Using Artificial Intelligence to Drive Product Recommendations

27 Aug Using Artificial Intelligence to Drive Product Recommendations

With the number of online shoppers growing at unprecedented rates, companies are increasingly relying on artificial intelligence (AI) and machine Learning (ML) to generate recommendations for products that their customers are likely to purchase. These recommendations are based on AI and ML algorithms that produce deep analytics and learning regarding customer trends and preferences.

This technology has multiple applications across different industries, including the following:

• Online retailers
• Digital media websites
• Any industry needing to increase activity on their ecommerce website and/or to generate higher levels of interaction between users

How Do AI-Enabled Product Recommendations Work?

Recommendation systems provide companies with tools to improve revenue and engagement by suggesting items or media based on the consumer’s demonstrated preferences. Those preferences are identified via analysis—and deep learning—of the consumer’s rating of products, shopping/browsing history, purchase habits, similarity in purchase patterns, etc.

There are two basic types of recommendation systems:

• • Collaborative filtering: There are two methods in this system. 
    • User to user: The system finds users with similar profiles to the target customer and recommends items that they liked. As the computational cost to compare all customers is high, this solution is a good fit for companies with a smaller customer base.
    • Item to item: This approach identifies products similar to those purchased by targeted customers and recommends them as viable additions to current product offerings.
  • Content-based filtering: This type of system actively learns from available information about various aspects of the targeted customers, including the types of products they have historically interacted with, and then recommends new products that they may be interested in.

In many cases both approaches can be used in combination to produce results that are more comprehensive and holistic in nature.

The Benefits

AI-enabled product recommendations can provide several tangible advantages to the company and its customers.

For the company:

• Increased order intake, both in terms of the quantity of orders received and the variety of products purchased. This can significantly increase the company’s revenue and market position.

• Provides targeted marketing strategies for websites and email campaigns. This can create a better ROI for marketing investments.

• Allows the company to provide content to its customer that is based on deeper analysis of what the customer wants.

• Improve customer attraction, satisfaction, and retention. Helps establish the company as a provider of a great customer experience.

For the customer:

• Reduces time to find and select items during their online shopping experience, which also reduces the resources needed on the website for each user.

• Provides a more personalized experience, as only products relevant to the customer are recommended.

• Provides the customer with the opportunity to pursue new interests based on deep analysis of current customer and market trends.

AI-enabled product recommendation systems are revolutionizing the ways in which companies are using deep leaning to provide their customers with a more engaging—and rewarding—shopping experience.