09 February 2022
less than a minute read
Hybrid shopping and other enhanced customer experiences are big trends in retail. Customers want a unified, personalized experience across retail channels, but this is easier said than done. Achieving this vision means interconnecting everything: from your distribution centers, to your websites, to your brick and mortar stores.
A Fortune 50 retailer drove $1 billion in incremental revenues over the last three years after deploying AI models developed by RelationalAl.
These AI-enhanced customer experience initiatives covered three areas:
A Relational Knowledge Graph serves as a comprehensive foundation for AI, optimizing personalization to drive incremental revenues.
True Omnichannel Customer Experiences
RelationalAI connects disparate data sources to bridge the gap between in-store and online experiences.
New Revenue Streams
A Relational Knowledge Graph enables next-generation features and functionality such as virtual visual experiences.
Unifying your customers' experience with interconnected retail knowledge delivers results by increasing sales in existing channels and enabling new ones.
By combining industry knowledge, public and internal data, and cutting-edge machine learning techniques, this retailer saw dramatic improvements:
By leveraging our graph-driven AI as a reusable data fabric for applications that serve in-store and online shoppers, customers get a world-class experience finding products in a physical store as quickly as they can online, with precise location information surfaced in search results while they are in-store.
An all-new Virtual Visual Experience powered by RelationalAI lets customers quickly find and replace products in their home using their mobile phone camera, matching specifications against available inventory for quick purchase.
If the scale and complexity of your customer and product data makes it seem impossible to make progress on major initiatives, get in touch to find out how we can help!
Demonstrating the details of weaving metadata into a knowledge graph to automate checking and maintenance of complex policy requirements.Read More
Showing how knowledge graphs support the construction of scalable models that mix discoverable with explicitly asserted metadata to afford reasoning and policy enforcement. In this first article, we show how explicitly asserted metadata in a knowledge graph enables automated reasoning.Read More
Business applications are traditionally complicated to understand and maintain because business logic tends to be scattered in separate tiers of growing code bases, while schema becomes rigid and brittle. The end result is that business logic dissolves in a soup of disparate application code.Read More