Jess Erdman, April 2023
YesPlz eCommerce recommendations powered by AI revolutionize the way shoppers discover new items.
But first, let’s break down the two most common AI product recommendations:
Collaborative filtering is an algorithm that is based on the choices that shoppers make. It connects those choices to groups of shoppers with similar interests.
A similar recommendation system is when shoppers see products that have a similar style, or, in more advanced cases, vibe, to the product that they’re viewing.
But, what if we go a step further?
By incorporating advanced algorithms and machine learning, YesPlz brings a new dimension of personalization to the shopping experience.
It goes beyond collaborative filtering and similar recommendations, identifying not just text matches but also delving deeper into the intrinsic qualities of products that appeal to individual shoppers.
Improving upon traditional recommendation methods, YesPlz’s AI intuitively understands a shopper's query, even when the text descriptions don't exactly match.
We can recognize style preferences, trends, and even vibes that resonate with consumers.
The secret is taking a user-centric approach to building eCommerce recommendations–then, combining fashion-trained AI to build superior recommendations.
Traditionally, similar recommendations are made by matching product names or brands to a shopper's query. But, that’s not enough to make truly personalized recommendations.
YesPlz’s approach to eCommerce recommendations is grounded in an understanding that shoppers value silhouettes, cuts, and vibes as key attributes of fashion products.
After identifying the core attributes that shoppers care about, we built similar recommendations that find matching products based on silhouette/vibe.
Then, through AI image tagging, we can match product attributes that go beyond product title or text descriptions, from a search query back to a retailer product catalog.
Our similar recommendations find designs that are similar, but just different enough to spark engagement.
The secret sauce? Have our fashion AI match items with a similar fit and silhouette plus similar fashion vibes and occasions.
A collaborative filtering algorithm can identify patterns in shopper purchases and use this information to recommend similar products, leading to highly personalized product recommendations.
This can lead to increased customer satisfaction, higher sales, and better customer retention.
Solving Cold-Start Problem Problems with Combined Recommendation Systems
However, collaborative filtering can suffer from a “cold start problem,” where the algorithm needs time to learn more about a user to make recommendations, effectively leaving shoppers without relevant recommendations at first.
YesPlz combines both similar recommendations and collaborative filtering to create even stronger recommendations, never leaving shoppers without a relevant product suggestion.
What allows YesPlz to make more targeted recommendations than other tools?
By pre-tagging retailer catalogs using computer vision, YesPlz can make more accurate eCommerce recommendations that go beyond matching text titles or brands.
Our computer vision is trained to identify the most important product attributes in seconds by simply scanning a product image. We use this data to create recommendations that are accurate and standardized.
Retailers simply plug-in their product feeds, and YesPlz can tag products quickly and accurately.
When crafting eCommerce recommendations powered by AI, we realized that answering the “why” behind shopper behavior is key to showing the most relevant products.
We interviewed real shoppers to learn what constitutes a certain style or mood, and trained our AI based on this feedback.
And, we partner with retailers to better understand their specific shoppers’ intent to better curate recommendations.
By having real conversations with shoppers, we create the environment for better quality data by first pre-training our AI to recognize the product attributes that matter most to shoppers.
Ecommerce product recommendations don’t need to be limited to only similar and collaborative filtering. We’re always thinking creatively about how to make recommendations, combining both technology with retailer knowledge.
For example, we partnered with Kfashion retailer, The Handsome, to create a fashion quiz to help their shoppers discover new styles. Shoppers swipe ‘yes’ or ‘no’ on a Tinder-style fashion quiz and then see curated product recommendations based on their selected styles.
With a single point of integration, YesPlz ecommerce recommendations are retailer-friendly.
And, retailers can continue to control the products that are recommended to their shoppers, by boosting specific products such as new arrivals or featured products.
Why personalized eCommerce recommendations matter:
Personalized eCommerce recommendations are a powerful tool for retailers to increase engagement, increase revenue and drive loyalty.
Retailers using YesPlz product recommendations saw:
+15% increase in sales generated
+10% in average cart values
But, not all AI recommendations are created equal. By combining AI and real shopper feedback, YesPlz has created eCommerce recommendations that are tailored to each retailer, that tap into the “why” behind shopper search intent.
Curious to see how YesPlz can help you?
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