Personalised product recommendation engine
at SKU level for online fashion retailers.

Deliver more relevant recommendations to online shoppers, show clothes that are available in customer’s size and in stock.

A personalised product recommendation engine helps reduce online returns, boost website conversion ratio, average order value and significantly increase views of product description pages on mobile devices. Essentially, easing customer journey to products they are likely to purchase.

PRIME AI has developed a proprietary product recommendation engine for online fashion retailers unlike any other on the market today. Technology is powered by artificial intelligence and understands every garment specifications at SKU level. It is capable to identify the right item size for online shoppers. As a result, generated product recommendations will always be in stock in a size suitable for the customer.
PRIME AI machine learning technology will take into consideration and analyse many parameters from customers’ browsing history and capture their interest and intent to formulate the most appropriate recommendation in order to alleviate missed sales opportunities on mobile devices that have limited view port.
For example: in the scenario that a customer lands on a jacket that is out of stock in their size, they will be offered product recommendations for alternative jackets that are available in the appropriate size.

This is what PRIME AI calls true personalised product recommendation that can match SKU to individual customer.

Visitor Behaviour in real time
customer behaviour
Past Transactions data
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Visitor information:
Age, Weight, Height, All other data
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SKU information.
Recommended Item: Adidas Top Size: Medium
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SKU information.
Recommended Item: Super Dry Top Size: Small
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SKU information.
Recommended Item: Nike Top Size: Medium
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Prime AI also offer all the traditional product recommendation capabilities available at different stages of the funnel where user signals may be limited.

1. Offer best-selling and new products in the scenario where no data are available about the visitor yet.
2. Serve similar products when data about the visitor is limited.
3. Upsell and cross-reference different products at specific levels of funnel based on various selected dimensions and signals. For example, similar products, basket mix or what similarly behaving customer group or specific individuals have viewed and/or purchased.
This is similar to what many recommendation engines call personalisation. However, these methods and strategies still call on very large buckets of customers and products, making recommendations more representative of group personalisation.
Nevertheless, PRIME AI product recommendation engine powered by advanced machine learning and are self-learning in the real time and has the capabilities to go further in granularity and adapt its recommendation down to individual customer size and SKU. PRIME AI recommendations would not bring forward recently viewed products that are out of stock in the visitor’s size.

PRIME AI technology brings together artificial intelligence and fashion retail expertise to modernise the fashion industry and take your online presence to a whole new level.

Does your product recommendation understand garment specifications at SKU level?
Get a product recommendation technology that is designed for selling clothes!

To find out what we can do for your business

Request a Demo Now !