c-suite

Are you ready to take AI-led personalization in Retail to the next level?

insight
December 06, 2024
9 min read


Rahul Mahajan is Chief Technology Officer  (CTO) at Nagarro. He pushes the boundaries of what is possible for the customers.

To increase retail profitability, retaining existing customers is just as important as attracting new ones. Maximizing customer lifetime value (CLV) is key, and personalization is one of the most effective ways to achieve this. Tailored experiences, offers, and communication create loyalty, increase satisfaction, and help retailers stand out in a crowded marketplace. 

Retailers in various sectors are increasingly using AI to personalize the customer experience. L'Oréal and Sephora are leading the way, offering tailored product recommendations based on data such as purchase history, browsing behavior, and personal characteristics such as skin type. Other retailers, such as Macy's, Nordstrom, and Amazon, are also using AI for personalized marketing, product suggestions, and pricing strategies. Walmart and Target are using AI to optimize inventory and develop tailored promotions based on customer preferences and seasonal trends. This change not only improves customer retention but also drives long-term loyalty and growth for these retailers 

This article looks at the different levels of AI-driven personalization and its impact on the customer experience. Watch how businesses that leverage greater personalization not only increase customer engagement but also build long-term loyalty, paving the way for sustainable growth and customer retention. 

Advancing retail personalization: from basic to ultra-personalized  

 

 

Level 0: Minimal personalization

Many businesses rely on minimal personalization and offer generic recommendations that lack context, such as recommending trips that customers have already booked. Disjointed channels online, on mobile devices, and in call centers create fragmented experiences that frustrate users and undermine trust. This approach risks customer churn and misses important opportunities to improve customer loyalty and satisfaction. 
AI-personalization in retail_7

Level 1: Basic predictive personalization 

Basic predictive personalization in retail boosts customer loyalty but lacks depth in connecting with individual customers. Retailers use simple models, such as rule-based recommendations or broad demographic segmentation, that do not take into account differentiated preferences or behaviors. For example, e-commerce sites suggest products based on frequent purchases, while physical stores rely on general purchase histories. 

While these methods improve customer loyalty, they do not take into account the complexities of modern shopping, such as multi-channel interactions or individual product preferences. As a result, customers may perceive these experiences as too generic, so their expectations of true personalization are not met. 
AI-personalization in retail

Level 2: Advanced AI/ML-based personalization

Advanced AI/ML-based personalization is transforming the retail experience with techniques such as collaborative filtering and propensity modeling, enabling highly tailored recommendations, dynamic pricing, and personalized promotions. These strategies adapt in real-time and increase customer loyalty through triggers such as abandoned cart reminders or reorder notifications. While these methods outperform basic predictive approaches, they often operate in silos, limiting seamless, consistent experience across all channels. To realize their full potential, retailers need to break down these silos and ensure a unified and effective customer journey.
AI-personalization in retail

Level 3: Humanized and ultra-personalized customer experiences (CX)

Level 3 represents the pinnacle of personalization in retail, where technology and customer insights come together to create deeply tailored experiences. At this stage, retailers go beyond basic personalization by leveraging micro attributes, IOT sensor data sets, and a unified ecosystem to understand and respond to individual customer needs in real time. This level focuses on four key pillars: a higher level of personalization that taps into an interconnected ecosystem, a deeper understanding of user context, embedding emotional value into the customer’s lifestyle, and delivering experiences that are simple and human-friendly. By combining these elements, retailers can create meaningful, seamless interactions that not only meet customer expectations but also build lasting relationships, foster loyalty, and drive business growth.
AI-personalization in retail

 

Level 0: Minimal personalization

Many businesses rely on minimal personalization and offer generic recommendations that lack context, such as recommending trips that customers have already booked. Disjointed channels online, on mobile devices, and in call centers create fragmented experiences that frustrate users and undermine trust. This approach risks customer churn and misses important opportunities to improve customer loyalty and satisfaction. 
AI-personalization in retail_7

Level 1: Basic predictive personalization 

Basic predictive personalization in retail boosts customer loyalty but lacks depth in connecting with individual customers. Retailers use simple models, such as rule-based recommendations or broad demographic segmentation, that do not take into account differentiated preferences or behaviors. For example, e-commerce sites suggest products based on frequent purchases, while physical stores rely on general purchase histories. 

While these methods improve customer loyalty, they do not take into account the complexities of modern shopping, such as multi-channel interactions or individual product preferences. As a result, customers may perceive these experiences as too generic, so their expectations of true personalization are not met. 
AI-personalization in retail

Level 2: Advanced AI/ML-based personalization

Advanced AI/ML-based personalization is transforming the retail experience with techniques such as collaborative filtering and propensity modeling, enabling highly tailored recommendations, dynamic pricing, and personalized promotions. These strategies adapt in real-time and increase customer loyalty through triggers such as abandoned cart reminders or reorder notifications. While these methods outperform basic predictive approaches, they often operate in silos, limiting seamless, consistent experience across all channels. To realize their full potential, retailers need to break down these silos and ensure a unified and effective customer journey.
AI-personalization in retail

Level 3: Humanized and ultra-personalized customer experiences (CX)

Level 3 represents the pinnacle of personalization in retail, where technology and customer insights come together to create deeply tailored experiences. At this stage, retailers go beyond basic personalization by leveraging micro attributes, IOT sensor data sets, and a unified ecosystem to understand and respond to individual customer needs in real time. This level focuses on four key pillars: a higher level of personalization that taps into an interconnected ecosystem, a deeper understanding of user context, embedding emotional value into the customer’s lifestyle, and delivering experiences that are simple and human-friendly. By combining these elements, retailers can create meaningful, seamless interactions that not only meet customer expectations but also build lasting relationships, foster loyalty, and drive business growth.
AI-personalization in retail

 

Personalization levels: a comparison Levels of AI Personalization


Principles of Level 3— Humanized and ultra-personalized customer experiences (CX)

A higher level of personalization taps into an interconnected ecosystem, a deeper understanding of user context, embedding emotional value into the customer’s lifestyle, and delivering experiences that are simple and human-friendly. By combining these elements, retailers can create meaningful, seamless interactions that not only meet customer expectations but also build lasting relationships, foster loyalty, and drive business growth. 

 

Leveraging ecosystem
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Level 3 achieves advanced personalization by leveraging an interconnected ecosystem of data from IoT devices, in-store sensors, mobile apps and online platforms. This unified approach enables seamless, adaptive experiences that are tailored to customers’ lifestyles and preferences.

For example, a customer looking to buy an evening dress could receive personalized suggestions for accessories, make-up tips and local tailoring services based on previous interactions. By providing such hyper-relevant experiences, retailers build trust, create value and become an integral part of the customer journey.
 

Deeper understanding of the user context
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Personalization focuses on a deeper understanding of the user context and uses real-time insights from customer behavior, preferences, and environment. Retailers analyze data from online interactions, in-store activities, and IoT-enabled devices to anticipate needs and offer solutions tailored to individual situations. 

For example, a customer planning a beach holiday could receive recommendations for weather-appropriate skincare, travel-friendly outfits, and exclusive discounts on relevant accessories. By tailoring offers to the customer’s specific context, retailers create experiences that feel intuitive, relevant, and truly supportive, fostering loyalty and trust.  

Emotional with value embedded into customer lifestyle
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Personalization goes beyond functional recommendations by embedding emotional value into the customer’s lifestyle. Retailers use deep data insights to understand customers’ desires, challenges and emotions and offer solutions that resonate on a personal level. This approach builds emotional connections, making the shopping experience feel meaningful and aligned with the customer’s life goals. 

For example, imagine a customer dreaming of glowing skin like their favorite influencer. Advanced personalization offers tailored skincare recommendations, personalized routines and exclusive discounts, all aligned with their beauty goals. Through an intuitive, user-friendly interface, this experience creates an emotional connection and demonstrates that the retailer truly understands and supports the customer's desires. This empathy-based approach builds trust, encourages loyalty and turns the shopping trip into a meaningful partnership.


Simple and human-friendly communication
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Personalization should be simple and human-friendly, ensuring that advanced technology enhances the customer's experience rather than complicating it. Retailers are focusing on intuitive interfaces and seamless interactions that make it easy for customers to access personalized recommendations without feeling overwhelmed. The goal is to offer relevant solutions without unnecessary complexity effortlessly. 

For example, a customer could receive personalized skincare recommendations based on their preferences, delivered via a user-friendly mobile app with clear, actionable steps. This simplicity, coupled with the high level of personalization, makes the experience feel natural and approachable, reinforcing a sense of trust and making the customer feel valued at every touchpoint.

Real-world use cases

Building blocks of Level 3— Humanized and ultra-personalized customer experiences (CX)

Leveraging ecosystem
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  • Ecosystem integration
By integrating first-party and partner catalogs, businesses can provide a diverse product range on a unified platform. This broadens customer choice, enhances satisfaction, and positions the brand as a one-stop solution for varied needs, streamlining the shopping journey. 

  • ML-based Next Best Action (NBA) Advisory

NBA advisory, using machine learning, analyzes customer interaction history and preferences to recommend precise next steps. By connecting to an extended product ecosystem it ensures meaningful interactions, driving loyalty and long-term retention. 

 

AI personalization- leveraging ecosystem

Deeper understanding of the user context
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  • Deep product affinity model 

By leveraging time series data indexing, this model deciphers historical customer interactions to predict future behaviors. It anticipates what will resonate with each customer, transforming casual shoppers into loyal patrons while boosting engagement across all channels. 

  • Advanced feature engineering 
Seasonality and SKU propensity analysis ensures that offers and inventory align with customer demand. For example, winter skincare promotions or summer travel gear are timely and relevant and enhance satisfaction while optimizing inventory and marketing ROI. 
AI-personalization-Deep Product Affinity Model

Emotional value embedded into customer lifestyle 
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  • Micro-signals from beauty-tech IoT 

IoT devices capture real-time micro-signals like skin hydration or hair texture. These insights power hyper-personalized recommendations, such as skincare tailored to a customer’s current condition, fostering loyalty through deep resonance and satisfaction. 

  • Dynamic offers and promotion planning 

Context-aware promotions combine strategies like bundling, exclusive discounts, and complementary product suggestions. These personalized offers strengthen brand affinity and create emotional connections by aligning with customer preferences. 

AI-personalization- emotional value

Simple and human-friendly communication 
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  • Channel-Specific Templates
Customer communication is customized with precise timing and format. For instance, lunchtime notifications for quick meal deals or evening emails about skincare routines match customer schedules, boosting engagement and loyalty. 

  • LLM & Gen-AI for summarization

Large language models and generative AI ensure tailored, brand-consistent messaging. Whether crafting offers or responding to queries, this technology delivers personalized yet coherent communications, strengthening brand trust and recognition. 

Precise AI-personalization

Future AI trends in retail personalization: AI-driven playbooks for automation 


AI-driven playbooks will transform retail personalization by automating key workflows and delivering real-time, dynamic recommendations for promotions, product content, and pricing. These playbooks ensure every customer interaction is tailored to individual preferences, while advanced simulations optimize strategies for different retail scenarios. This level of automation boosts efficiency, enabling retailers to continuously refine their approaches and deliver hyper-personalized experiences that enhance customer satisfaction, increase engagement, and build long-term loyalty. 

Rahul Mahajan

 

 

Endnote- It's time to elevate retail personalization. 


To be successful in 2025, retailers will need to expand their personalization strategies beyond simple approaches. Deploying advanced, AI-driven solutions that deliver highly personalized experiences is key to strengthening customer loyalty, increasing conversion rates, and remaining competitive. Retailers that scale their personalization efforts will strengthen relationships, foster long-term loyalty, and position themselves for sustainable growth in a rapidly evolving market. 
Are you ready to take AI-led personalization in Retail to the next level?

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