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Luminar to Help Companies to Effectively Reach and Retain Latino Consumers

By Nathesh
TMCnet Contributor

The newly launched Luminar is slated to help clients enhance their ability to identify predictive models of consumers’ behavior, allowing them to reach and retain U.S. Latino consumers.

Luminar is reportedly the first ever big data analytics and modeling provider, with a specific focus on connecting marketers with U.S. Latino consumers. It will do so by making use of cutting-edge technology and delivering deeper actionable insights.

“Many marketers looking to connect with the Latino consumer have relied on demographic information and broad cultural themes when developing their marketing strategies, leading to generalized campaigns,” said Franklin Rios, president of Luminar. “The analysis of ‘big data,’ however, offers a much deeper understanding of consumer behaviors, giving marketers the power to truly connect with U.S. Latinos in a relevant way.”

From Luminar Insight APP to Data Hygiene & Integration, Luminar’s solutions allow clients to fine-tune their targeting and messaging strategies for the Hispanic market. Its three areas of approach consist of data sources, data management and staging, and insights.

The organization continues to collect data and develop unique data models based on its proprietary hypothesis and approach, and deep understanding of the Hispanic culture. The obtained data is analyzed to present specific Hispanic nuances and trigger points, and provides insight into the ‘who, what, where and how’ of the Hispanic consumer.

The Luminar Insights app is a Web-based application that reportedly gives clients access to Luminar’s proprietary Latino persona models and analytics while Luminar employs data hygiene and ETL (extract, transform, load) technology before exploratory data analysis in order to provide a singled trusted view of the master data.

Clients can also utilize the Real-Time Cloud Insights from Luminar that takes a client’s data and integrates near real-time insights to build predictable, relevant models and improve business efficiencies in real time.

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Edited by Braden Becker

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