Mastercard Is Redefining Personalized Retail Experiences With Dynamic Yield

In the ever-evolving world of retail, personalization has moved from a nice-to-have to a must-have. In response to this shift, Dynamic Yield by Mastercard recently launched Shopping Muse, a cutting-edge artificial intelligence (AI) tool that's refreshing the way consumers discover and interact with products online. Dynamic Yield, a company acquired by Mastercard in 2022, is a six-time Leader in the Gartner Magic Quadrant for Personalization Engines.

Shopping Muse strives to understand consumer language, translating informal, colloquial phrases into tailored product recommendations. Users can wade through the river of modern aesthetics, trending styles, dress codes, and even unconventional search terms like "cottagecore" and "beach formal" effortlessly.

The platform leverages Dynamic Yield's personalization capabilities to offer suggestions that align seamlessly with each consumer’s unique profile and preferences — no matter how eclectic they might be. Incorporating advanced image recognition tools, Shopping Muse aids users in tracking down the ideal item even if they're unsure about how to articulate exactly what they're looking for. The platform's ability to recommend items bearing visual similarities, despite lacking precise technical tags, is a game-changer.

Furthermore, Shopping Muse showcases an acute understanding of the user’s shopping behavior, drawing upon browsing history or past purchases to predict future buying intent. It utilizes this knowledge alongside broader collective behavioral data to ensure that the suggested items are complementary and not repetitive.

“Personalization gives people the shopping experiences they want, and AI-driven innovation is the key to unlocking immersive and tailored online shopping. By harnessing the power of generative AI in Shopping Muse, we’re meeting the consumer’s standards and making shopping smarter and more seamless than ever.” 

Ori Bauer, CEO of Dynamic Yield by Mastercard

In this age of rapidly evolving trends and advanced deep learning algorithms, retailers must stay ahead of the curve. More than 25% of retailers are already embracing generative AI solutions, with another 13% planning their adoption within the next year.

Walmart is pushing the boundaries of the shopping experience by integrating GenAI into its search function. This allows customers to make more relevant and specific use case searches, thus saving time and simplifying complex purchases. To enhance customer interaction, Walmart is testing a voice shopping experience on its mobile app, adding to its successful "Text to Shop" feature.

Merging augmented reality (AR) and GenAI, Walmart also offers personalized design assistance tools that consider customers' budgets and theme preferences. Stepping further into the future, Walmart has embarked on virtual commerce opportunities. This innovative feature, already introduced in the game "House Flip," allows customers to make contextual purchases of physical items in virtual environments.

Google's Search Generative Experience (SGE) is using AI to ease holiday shopping with tailored gift suggestions and a variety of product options from diverse brands. The AI also connects users to additional content and links for further exploration. A new feature employing AI-powered image generation will help users visualize and shop for apparel based on their unique search descriptions. Also, Google's virtual try-on tool is now extended to men's tops, allowing shoppers to preview products on models with diverse representations to make more confident purchase decisions.

According to Amazon, the retail giant evolved its item-based collaborative filtering in 2003. Unlike the previous user-based method that suggested items based on similar users' preferences, Amazon's algorithm starts by finding items related to each product in the catalog. This "relation" refers to how frequently two items are purchased together.

Once this "related items" database was established, the algorithm was quickly able to generate recommendations by matching a user's current context and past interests with related items while filtering out those they've already seen or bought. This method vastly speeds up the recommendation process, allows for real-time recommendations, and can scale to cater to millions of users and items without compromising on quality. The algorithm is also continually updating, absorbing new information about users' interests. It's user-friendly too, offering intuitive explanations for its recommendations based on the customer's past purchases.

As this technology continues to rapidly evolve, consumers will have a plethora of virtual and AI shopping assistants — possibly more than they can handle.