The most common application of this is fraud detection. Machine Learning rapidly processes the repetitive data to detect frauds before they can happen.

Market Analytics

Machine learning can empower companies to create more effective marketing campaigns. Have you ever noticed ads following you around everywhere after you have visited a page? Companies can collect online consumer data to detect customers’ behavior, demographics, and habits. Machine learning models can use this data to forecast the most effective marketing channels to deliver content.