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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.
Discussion Questions
Given the rapid growth of personalized retail experiences, how do you see generative AI tools like Shopping Muse altering the landscape of online retail in the coming years? What implications would this shift to AI-driven personalization have for traditional brick-and-mortar retailers?
Considering the unique applications of generative AI in e-commerce, how crucial is the integration of these technologies for small to medium-scale enterprises? More specifically, how can these businesses approach the adoption of such technologies to boost their competitive edge while managing the associated costs and risks?
Poll
Loading …BrainTrust
David Naumann
Marketing Strategy Lead - Retail, Travel & Distribution, Verizon
Gene Detroyer
Professor, International Business, Guizhou University of Finance & Economics and University of Sanya, China.
We went from a highly curated retail world (what you could get in any given store was determined by the retailer an their expert buyers) to a world of endless choice, endless aisles, and low curation. I think personalization is a way to reintroduce curation and personal experiences to retail. There is a lot of paralysis by analysis in the market.
I’m sure this will excite some, and creep out just as many – based largely on whether the thought of AI gives you tingles…or just a cold chill – but exactly how useful it will be I don’t know: recommendations based on a simple analysis of past purchases have been available for years, and while many times they miss – sometimes famously – I’m thinking they’re still more useful than not; so this is perhaps a new variation of the ’80/20 rule’: if it gets us to – say – a 96% success rate, it sounds wonderful…until we realize we were already 80% of the way there.
If I could just tell the Algorithm, “stop telling me about X, my need for it is completed / considered and rejected it / was just browsing” my experience would be so much better. I bought the oven, I’m not going to need another oven until I move somewhere else, stop telling me about ovens.
This will actually change the game. However, the promise of “personalization” has been made for decades – consumers want to see a demonstrable improvement over what they have experienced in the past. I think A.I. will, if not now, in the very near future. This domain is moving so rapidity it’s virtually impossible to keep up. When refined and optimized, A.I personalization will drastically improve the probability a shopper gets exactly what they wanted. With enough data to feed it, it will become eerily accurate. All businesses need to consider how A.I. impacts the products/services they deliver today and ask, ‘How does A.I. help me deliver more value to clients and/or save expense?” I’d advise smaller enterprises to start small. Identify a practical problem/opportunity, and assign a team member to lead the investigation and implementation, reporting to a small steering committee. Don’t just rely on outside advisors – there will always be wild claims, but you need to determine which will truly deliver value.
Mastercard has a lot of data on people’s purchasing habits and preferences. In the past, using this goldmine of data effectively has been challenging. AI is a potential game-changer here as it will allow the data to be used far more intensely in terms of making relevant recommendations. Of course, this raises all kinds of ethical questions over how the data are shared and used, but I have no doubt that this is a potentially useful application of AI.
The race is clearly on for finding and deploying AI-powered retail wherever it seems to fit. It’s true that retailers have been leveraging shopper history and preferences for decades, but constant advances in AI—especially in image use—are making new algorithms possible.
Let’s not forget the ability to include user-generated content (UGC) in the mix. The recommendation engine needs to feed unpaid influencers’ content into the AI box, too. I think the most powerful feature of what we’re talking about here is the natural language processing and cross-referencing that to fashion, furnishings, and other items seen in shows and movies. “Where can I buy that jacket that the star of that show about time travel was wearing?” This is vague, but AI could figure this out and send the shopper straight to a PDP with the item.
What retailers must avoid is the bubble effect. Just like news preferences where people only see and hear what they’re used to, only suggesting items similar to what a shopper has already bought can be extremely limiting.
Amazon has been the leader in online shopping personalized recommendations and the new AI tools that are available to any company that can afford it will level the personalization playing field. As a shopper, anything that retailers can do to make my shopping more efficient and effective in finding the best fit for my needs will make me more loyal to their brand. AI tools will also make it easier for small and mid-sized retailers to compete more effectively. It is all good, as long as it doesn’t infringe on consumers’ privacy.
We are in the ice age of this technology and there is a long road ahead before these tools develop real usefulness.
If Shopping Muse can turn the “endless aisle” into the “curated/personalized aisle”, then I am thumbs-up. I understand how it would know my purchase history, but how does in know my shopping behavior? Websites visited? Google searches? Bing searches? Am I going to opt-in to some new process and a year from now my internet browsing behavior will all be on file some where? Hmmmmmmm… Some of this starts to sound very Orwellian.
Personalized experiences at scale empower retailers to establish stronger connections with their customers, helping to increase sales by 1 to 2 percent, build brand loyalty, and ultimately enhance customer lifetime value. Additionally, personalization strategies have contributed to 20 percent higher customer satisfaction rates, a 10 to 15 percent boost in sales conversion rates, and an increase in overall employee engagement of 20 to 30 percent. Personalization strategies have evolved across channels as consumers seamlessly navigate physical, digital, social, and live-streaming platforms to shop with their favorite brands and retailers.
Customer-first commerce operating models require resilient and scalable strategies as third-party cookies are phased out and the industry shifts towards relying more on first-party data. Publicis Sapient conducted a research program among more than 6,700 consumers globally to understand their opinions on customer data and why, how, and when they are willing to share their personal data with organizations. The key is to establish a relationship of trust and transparency, as consumers worldwide have concerns about what data they share and how companies leverage it.
These concerns include:
If AI shopping tools ultimately do the perfect shopping for the shopper, is there anything for the shopper to do?
It’s increasingly expected that experiences are personalized for shoppers. Generative AI tools like Shopping Muse are poised to revolutionize online retail by offering these highly personalized experiences, especially given the vast datasets provided. Data enables retailers to understand individual preferences and tailor recommendations accordingly. Traditional brick-and-mortar retailers may face challenges in keeping up with the personalized experiences offered by online platforms because the shift to AI-driven personalization emphasizes the need for innovation and technology adoption in physical retail spaces. However, there are opportunities for traditional retailers to integrate AI tools in-store, which can enhance the in-person shopping experience and assist in inventory management and customer engagement.
Smaller enterprises should not feel left out either, as they can leverage similar AI and personalization tool sets offered by third parties to stay competitive. This can be advantageous, as SMEs will be more agile and thus faster to bring things to market. It’s crucial that small enterprises participate to remain competitive, but they need to strategically plan their investment and consider scalable solutions that align with their growth trajectory. SMEs should consider a gradual adoption approach, starting with specific use cases like personalized product recommendations or targeted marketing. Incremental integration allows them to manage costs and risks while gauging the impact on customer engagement.