Personalization at Scale: How AI and ML Are Powering One-to-One Retail Experiences
By Dheeraj Trikha, Vice President, ML/AI & Analytics, Designer Brands
In today’s retail landscape, consumers expect every interaction to feel tailored, timely, and relevant. From product recommendations to promotional offers, the benchmark is set not by a brand’s historical strengths, but by tech-driven leaders capable of delivering seamless, hyper-personalized experiences. The challenge? Doing this not for hundreds or thousands, but for millions of customers, across every channel, IN REAL TIME. This is where AI and machine learning, and GenAI are fundamentally reshaping the art and science of personalization.
For years, retailers relied on broad customer segments and static rules to guide personalization efforts. While helpful, these approaches quickly show their limitations in a world where customers’ preferences evolve rapidly, and engagement is often fleeting.
AI and ML flip this equation. By analyzing massive volumes of real-time data, from browsing to behavior, purchase history, location, and even social sentiment, these technologies allow retailers to move beyond static segments to understand intent, predict needs, and deliver relevant experiences as they unfold.
According to McKinsey research, businesses who are successful at personalizing can increase their revenue by 40% over those that are not. Personalization isn’t just a customer experience play; it’s a REVENUE GROWTH ENGINE.
AI, ML and now GenAI give retailers the tools to meet this challenge head-on, delivering the kind of experiences that not only drive sales, but also build lasting brand loyalty.
Real World Impact / Personalization at Work:
AI models today factor in browsing patterns, complementary products, and even subtle behavioral cues to suggest items with remarkable accuracy, boosting conversion rates and average order value. Similarly, AI-powered marketing campaigns optimize timing, messaging, and channel selection, ensuring outreach feels personalized rather than generic.
Recently, a multinational retailer of sportswear attributed a double-digit increase in online conversion rates to its AI-powered personalization engine. Another major beauty brand utilizes machine learning to provide personalized product suggestions tailored to skin type, purchase history, and preferences, resulting in enhanced customer satisfaction and increased repeat purchases.
In-store, AI-enabled clienteling tools can equip associates with real-time customer insights, allowing them to deliver tailored recommendations that mirror the digital experience. The result? A truly connected, omnichannel journey that builds loyalty and drives revenue.
Delivering this level of personalization isn’t simply about plugging in a new AI model. It requires robust data foundations, such as unified customer profiles, real-time data pipelines, and scalable AI/ML infrastructure. Companies must also invest in governance frameworks that ensure data privacy, model transparency, and ethical AI use.
Customer data platforms (CDPs) are becoming a key enabler. By aggregating data from disparate sources, CDPs provide a unified view of each customer, empowering AI to make accurate, relevant recommendations. Combined with experimentation platforms and real-time decision engines, retailers can continuously test, learn, and refine personalization strategies.
While AI is powerful, the most effective personalization strategies blend automation with a human touch. Retailers who use AI to enhance, NOT REPLACE, human connection strike the right balance. Customers value efficiency, but they also value authenticity, empathy, and choice.
This is particularly true for in-store experiences, where associates equipped with AI-powered insights can engage in meaningful, personalized conversations. It’s also evident in customer service interactions, where AI can handle routine tasks, freeing human agents to focus on complex, high-value conversations.
Personalization 2.0:
Generative AI has the potential to revolutionize personalization. The possibilities are endless, ranging from creating customized product descriptions to enabling conversational AI that offers one-on-one purchasing support. Imagine a world where every product page dynamically adapts to each customer’s preferences, or where AI chatbots offer personalized style advice as naturally as a store associate.
Almost every retailer today is already experimenting with Generative AI to generate personalized marketing content, simulate virtual try-ons, and even build AI-powered shopping companions that learn and adapt to each customer over time.
Challenges to Consider
Data privacy remains paramount, with evolving regulations like GDPR and CCPA demanding strict controls and transparency. Algorithmic bias and model drift can undermine personalization efforts if not carefully monitored. Retailers must also guard against “creepy” personalization that feels invasive rather than helpful.
Beyond technical hurdles, organizational silos often limit personalization success. For AI to be effective, data must flow freely across marketing, e-commerce, customer service, and in-store operations. Companies that break down these barriers and foster true cross-functional collaboration will be best positioned to deliver seamless, consistent experiences.
Additionally, measurement is critical. Retailers must define clear success metrics, whether it’s conversion uplift, customer lifetime value, or net promoter score, and continuously track the impact of personalization initiatives. This data-driven feedback loop ensures efforts remain aligned with business goals and customer expectations.
At a time when customer expectations are soaring and competition is fierce, personalization at scale is no longer optional. AI, ML and now GenAI give retailers the tools to meet this challenge head-on, delivering the kind of experiences that not only drive sales, but also build lasting brand loyalty.
The future belongs to retailers who can anticipate needs, delight customers, and make every interaction feel personal, at scale. With the right technology, talent, and data foundations, that future is within reach.