Machine Learning Provides 360-Degree View Of The Customer
MapR introduces real-time customer analysis aids in improving customer experience, targeting, and new revenue programs.
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MapR Technologies, Inc. announced the availability of the MapR Customer 360 Quick Start Solution powered by the MapR Converged Data Platform. Using advanced, real-time analytics to deliver hyper-relevance at the point of sale, social interaction or ecommerce site, marketing organizations can engage with MapR data scientists to deploy the MapR Converged Data Platform on their road to providing more relevant, real-time customer experience (CX).
“In the C-Suite, CMOs may have the most to gain and the most to offer by adopting real-time Customer 360 use cases,” said Dave Jespersen, senior vice president, worldwide services, MapR Technologies. “Digital marketers now have enormously powerful tools with which to understand and adjust to how customer and markets evolve and this solution puts them on the fast track for realizing the benefits.”
Traditional CRM powered Customer 360 solutions break down when data volumes and variety are integrated en masse. Few companies are able to leverage customer data to provide relevant, real-time CX. Predictive and accurate customer analytics stem from richer data sets (e.g. social, credit, behavioral) and machine learning models. Marketers are getting a clearer vision of what a data-driven marketing organization must support such as sales and marketing operations, content and ad targeting, and voice-of-the-customer.
Details of MapR Customer 360 Quick Start Solution (QSS)
The core of the Customer 360 QSS is to plan and execute machine learning models to address key digital marketing imperatives for the CMO.
Use cases include customer upsell and cross sell, micro segmentation, call center analytics, and content targeting recommendation engine. As part of the QSS, MapR Professional Services provide discovery and planning (1 Week), and development and project execution (5 Weeks). The execution phase includes curation of data sources, machine-learning iterations (features, modeling, metrics), knowledge transfer to customer to maintain / implement solution, and continuous training and consultation about tools and use case roadmap.
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