E-commerce Customer Engagement
Scaling eCommerce customer engagement services is complex and resource-intensive, often forcing a tradeoff between accuracy and performance and limiting revenue potential. Generative AI revolutionizes this landscape by enhancing search, recommendation, and personalization with advanced technologies like natural language processing (NLP), visual search, and hyper-personalized recommendations. With predictive personalization anticipating customer needs in real-time and seamless omnichannel integration across all platforms, businesses can deliver a more engaging, responsive, and revenue-boosting customer experience.
Vespa.ai is a powerful platform for developing and running real-time AI-driven search, recommendation, and personalization applications. Engage your customers with precise offers. Drive deeper engagement to increase revenue. Differentiate your brand in a crowded market.
Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa enables fast and efficient search capabilities.
Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.
Enhance the shopping experience by allowing customers to search using images instead of text, resulting in greater convenience and deeper engagement.
Seamlessly handle increased demand with Vespa’s horizontal and vertical scaling capabilities, adding capacity on-demand to maintain peak performance during high-traffic periods.
Customize your solution and integrate Vespa with your core applications and data sources to deliver services tailored to your business needs.
Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.
Enhance search accuracy, provide more natural, conversational search experiences, and generate relevant, customer-centric content that reflects user needs.
Vespa brings computation to data distributed across many nodes. This not only reduces network bandwidth costs and latency from moving data around, but ensures your AI applications operate within your existing data governance and security policies.
Generative AI has the potential to significantly level up customer experiences, specifically for search, recommendations, and personalizing user experiences, but the market is very much in the early exploratory stage. How can generative AI transform search, recommendations, and personalization? There are roadblocks, but with the right partner, e-commerce retailers can transform their customer engagement.
This research note explores the emergence of versatile AI databases that support multi-model applications. Practitioners, data/AI leaders, and business leaders should read this report to understand this new platform option for supporting modern AI/ML initiatives.
At Farfetch, Vespa is used to power the recommendation system for delivering personalized product suggestions to millions of users in real-time. The platform enables low-latency, high-throughput recommendations by efficiently handling large-scale data and integrating machine learning models directly into its serving layer.
Vinted transitioned to Vespa to enhance the performance and scalability of their search and recommendation systems. Vespa’s ability to handle complex, large-scale data and support machine learning models natively allowed Vinted to provide faster, more personalized search results.
Otto.de enhanced autosuggestions with Vespa, significantly improving relevance, performance, and scalability. With Vespa’s machine learning capabilities and flexible deployment, Otto.de personalized suggestions, reduced latency, and deliver a superior user experience.
