In the dynamic world of online shopping, Google
GOOG
is pioneering a more personalized and efficient way for consumers to find exactly what they’re looking for, tapping into the potential of its Shopping Graph.
Each day, shoppers are presented with billions of product listings through the platform. These listings are dynamic and non-static; continually updated every hour with the latest information on pricing, availability, and shipping details for an extensive range of products.
This much optionality, however, can also produce a point of friction that results in what psychologists call Choice Overload, wherein buyers become paralyzed by having too many options to choose from and end up taking no action at all.
In an effort to tackle this issue and further streamline and personalize the shopping journey, Google has introduced several new features aimed at delivering a more customized, efficient shopping experience to its users.
Google’s newly introduced “style recommendations” feature, for example, allows users to rate apparel, shoes, and accessories by swiping or clicking a thumbs up or down.
This interactive feedback mechanism is designed to refine and personalize the search results in real-time, ensuring shoppers can discover products that align with their tastes more swiftly. Brands, too, can use this feedback data to further optimize their Google Shopping product listings.
“People shop on Google more than a billion times a day, and with more than 45 billion product offers to choose from in Google’s Shopping Graph, we’re making it easier for people to find the perfect thing,” said Sean Scott, VP/GM of Consumer Shopping at Google.
“With these features, shoppers can more easily tailor their experience and discover more of the products they like in just a few clicks.”
Along with this, Google has focused on AI image generation to address the challenge of finding apparel that matches a shopper’s exact mental picture when searching for products online.
Now, by entering detailed descriptions, users can generate photorealistic images of potential outfits and items, which Google then matches with shoppable options available on its platform.
From there, the product discovery process can improve further with Google’s new virtual try-on (VTO) tool. Using the tool, shoppers can see how clothes look on a diverse range of models between sizes XXS-4XL across various different skin tones, body shapes, ethnicities and hair types.
The goal of these features: to help shoppers get a more realistic product view pre-purchase, eliminate uncertainty during the product discovery and research phases, and ultimately, to cut down on returns, which result in more than $101 billion in annual losses for brands, according to the National Retail Federation.
“Combined with AI image generation and virtual try-ons, tools like this will help make shopping online easier and more personalized than ever for consumers,” said Keith Fix, CEO of shopper behavior data company Retail Aware.
“For brands, this should translate into higher sales from better targeted product discovery, increased loyalty as shoppers can specify which brands they like, and we’ve seen how virtual try-ons can reduce return rates lowering overall direct-to-consumer costs in apparel.”
Google’s focus on personalization and AI-enhanced shopping experiences is an attempt to help both shoppers and retailers overcome the long-standing hurdles around the online shopping experience.
As Google and other ecommerce facilitators continue to evolve existing shopping features, staying ahead in leveraging these tools will be crucial for retailers aiming to maximize their online presence and connect with their audience in meaningful ways.
