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How AI's Transforming Product Recommendations for Retail

Updated: Jun 24, 2023


AI-Powered Shopping Experiences

AI is already transforming the way we shop. Chatbots and virtual assistants are helping customers shop with ease, while visual search is creating a new field that enables faster product search by letting customers snap a picture of what they're looking for.





AI-powered product recommendations are a way to increase the likelihood that users will make a purchase. There are many different types of recommendation systems, but they can be classified into two main categories: collaborative filtering and content-based filtering. Collaborative filtering involves analyzing data about what products consumers have previously purchased or viewed and then showing similar items based on that information. But it's not always accurate--it's possible that someone who likes one type of skincare product won't necessarily like another type just because they share some commonalities in what they've purchased. Content-Based filtering is a better alternative to this, where using AI to analyze images and recommending products based on those data points can increase the relevance and likelihood of purchase for the customer.


AI-Powered Product Discovery with Image Recognition

At FINIITE we use this type of technology allowing users to take pictures of their skin concerns while our computer vision models instantly identify and recommend products based on it. It's useful when trying to determine what specific products a customer might want or need, while having the convenience of using it anytime, anywhere.


AI-Powered Personalization


Here are some ways you can use it:

  • Dynamic Pricing: Dynamic pricing allows retailers to adjust prices based on demand and other factors, making sure that they're always getting the best possible return on their inventory. It also helps them avoid overpricing items during peak seasons or underpricing them during slow periods, which would result in lost profits.

  • Targeted Advertising: Targeted advertising lets you show ads for products that are relevant for each individual user based on their past purchases and browsing history--so if someone has been looking at a moisturizer recently but hasn't bought any yet, you could show them an ad for it when they visit another site like Amazon where those products are sold (or send them an email!). This improves customer retention rates while reducing costs associated with acquiring new customers since it allows you to focus only on those who have shown interest.


Impact of AI on Product Recommendations and Online Shopping


AI is already having a significant impact on product recommendations and online shopping. In fact, AI-powered product recommendations have been shown to increase conversion rates by as much as 20%. Our API also uses sales endpoint to keep track of these metrics via dashboard and allowing for higher sales conversions and revenue growth for ecommerce businesses.


Improved customer experience


Customers have come to expect fast delivery times when ordering products online; retailers must keep up with demand while ensuring accuracy when fulfilling orders from inventory management systems (IMS). With an automated recommendation engine built into your online platform, you'll be able to better predict peak sales cycles so customers don't have to wait around long periods before receiving their orders.



Conclusion

In this article, we have discussed:

  • How AI can be used to improve recommending products to your customers

  • How AI can be used to improve the overall sales conversion for your brand or business


Connect with us if you would like to boost growth for your ecommerce business with AI and image recognition.




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