Shopify’s Future of Personalization report names the top trends in 2025, and hyper-personalization is at the top of the list. Hyper-personalization is a strategy that uses data and AI to create experiences that are tailored to individual shoppers or customers.
Personalization: Send an email featuring coats and sweaters to people in Minneapolis and sandals to people in Florida.
Hyper-personalization: Not only send an email with tailored clothing recommendations based on weather, but insert the exact products someone recently browsed.
Personalization: Give shoppers a 20% off message once they’ve been on the site for a certain amount of time.
Hyper-personalization: Send a shopper a link to preview a new collection instead based on their unique preferences and habits.
Knowing what hyper-personalization tactic moves the needle requires retailers to actually create and launch tests, which can be daunting when so many retailers are frustrated with the state of their current personalization efforts. But retailers don’t have to let the gaps in their current personalization strategy slow them down from testing hyper-personalization and AI.
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Hyper-Personalization Tests to Try Now
Retailers at nearly any state of personalization maturity can try the following hyper-personalization tests:
Leverage one insight: Retailers should consider which data point they have that defines something important in the customer journey and also is readily available to use for marketing. For Carvana, that data point was the specific car that a customer just purchased. They used that image along with AI video tools to generate unique videos celebrating the customer’s new purchase. With this single bit of information, Carvana was able to take a generic purchase experience and make it hyper-personalized.
Find the data that works: There’s a lot of data out there, but it’s not always easy to access, use or trust. Offline shopping data could be outdated and CDP data might be spotty, with profiles that just don’t match the 360-degree view retailers feel they need. In these cases, retailers should work with partners to find a data set they can be confident in, even if it’s not a full picture of a shopper or customer.
One place to look is on-site shopping data. Not only is the data up-to-date and accurate; it also combines product information and shopper information in one. Retailers are employing shopper data to generate automated triggers that are hyper-personalized, such as “back in stock” notifications and notices when an item that was browsed goes on sale. Or, even cooler, retailers can use AI to analyze shopping data to generate a perfect outfit, suggest a recipe or assemble an automated wish list.
Focus on Customers with Growth Potential
Every retailer has a set of customers that could generate higher profits for the company if only they could be activated effectively. Often, it’s customers that have bought once or twice, but aren’t buying as frequently as loyal shoppers. The “moveable middle” of customers is a good place to focus hyper-personalization efforts.
Here is where AI can be particularly valuable. Retailers can create a predictive model to identify actions that are tied to the potential for future conversion or, even better, long-term loyalty. AI can assess lots of different insights to surface the ones that are most likely to tip customers into a higher-value customer tier. With these insights, retailers can create tailored experiences that are designed to light up a dormant customer.
You Can’t Manage What You Don’t Measure
Marketers often need better measurement to understand the incremental lift that hyper-personalization delivers. Measuring marketing is hard, and data is never perfect.
For example, marketers would need to have a control in place to measure the difference that hyper-personalized marketing delivers across a specific segment and then perform incremental lift measurement. Marketers need to set budget aside and get executive buy-in to perform the tests and the measurement correctly, and they need to keep their eyes on the future, because sometimes personalization’s true value is only seen over months. Knowing this up front can help set expectations that the road may be a bit longer and bumpier than everyone would like.
Jonathan Sherry is Founder and CEO of Alium, an intelligence platform that helps buyers and sellers of marketing and Ecommerce solutions make smarter decisions. Before Alium, he co-founded CB Insights. Over the course of his 11+ years as their COO, he built and led CB Insights to what it is today: the venture industry’s preeminent source of research and intelligence. Sherry holds an MBA from Columbia Business School and a Bachelor of Science in Electrical Engineering from the University of Pennsylvania. He is also a board member and investor in tech startups, venture funds and Broadway productions.
