Jess Erdman, October 2023
While 85% of brands believe they’re offering personalized experiences, only 60% of consumers seem to agree. There is a disconnect between what brands think they are doing and what shoppers actually experience when it comes to eCommerce recommendations.
Shoppers don’t seem to think that retailer recommendations are personalized. And, as retailers know, generalized recommendations simply don’t convert shoppers.
However, with 4 of YesPlz’s basic eCommerce recommendations, retailers can take a major step towards delivering the personalized experiences that shoppers expect.
This article will explore each of the 4 basic eCommerce recommendations, how to use them, and why YesPlz's AI-powered recommendations can outperform others.
Also known as similar recommendations, “You Might Also Like” is an eCommerce recommendation based on similar products to what a shopper is viewing. These can be based on detailed matching attributes like color, pattern, silhouette, neckline
Traditionally, keyword matching provides similar recommendations but that is too general and doesn't consider the actual product attributes. It can miss the most important product attributes because they aren’t in the title.
With YesPlz AI “You Might Also Like” recommendations, we use computer vision to capture the key product attributes in fashion products to create similar recommendations that are mapped to attributes just like an experienced fashion stylist would match.
Retailers can place YesPlz similar recommendations on product detail pages, cart detail pages, and checkout to inspire shoppers. With an easy to integrate API, retailers can place similar ecommerce recommendations anywhere on their website to deliver shoppers with the personalized experiences they are looking for.
The YesPlz Advantage:
Our fashion AI analyzes products based on 20-60 granular fashion attributes in seconds, delivering more relevant matches than text or image matching or any human. When compared to other tagging, we get more granular, and therefore deliver even micro-matching attributes to yield the best similar recommendations
How Kolon Mall, A Leading Retailer in Korea Sparked Sales:
Kolon Mall saw a 15% sales increase after implementing YesPlz similar recommendations by placing them on product detail pages and after adding a product to cart.
Collaborative filtering recommends products based on what other users with similar tastes purchased or viewed, helping shoppers find products based on others who loved similar products. Ideally, groupings are unique like trendy items, shoppers can see more diverse but still curated items.
Traditionally, collaborative filtering finds patterns without considering or matching the key fashion attributes, resulting in general and inaccurate recommendations
With YesPlz collaborative filtering, we can analyze the data, mapped to fashion knowledge, and find the most optimized products to show shoppers.
Retailers can control the input process and weights to adjust to their specific needs. Place the API at any point of a shopper's journey such as product detail pages, quick views, or checkout.
The YesPlz Advantage:
By dynamically serving the best collaborative filtering recommendations, combined with our fashion expertise, we can help retailers increase conversion from product search to add to cart by helping shoppers easily discover the products the shared taste group of people liked or purchased.
How W Concept Used Collaborative Filtering for Increased Conversion
After integrating YesPlz's collaborative filtering, 15% of all "add to cart" traffic now originates from collaborating filtering. Shoppers are now discovering and purchasing more products.
Complete the Look eCommerce recommendations show full outfits that pair with a particular product.
They’re a creative way to show shoppers entire outfits, without needing to piece together individual products separately, therefore making the shopping experience cohesive, and shoppers more likely to add complementary items.
And they’re powerful. Shoppers save in their effort to find complete looks, which increases conversion with upsell. Bounce rates also decrease, because shoppers don’t need to leave websites to find complementary items.
Traditionally, retailers would need to use a team of stylists which is expensive, or use a third-party solution which is too simple for the complex outfit combinations
With YesPlz Complete the Look, we use combined AI processes that can create complete outfits for any occasion and vibe, based on an understanding of the entire product catalog.
YesPlz Complete the Look is a low effort for retailers to implement, saving time but providing a scalable, fashion-forward styling solution, One single integration gives access to the tool.
The YesPlz Advantage:
Our AI generates as many outfits a shopper needs in just seconds using retailers' full catalog. We use sophisticated machine learning models that understand fashion occasions, styling, and products that match.
For example, a red dress can include formal, vacation, and casual occasions–one product can be used for different occasions.
Personalization is a way to create custom eCommerce recommendations for each user based on their interactions and purchase history.
Based on YesPlz research, shoppers are all about the “me”--they expect hyper-individualized recommendations based on their unique interactions and history
Retailers would traditionally need to manually configure personalization, which is unrealistic at scale.
But, with YesPlz AI personalization, each shopper sees unique product recommendations based on their specific interactions. Our AI can read the pattern of past products shoppers viewed, loved, or purchased, and quickly analyze thousands of products, matching the key style patterns.
No more manual configuration or human stylists. Shoppers can skip browsing irrelevant products.
Retailers can use YesPlz personalization onsite, like in a wardrobe capsule to see products, or even include them in email campaigns.
The YesPlz Advantage
YesPlz’s personalization is constantly improving based on training from multiple, complex retailers.
Our advanced algorithms only need a few data points to start personalizing.
Here’s an example of YesPlz AI personalization for User 1, who prefers trendy styles, and User 2, who prefers minimal chic styles:
1- YesPlz uses pre-trained, advanced fashion AI
Unlike newcomers to the market, we’ve been training our AI with/ more than 1 million data points to understand the details of fashion, from identifying vibes to piecing together attribute similarities to create similar recommendations.
We first defined the core fashion attributes that mattered to shoppers through user interviews, then trained our AI–and continued to refine the tagging results with real fashion experts.
The result: granular, detailed fashion tagging that is the foundation for effective eCommerce recommendations
2- Our recommendation system is built for retailers and includes the flexibility they need
One API gives retailers access to all different types of recommendations, making the integration process seamless.
YesPlz also offers mix-and-match recommendation solutions for retailers, giving them the flexibility to choose which recommendation types work best for their needs.
With YesPlz's AI-powered recommendations, retailers can deliver the personalized, relevant experiences that today's shoppers crave.
By implementing even just one of these 4 basic eCommerce recommendations: Similar Products, Collaborative Filtering, Complete the Look, and Personalization, retailers can take a big step towards making the recommendation experience personalized.
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