How GenAI is Reinventing the Retail Customer Journey
Retail has long been a major beneficiary of AI innovation, in various forms such as predictive AI via machine learning. It stands to reason that, with the emergence of GenAI, the retail industry would be innovating once again. Traditional AI excels at analysing patterns and making predictions based on existing data. In parallel, GenAI is pulling together vast quantities of data, providing real-time insights, curating tailored recommendations, and creating entirely new content. It then interacts through natural, context-aware conversations with customers throughout the customer journey.
We already know GenAI is being actively adopted by retailers across the industry. We also know that today’s consumers expect more than just products—they demand experiences that are personalised, seamless, and efficient. So how is GenAI supporting the drive to meet these growing demands? Below are just a few examples:
Even More Personalised
Today’s retail success increasingly depends on delivering highly personalised experiences to individual shoppers. Predictive AI has long enabled personalisation, but GenAI is taking this to new levels. For instance, Amazon’s recommendation engine employs machine learning algorithms to suggest products based on browsing and purchasing patterns.
However, GenAI expands upon this approach. Stitch Fix, for example, uses GenAI to create unique outfit combinations and detailed style recommendations, considering not just past purchases but also current trends, social media sentiment, and personal style preferences.
Smarter Search and Product Discovery
GenAI transforms this crucial aspect of the customer journey by understanding context and intent, moving well beyond reliance on keyword-based queries and predefined attributes, which are a constant source of frustration for customers. For example, Shopify’s Magic Search leverages GenAI to understand complex, conversational queries, generating relevant results even when customers are unsure of exact product names or terminology.
Zalando’s Virtual Stylist takes this a step further, generating complete outfit recommendations from natural language descriptions. This approach enhances the customer experience and significantly increases the likelihood of conversion.
Dynamic Merchandising
Effective merchandising significantly influences customer purchasing decisions. GenAI is augmenting the skills and processes behind in-store merchandising teams in department stores, supporting promotional activities, product placement, and merchandising strategies. Macy’s uses GenAI to analyse sales trends, customer purchase patterns, interactions, and media feeds to generate actionable insights and recommendations on display contents, positioning, and prominence. Pulling disparate data and insights together and using them to create recommendations is a key strength of GenAI. Additionally, it curates cross-merchandising opportunities by understanding which products are browsed, seen, and talked about together.
Service Beyond Basic Chatbots
The distinction between traditional AI and GenAI is most apparent in customer service. Traditional chatbots follow scripted responses, while GenAI-powered solutions engage in natural, context-aware conversations.
For example, H&M’s basic chatbot uses predefined rules to help customers navigate products but lacks the depth of interaction that GenAI can provide. On the other hand, Sephora’s Beauty GPS uses GenAI to offer personalised skincare and makeup advice, generating custom product combinations based on detailed skin analysis and customer preferences.
Supply Chain Transparency
Consumers demand transparency and sustainability and GenAI is playing a crucial role in enhancing visibility throughout the supply chain. By integrating data from various sources, including suppliers, logistics, and market trends, retailers are able to build a comprehensive view of their end-to-end supply chain operations.
Unilever monitors and analyses supply chain data in real time using GenAI, enabling them to track product origins, assess environmental impacts, and ensure compliance with sustainability standards. By providing consumers with detailed information about the sourcing and journey of their products, Unilever not only builds trust but also aligns with the growing consumer preference for ethical and sustainable practices.
Looking Ahead
GenAI isn’t just another technology tool adding complexity to an already crowded value chain. It represents a fundamental shift in how retailers understand, serve, and delight their customers. While traditional AI will continue to play a crucial role in optimising many aspects of retail operations, GenAI’s ability to imagine, create, and curate is generating many new possibilities across the length of the customer journey.
So where next?
To quote Bernard Marr:
“2025’s AI systems are creating shopping experiences as unique as fingerprints. We’re talking about dynamic pricing that adapts to individual budgets, loyalty programs that actually understand what you value, and product recommendations that feel like they’re coming from a friend who really gets you”.
Next week, the next instalment will explore the enablers: the critical areas to focus on when building out your roadmap to leverage technologies like these to transform your own customer journey.