Hyper-Personalization in Retail: 7 Examples of Retailers Doing It Right

Hyperpersonalization is revolutionizing the retail industry by offering a more tailored and individualized shopping experience for customers.

Hyper-Personalization In Retail: 7 Examples Of Retailers Doing It Right

Hyperpersonalization is revolutionizing the retail industry by offering a more tailored and individualized shopping experience for customers. As technology continues to advance, particularly in the fields of artificial intelligence (AI) and machine learning, hyperpersonalization is becoming increasingly important for businesses to stay competitive and meet customer expectations.

One of the key benefits of hyperpersonalization in retail is the ability to send targeted promotions to customers based on their purchase history and time preferences. This approach helps build loyalty, improve engagement, and keep customers coming back. In fact, nearly half (49 percent) of consumers say they are likely to become repeat buyers after a personalized shopping experience with a retailer.

The future of hyperpersonalization in retail is promising, as it enables businesses to better understand their customers’ true wants and needs. By leveraging real-time data and other sourced consumer data, retailers can strengthen relationships, boost customer loyalty, and positively impact their bottom line.

As the retail market reached $27 trillion by 2022, it is crucial for businesses to adapt and embrace hyperpersonalization strategies to stay ahead of the competition.

What is hyper-personalization?

Hyper-personalization is a marketing strategy that uses data, analytics, AI, and automation to create custom and targeted experiences for individual customers. This approach uses artificial intelligence (AI), real-time data, and machine learning to analyze customer preferences and behavior, enabling brands and retailers to offer unique products and content tailored to each individual shopper.

Retailers can also monitor multiple parallel journeys for better personalization, blending conceptual data and context for real-time reactions, and unifying customer data for more accurate hyper-personalization. Starbucks is an example of a brand that excels at hyper-personalization, using AI to send users unique offers based on their preferences, activity, and past purchases.

In retail, hyper-personalization is becoming increasingly important as customers expect tailored experiences and highly curated product offerings. Retailers are using various hyper-personalization methods to improve customer experience and increase profits.

Hyper-personalization in Retail

By implementing multichannel options that store customer information across all online and offline channels, retailers can provide a seamless shopping experience that keeps customers coming back for more. Hyper-personalization combines behavioral and real-time data to anticipate the needs of customers, and it pays dividends in customer loyalty, repeat purchases, and upsells.

It also helps retailers reduce customer churn by retaining existing customers through targeted promotions based on their purchase history and time preference. For example, a shopper browsing a website for beauty supplies might simultaneously receive recommendations for similar products based on their specific search history. This level of personalization can incentivizes customers to return to a retailer’s website or brick-and-mortar location and make repeat purchases, ultimately increasing customer lifetime value.

Let’s dive into the retailers who are utilizing the hyper-personalization strategy the best.


Amazon is a prime example of a company that excels in hyper-personalization, creating custom and targeted experiences for its customers based on their unique preferences, browsing history, and purchase behavior. By leveraging advanced technologies such as artificial intelligence (AI) and machine learning algorithms, Amazon is able to provide highly relevant product recommendations and tailored content to each individual user, enhancing the overall customer experience.

One way Amazon achieves hyper-personalization is through its recommendation engine, which uses AI to analyze a customer’s past purchases, browsing history, and other data points to suggest products that are likely to be of interest to them. This not only helps customers discover new items they might enjoy but also makes the shopping process more efficient and enjoyable.

Another aspect of Amazon’s hyper-personalization strategy is its use of omnichannel marketing, which ensures a seamless and consistent customer experience across all touchpoints, whether a customer is shopping on the website, using the mobile app, or interacting with customer service. This integrated approach allows Amazon to better understand its customers’ needs and preferences, enabling the company to deliver a more personalized experience.

Amazon has also ventured into personalized fashion with its “Made for You” service, which uses customer data to create custom-fit t-shirts. Customers provide information on their height, weight, and sizing, as well as two photos of themselves. They can then choose from various fit and fabric options to create a shirt tailored specifically to their measurements.

In addition to product recommendations, Amazon also employs hyper-personalization in its marketing efforts, sending targeted promotions and offers to customers based on their purchase history and preferences. This helps build loyalty and engagement, as customers are more likely to respond positively to marketing messages that are relevant and tailored to their interests.



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A post shared by Starbucks Coffee ☕ (@starbucks)

Starbucks is a prime example of a company that has successfully implemented hyper-personalization in its marketing strategy. With over 24,000 locations worldwide, Starbucks has access to an extensive set of customer data, which it uses to create highly personalized experiences for its customers.

n 2016, Starbucks began its journey towards hyper-personalization by initially sending 30 variants of emails per week based on basic personalization. However, this method proved to be time-consuming and unscalable. To enhance their efforts, Starbucks transitioned to hyper-personalization, resulting in the creation of 400,000 email variants per week, 1:1 personalized offers in their app, and 1:1 personalized recommendations within the Starbucks app.

For instance, imagine a customer who frequently visits Starbucks on weekday mornings and orders a specific type of coffee. With hyper-personalization, Starbucks can send this customer a tailored offer for their favorite drink on a Monday morning, increasing the likelihood of a purchase and fostering brand loyalty.

The Starbucks app utilizes advanced AI technology to generate food and beverage suggestions tailored to each customer’s preferences, purchase history, and behavior. For instance, a customer may receive a free drink on their birthday, a push notification when they are near a Starbucks store, or a promotion for an iced coffee on a particularly hot day.

The rewards program has also seen an increase from five million to 12 million customers during this period. By understanding and catering to individual preferences and behaviors, Starbucks has been able to foster customer loyalty, drive up transactions, and ultimately, achieve measurable business results.



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A post shared by Prose: Custom Haircare (@prose)

One notable example of hyperpersonalization in action is Prose, a direct-to-consumer (DTC) hair care brand that offers personalized products tailored to each customer’s unique needs.

Prose‘s approach to hyperpersonalization begins at the very start of the customer journey. When a user visits their website, they are prompted to complete an online consultation. This consultation gathers essential information about the customer’s lifestyle, dietary habits, geographic location, stress levels, and more. Using a proprietary algorithm developed in-house, Prose then utilizes this data to create individualized hair care products that address the customer’s specific needs and concerns. Each product even comes in a bottle marked with the customer’s name.

This level of personalization has proven to be highly effective for Prose. Since its launch in 2017, the brand’s customer base has increased by more than five times, and they have received over one million completed consultations. The company’s success can be attributed to its ability to make customers feel seen and heard, providing them with a truly personalized shopping experience.

In addition to the online consultation, Prose also leverages technology to optimize its supply chain and order fulfillment processes. As the company grows, it continues to find innovative ways to evolve its use of technology, enabling it to increase order production while maintaining customer satisfaction.



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SHEIN has successfully implemented hyper-personalization, a fast-fashion retailer that offers a scrollable feed of products powered by a real-time recommendation algorithm. This algorithm takes into account various data points from social media and other channels to provide personalized product suggestions for each customer.

One way SHEIN achieves a high level of personalization is by allowing members to set their preferences and earn bonus points for doing so. To join the SHEIN Rewards program, customers simply create an account and provide basic details. Once enrolled, members can earn points through various activities such as spending, posting helpful reviews, and engaging in non-transactional activities like creating outfit vision boards.

Members can choose their favorite categories, who they buy clothes for, and their style preferences. Based on these preferences and previous searches, SHEIN’s main shopping page displays products that are most likely to appeal to the member, resulting in a more personalized experience and increased sales for the company.

Another aspect of SHEIN’s hyper-personalization strategy is its review system. After making a purchase, members are encouraged to review their items, post photos, and share their size measurements. Other members can then give a thumbs up to helpful reviews, which in turn earns the reviewing member more SHEIN Points. This system not only incentivizes members to share their experiences but also theese user-generated reviews contribute to a highly personalized shopping experience, as customers can see how products look on real people before making a purchase.

ILIA Beauty


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ILIA Beauty, a clean beauty brand, is an excellent example of a company utilizing hyper-personalization to enhance customer engagement and loyalty. By leveraging data and artificial intelligence, ILIA creates tailored experiences for its customers, making them feel valued and understood.

One way ILIA achieves this is through personalized emails. These emails are sent to customers at strategic times, such as when it’s time to reorder a product or providing information on how long the product typically lasts. This targeted communication ensures that customers receive relevant information, making them more likely to engage with the brand and make repeat purchases.

Another way ILIA uses hyper-personalization is by creating a branded post-purchase experience. After a customer makes a purchase, they are directed to a shipment tracking page that contains educational videos about how to care for and use their newly purchased product. The page also includes shipment FAQs, referral program information, and details about ILIA’s mission. This customized post-purchase experience not only educates customers but also fosters a deeper connection with the brand. As a result, returning traffic from ILIA’s updated tracking pages converts 25% higher than their site average.

Stitch Fix


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A post shared by Stitch Fix (@stitchfix)

One notable example of hyperpersonalization in fashion is Stitch Fix, a styling service that curates personalized clothing selections for customers based on their tastes and needs.

To achieve this level of personalization, Stitch Fix collects a wide variety of data from customers, including their sizes, style preferences, and lifestyle information. This data is gathered through pictures, social media profiles, surveys, and more. The company utilizes a discovery tool called “style shuffle” to help users indicate their preferred designers and styles.

Stitch Fix has also expanded its services beyond its core subscription model with the launch of Freestyle, a personalized shopping experience that allows anyone to buy items anytime without being a current subscriber. Freestyle offers customers a personalized online shopping feed with products curated based on their style profile, size, fit, and preferences.

The company then uses algorithms to analyze this data and pinpoint exactly what each customer is looking for in their clothing selections. However, Stitch Fix doesn’t rely solely on technology to curate personalized clothing boxes for its customers. The company employs a team of expert stylists who work alongside data scientists to ensure that the final selection of items in each box has a human touch. This combination of technology and human expertise allows Stitch Fix to offer a unique and personalized shopping experience that caters to individual preferences and needs.

One of the key aspects of Stitch Fix’s hyperpersonalization strategy is the incorporation of customer feedback into its model. After receiving their customized box of clothing, customers can decide which items they want to keep and provide specific feedback on each item through a follow-up survey. This feedback helps the company and its stylists improve future selections and maintain a high retention rate among customers.

By analyzing customer data and preferences, Stitch Fix can provide highly accurate, fashion-oriented recommendations that cater to each customer’s unique style.


Adewunmi is a Canadian-owned beauty brand that invites visitors to their website to choose the natural ingredients best suited to their needs and skin type, which are then mixed to create customized skincare products. In addition to skincare products, the founder of Adewunmi is working on personalized skincare technology and robotics to bring to major retailers and brands worldwide.  The benefits of hyper-personalization in the beauty industry include helping customers make informed decisions based on their preferences, building loyalty through better engagement, and empowering individuals to take control of their own definition of beauty.

Final Thoughts

Some challenges that retailers may face when implementing hyperpersonalization include ensuring they do not store more information than necessary and disclosing when and how that information is used. However, overcoming these challenges is essential for businesses to provide the level of personalization that customers have come to expect.

In conclusion, hyperpersonalization is shaping the future of the retail industry by providing customers with a more customized and engaging shopping experience. As AI and machine learning technologies continue to advance, retailers must adapt and invest in hyperpersonalization strategies to stay competitive and meet the evolving demands of their customers. By doing so, businesses can expect to see increased customer loyalty, repeat purchases, and overall growth in the ever-changing retail landscape.