Product discovery challenges in e-commerce
Product discovery is at the heart of e-commerce success. A potential customer lands on a site with an intent—whether they know exactly what they’re looking for or are just browsing. The challenge? Bringing the right product to the right person at the right time.
E-commerce Search UX benchmark comprising 5,000+ performance ratings reveals that 41% of sites fail to fully support 8 key search query types performed by users.
While still widely used, traditional search and filtering mechanisms are increasingly inadequate for modern e-commerce. Customers expect seamless, intuitive, personalized shopping experiences, yet outdated keyword-based searches often fail to understand their true intent. This disconnect between customer needs and the tools provided to meet them leads to negative business outcomes.
A survey by Constructor found that 44% of shoppers spend at least three minutes searching for a desired product, with over 21% spending more than eight minutes. High bounce rates become a persistent problem as potential customers quickly abandon sites that fail to deliver relevant results. Sales are lost as shoppers cannot find what they want and take their business elsewhere. And perhaps most importantly, customer satisfaction suffers, leading to negative brand perception and decreased loyalty. The limitations of traditional search and filtering methods create a frustrating and inefficient shopping experience, ultimately hindering businesses’ ability to succeed in the competitive e-commerce landscape.
Enhancing user experience: The key to better product discovery
Imagine entering a physical store where an intelligent assistant instantly understands your style, needs, and preferences. It guides you to the perfect product, making the shopping experience seamless and enjoyable. That’s precisely what GenAI can do for digital commerce.
GenAI in eCommerce product discovery enhances user experience in two significant ways:
- Conversational AI assistants like AI-powered chatbots can significantly enhance product discovery. These intelligent assistants interact with users in a natural, conversational manner, guiding them toward products that align with their needs and preferences. These chatbots can offer tailored product recommendations by analyzing a user’s browsing history, past purchases, and real-time intent, making the discovery process more efficient and personalized. This conversational approach eliminates the need for users to manually type out search queries, creating a more intuitive and engaging shopping experience.
- Intelligent and Nuanced Search Capabilities: Generative AI has the potential to transform search functionality in product discovery. Unlike traditional search engines that rely heavily on keyword matching, GenAI can interpret the context and intent behind a user’s search query. This nuanced understanding allows for more precise and relevant search results. For instance, a user searching for “casual shoes for summer” would be presented with different results compared to someone searching for “formal shoes for office,” even though both queries contain the keyword “shoes.” GenAI’s ability to discern subtle differences in search intent ensures that users are presented with products that genuinely match
AI-Powered Personalization: Transforming Product Discovery
A personalized shopping experience is what takes product discovery from good to exceptional. By leveraging user data—such as past purchases, browsing behavior, and even time of day—AI can refine search results dynamically.
Consider a shopper who frequently buys athletic wear. The next time they search for “jackets,” the AI can prioritize sportswear over formal blazers, aligning results with personal preferences. Personalization is a key element that elevates product discovery from being merely good to truly exceptional. AI algorithms can dynamically fine-tune search results by utilizing a wealth of user data, including past purchases, browsing behavior, and even the time of day a user searches. This level of personalization ensures that the products presented to the user are highly relevant and aligned with their individual needs and preferences.
Beyond search, AI-powered personalization extends to product recommendations where intelligent algorithms curate relevant product suggestions based on browsing behavior, purchase history, and real-time intent. AI can also curate personalized product recommendations, highlight items likely to interest the individual user, and even provide tailored suggestions for complementary products.
By leveraging the power of artificial intelligence (AI) and user data, retailers can create product discovery experiences that are both highly relevant and engaging, leading to increased customer satisfaction and, ultimately, driving sales. Several industry examples illustrate this transformative impact:
- Etsy and eBay: These online marketplaces have adopted algorithm-based personalization techniques similar to social media platforms. By utilizing AI and extensive data collection, they offer users personalized shopping experiences, making it easier for customers to discover products that align with their preferences. This approach addresses challenges such as navigating vast inventories and enhances customer engagement.
- Instacart: The grocery delivery service introduced an AI-powered “Smart Shop” feature designed to simplify grocery shopping for users with dietary restrictions. By incorporating enhanced search and recommendation functions tailored to 14 nutritional preferences (e.g., gluten-free, low-carb, vegan), Instacart provides a customized shopping experience that aligns with individual health needs, thereby increasing customer satisfaction.
- Shopify: By acquiring Vantage Discovery, an AI search company, Shopify aims to enhance retailers’ search functionalities using generative AI and large language models. This integration is expected to provide personalized and relevant search results, thereby improving the product discovery process for consumers and supporting merchants in delivering more engaging shopping experiences.
- Ulta Beauty: The beauty retailer has been leveraging AI to enhance data and marketing strategies since 2018. By integrating technology into their operations, Ulta Beauty aims to maintain the relevance of physical stores and provide personalized customer experiences, thereby increasing engagement and satisfaction.
These examples demonstrate how AI and user data can be harnessed to create personalized, efficient, and engaging product discovery experiences. By understanding individual preferences and behaviors, retailers can tailor their offerings to meet specific customer needs, leading to higher satisfaction and increased sales.
Bridging the GenAI Stack to E-Commerce Platforms
E-commerce businesses operate on platforms like Magento, Shopify, and WooCommerce. They need to integrate Gen AI in E-Commerce Product Discovery without disrupting their existing infrastructure. Cloud providers like AWS and Azure offer e-commerce AI solutions, enabling businesses to adopt AI-powered personalization while maintaining seamless operations. They provide the building blocks—AI models, vector databases, and computing power—to build custom GenAI-powered discovery solutions.
Moving beyond string matching with AI-powered search
Traditional e-commerce search engines rely on keyword-based string matching. This approach struggles with understanding user intent, synonyms, context, and personalization. AI-powered search, on the other hand, can transform product discovery by leveraging machine learning and natural language processing.
All major hyperscalers—AWS, Azure, and Google Cloud—offer LLMs (Large Language Models) and frameworks for customization. These models can be fine-tuned on e-commerce-specific data to improve search relevance and personalization.
Vectorization of product and customer data
For an AI-powered search to be effective, the LLM needs to be trained on product catalogs and customer interaction data. This involves:
- Vectorizing data: Converting product descriptions, user queries, and historical interactions into numerical vector representations.
- Using vector databases: Storing and retrieving vectorized data using specialized databases like Pinecone, Weaviate, FAISS, or AWS OpenSearch with k-NN for fast similarity searches.
- Fine-tuning with customer behavior: Adapting AI models to reflect real-world purchasing trends, seasonal variations, and individual shopping habits.
There are two primary ways to customize AI models for e-commerce product discovery:
- Prompt Engineering: Fine-tuning prompts to guide LLMs in generating relevant search results or recommendations. This is a lightweight approach that doesn’t require extensive model retraining.
- Retrieval-Augmented Generation (RAG) pipelines: Combining pre-trained LLMs with real-time retrieval from product databases. This ensures up-to-date, contextually accurate search results by fetching the latest product information before generating responses.
The UX question: Chatbots vs. Search bars
The final step in optimizing Gen AI in E-Commerce Product Discovery is the user interface. By integrating artificial intelligence (AI) and user data, retailers can significantly enhance product discovery, creating experiences that are both highly relevant and engaging. This approach leads to increased customer satisfaction and drives sales. Two key implementations of AI in this context are:
1. AI Chatbots: Enhancing Customer Interaction
AI-powered chatbots engage customers in natural language conversations, guiding them through product discovery and dynamically answering their questions. These virtual assistants provide personalized recommendations and support, enriching the shopping experience.
The beauty retailer Sephora’s chatbot on Facebook Messenger offers personalized product recommendations and makeup tutorials, helping customers find products that suit their preferences.
2. Intelligent Search Bars: Delivering Context-Aware Results
AI-enhanced search inputs interpret user queries beyond simple text matching, providing context-aware, hyper-personalized results. By understanding the intent behind a search, these intelligent search bars deliver more accurate and relevant product suggestions.
Marks & Spencer (M&S) launched an AI-powered “Wine Finder” tool to assist shoppers in selecting the perfect wine based on their flavor preferences. Customers can access the tool on the M&S website or app, answer a few questions about their wine tastes, and receive tailored wine recommendations, enhancing the product discovery process.
By adopting these AI-driven tools, retailers can create more personalized and engaging shopping experiences, increasing customer satisfaction and sales. A non-SaaS custom-built solution gives e-commerce vendors complete control, granting them unparalleled control over their data and AI capabilities. This approach facilitates customization and security that is often unattainable with standard SaaS offerings.
Data Privacy and Security
Maintaining robust data privacy and security is paramount in the realm of e-commerce, where sensitive customer data and proprietary business information are constantly in play. With a custom-built solution, businesses retain full control over their data, ensuring compliance with industry regulations and safeguarding against potential breaches. This level of control is particularly crucial for businesses operating in sectors with stringent data privacy requirements.
Tailored AI Models
Generic AI models may not always align perfectly with the unique nuances of a specific business. On the other hand, custom-built solutions allow for the development of AI models that are trained on the business’s own data, resulting in algorithms that are finely tuned to the specific needs and characteristics of the company. This tailored approach can lead to significant improvements in the accuracy and effectiveness of AI-driven applications, such as product recommendations, search results, and customer service interactions.
Seamless Integration
A significant advantage of custom-built solutions is their ability to integrate with a business’s existing infrastructure seamlessly. This includes product catalogs, search engines, customer relationship management (CRM) systems, and other critical components of the e-commerce ecosystem. By integrating AI directly into these systems, businesses can create a unified and cohesive customer experience, where AI-driven insights and recommendations are seamlessly woven into the fabric of the online shopping journey.
Leveraging Cloud-Based AI Services
The emergence of cloud-based AI services, such as AWS Bedrock and Azure OpenAI, has significantly lowered the barrier to entry for businesses seeking to deploy custom AI solutions. These services provide access to powerful AI models and tools that can be integrated directly with a business’s existing systems, eliminating the need for extensive in-house AI expertise. This democratization of AI technology empowers businesses of all sizes to harness the potential of artificial intelligence and machine learning, driving innovation and creating a competitive advantage in the dynamic e-commerce landscape.
The Future: AI-Driven, Hyper-Personalized Shopping
The future of e-commerce belongs to brands that offer intuitive, AI-driven shopping experiences. GenAI-powered product discovery ensures customers find what they need faster, making online shopping as natural and engaging as a conversation.
As AI technology evolves, its capabilities in ecommerce will only grow more profound. Future advancements are expected to introduce even more nuanced personalization, possibly integrating augmented reality (AR) to provide shoppers with a try-before-you-buy experience online.
Next-Level Ecommerce: AI’s Secret Weapon For Personalized Experiences, Forbes
Businesses can transform product discovery from a frustrating process into a competitive advantage by harnessing AI for personalization, bridging hyperscaler capabilities with commerce platforms, and delivering seamless user experiences.
Is your e-commerce platform ready to embrace AI-driven product discovery? The future is now—let’s innovate!
MARCH 27th, 2025
12:00PM EST
LIVE WEBINAR