Customer data is often siloed across departments, of poor quality (incomplete, old, held in inconsistent taxonomies, locked in unstructured formats, etc.), and not analyzed or modelled for clustering, propensities, or look-alike patterns. Some retailers use third-party and consortium data, but fewer use social graph, geospatial, and other new data types and sources.
Goals and Objectives
Maintain comprehensive, current, and consistent customer data assets harvested from enterprise, partner, and social media sources and made available to CRM, marketing, and demand management applications. Consume and mix traditional, transactional, structured web interactions and unstructured text and voice interaction data. Publish data assets to descriptive, predictive, and prescriptive analytical applications in customer care, hyper-personalized marketing, and curated merchandise design and assortment use cases.
Natural language processing, advanced statistical predictive and optimization analytics, sentiment and behavioral modeling, other AI tools, high-scale database applications, data lakes, data management platforms, serverless computing, 5G connectivity, and multicloud management
Use Case Summary
A data and analytic foundation is provided for all omni-experience customer engagement use cases and customer-centric use cases under other strategic priorities.