How Can This Eyewear Site Suggest The Perfect Frames For You?
How Can Cybersecurity Keep Baseball Fans Engaged?
Analyzing In-Session Activity To Understand The Customer
Hyper-personalization requires clean data, but organizing and accessing all of that information isn’t so simple. Dynamic Yield breaks down silos between applications to build a 360-degree view of the consumer and identify timely buying intent signals, says Matthews.
Synchronizing this information across channels—from customer site activity to promotional emails they open—offers a more holistic view of consumer preferences. Dynamic Yield also leverages Mastercard’s aggregated and anonymized insights in line with the company’s robust data principles, ensuring more relevant recommendations with proper use and storage.
Evolving With Changing User Tastes
More Connected Data
Personalization Done Right
Coupling informative and omnichannel data with advanced learning algorithms, brands are providing tailored product recommendations and experiences—and enhancing cybersecurity without disrupting the consumer journey. Privacy and security should be at the forefront of any initiative, and Mastercard believes AI and data-informed tech are only truly effective with trust and proper governance at their core. The company’s data principles promise consumers this: It’s your data, you own it, you should benefit from it and they protect it. That kind of next-level personalization fosters more confidence and loyalty in the long run.
See what’s possible when you partner with Mastercard. Mastercard is working with small businesses around the world to help them benefit from their financial data and stay competitive in an ever-changing environment. Learn More
The traditional recommendation strategy showcases items based on factors like similar search and ratings from other users. It’s effective but slower and not truly personalized, says Matthews. Dynamic Yield’s algorithm not only studies past user behavior but also responds to in-session activity to quickly offer in-the-moment recommendations based on needs and preferences, even for brand new customers.
GlassesUSA tested Dynamic Yield’s approach against standard tactics on its homepage with impressive results: The retailer achieved a 68% increase in purchases and an 88% uplift in revenue. The algorithm was refined and applied to mobile and is now the sole approach used on their homepage across devices.
“Consumers change on a dime,” Matthews says. “So that ability to understand and adapt to preferences in near real-time is hugely important.” Dynamic Yield’s approach prioritizes continuous analysis, learning with each new behavior to deliver quicker, more relevant recommendations.
These insights can also be applied to tailor dynamic content in emails, apps and other channels, creating pertinent and consistent omnichannel interactions that drive sales.
GlassesUSA.com offers eyewear in thousands of styles from dozens of brands. But they faced a problem with delivering bespoke recommendations to customers as they shopped: How could they match site visitors—including new ones—to the right products within their extensive catalog? And how could they account for changing tastes over time?
Enter Mastercard’s Dynamic Yield, an experience optimization platform that deploys a deep learning algorithm and precise data to ensure customers discover the frames they’ll love.
Explore how Dynamic Yield helped this retailer drill down on hyper-personalization:
SOLUTION I: DYNAMIC YIELD
Last year, viewers of the Major League Baseball All-Star Game got to play coach too: MLB invited fans to cast their votes to decide which players would make up the game’s starting lineup. But the organization needed to validate real votes and stop fraudulent voting. To prevent fraudsters and bots from virtually stuffing the ballot box, MLB utilized Mastercard’s NuDetect, a cybersecurity solution leveraging behavioral analytics.
Explore how it improved and secured the MLB
fan experience:
SOLUTION II: NUDETECT
Preventing Fraud While Improving CX
Verification With Less Friction
NuDetect provided MLB with a confidence score for every vote, which also assisted in stopping fraudsters and bots before they could wreak havoc and minimized the need for multiple interceptions that diminish user experience.
NuDetect conducts more than 20 billion risk assessments every year. It ultimately helped MLB facilitate a safer experience for diehard fans to influence their favorite game–and instill trust. “NuDetect helps MLB with integrity, knowing that the fan votes are what they say they are…just like we help retailers ensure that you are who you are at checkout,” says Matthews.
Whether selecting your favorite players or purchasing clothing online, digital identity verification can produce friction for users, leading to frustration and false declines.
By evaluating behavior behind the scenes at multiple touchpoints, including when users create an account, build a profile or check out, NuDetect can build an accurate picture of each user without requiring credentials or personal data. In MLB’s case, fans didn’t even have to set up an account to cast their vote.
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