You can do a lot in the back end of a website to make it easier for customers to find products that will interest them. Manually tagging photos, putting products in multiple categories, and adding suggested alternatives are a few of the most common methods for enhancing the buyer’s experience on an e-commerce site. However, this approach has certain limitations, especially for e-commerce businesses that have large inventories or product selections that are continually updated by multiple users.
Getting started with machine learning is a great way for e-commerce businesses to operate more efficiently and provide a better customer experience. Using machine learning to recognize browsing patterns will save countless hours of cross-referencing product features, adding tags, and cleaning up metadata. Customers also get the benefit of relevant product recommendations, which enhances their experience on your site.
How Machine Learning Can Be Used to Recognize Browsing Patterns
Machine learning can be used in a range of industries for various functions, including recognizing different types of patterns. For e-commerce companies that want to keep customers on the site longer and encourage them to add more products to their carts, it can be used to identify customer preferences and provide recommendations based on them. Machine learning incorporates multiple data sources to deliver these results, using computer vision and natural language process to analyze keywords customers enter during search and photos customers click while shopping.
The more a customer interacts with your site, the more the machine will learn about them based on their behavior. This information helps you automatically provide a personalized experience without any manual interaction. This might include showing recommended products while they browse a product page, refining search results to be more relevant, or making suggestions during the checkout process.
Benefits of Machine Learning to Recognize Browsing Patterns
Machine learning provides many benefits for e-commerce companies, which can ultimately help boost top line revenue and decreases expenses:
Increase cart size
When customers are browsing your site with the intent to purchase, you have an opportunity to encourage them to buy more. Showing similar or complementary products is one way to increase cart size and get more revenue per sale.
Boost customer conversion rates
When a new customer comes to your site because they are intrigued by an ad or curious about a promotion, they might not always see exactly what they want when they first land on the page. By showing additional relevant products, you can quickly show them that you have more to offer, increasing the chance that they will find something that matches their needs.
Improve overall customer experience
Customer experience is a top priority for businesses of all types because it is one of the primary factors that drives purchasing decisions. Personalization based on browsing patterns will help you create an experience that makes the customer feel valued and heard.
Get more repeat customers
When you make it easy for customers to shop and find the products they’re looking for, people are more likely to return to your site for their next purchase. Customers often prioritize their experience over other factors such as product selection and price. If they enjoyed shopping on your site, they’ll come back for more.
Decrease product returns
When customers have more choices available to them, they’re more likely to find the right product to meet their needs. Providing recommendations based on browsing habits delivers more options from which to choose. This helps reduce return rates because they are more likely to be satisfied with their original purchase after evaluating multiple options.
Getting Started with Machine Learning
AI and machine learning might feel out of reach, but Skyl.ai has developed a platform that makes it easy for companies of all sizes to automate workflows without technical expertise. You don’t need to be a data scientist or a tech wizard of any kind to automate machine learning processes for unstructured data. If you manage an e-commerce site, this means getting the most from your product data to drive more sales through better image tagging, automatic product categorization, and personalization recommendations.
Machine learning can be used for more than just enhancing your own site. Read our E-Commerce Counterfeit Case Study to learn about how one company was able to detect the sale of counterfeit products in other online marketplaces.