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 modeled for clustering, propensities, or look-alike patterns. Some 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 design of menu items, services, rooms, and offerings.
Natural language processing, advanced statistical predictive and optimization analytics, sentiment and behavioral modeling, other AI tools, high-scale database applications, data lakes, and data management platforms, social CRM
Use Case Summary
Provide data and analytic foundation for all omni-experience customer engagement use cases and customer-centric use cases under other strategic priorities.