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KXEN Announces Support for Geo-Location in Predictive Analytics - Predictive analytics solution now leverages geo-localized data to boost efforts in location-based marketing, fraud detection, security and operations - KXEN.com
KXEN Announces Support for Geo-Location in Predictive Analytics

 

NewswireToday - /newswire/ - San Francisco, CA, United States, 2013/06/20 - Predictive analytics solution now leverages geo-localized data to boost efforts in location-based marketing, fraud detection, security and operations - KXEN.com. NYSE: SAP

   
 
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KXEN, the leading provider of predictive analytics for business users, today announced support for geo-location in its flagship predictive analytics solution, InfiniteInsight®. This new capability natively supports geo-localized data in predictive models, enabling new insight to be derived from data sources such as phone calls, GPS, machine sensors, geo-referenced signals, and social media (check-ins, tweets, likes, clicks, etc.) This unprecedented predictive insight can be used in a number of ways including location-based marketing, fraud detection and security, such as counter-terrorism and border inspection.

“The world has never been so dependent on being connected. And as more people and more devices ‘plug in’ to the Internet, we’re seeing that valuable location-based data can be applied to many predictive modeling scenarios,” said Erik Marcade, CTO and Co-Founder of KXEN. “Geo-location provides a new level of insight about individual or machine behavior, making it increasingly important in today’s hyper-connected, hyper-mobile world.”

KXEN’s support for geo-location capabilities includes:

Location-Aware: Natively identifies the location of a person or transaction using latitude and longitude and translates that data into a new variable type, called geographic “tiles” or regions, which are analyzed with KXEN’s InfiniteInsight®. This new information can be used to enhance the accuracy of a predictive model, by providing a highly-personalized recommendation for a nearby store or restaurant, for example.
Co-Location: Detects transactions or events occurring in the same location during the same period of time. An example of this capability is identifying terrorist cells using mobile network data.
Path Identification: Creates a sequence of geo-localized positions from time-based transactions to extract patterns of events. For example, understanding urban driving patterns for traffic and toll planning.

Geo-localized data is yet another example of how new data sources are adding to the power but also complexity of Big Data. Geo-location often brings thousands of new dimensions to the underlying analytical data set, requiring the ability to build robust predictive models regardless of how many input variables exist a core strength of KXEN’s InfiniteInsight®.

Register for our Live Webinar: KXEN will be hosting two live regional webinars to discuss KXEN’s new geo-location capabilities and conduct a live demonstration:

• Americas: June 26, 2013 11 am. PST / 2 pm. EST
• Europe: June 27, 2013 2 pm. GMT / 3 pm. CET

About KXEN

KXEN (kxen.com) is revolutionizing the way companies use predictive analytics to make better decisions on petabytes of big data. Based on patented innovations, the company's flagship product, InfiniteInsight® and its pure cloud-based platform, Cloud Prediction™, delivers orders of magnitude improvements in speed and agility to optimize every step in the customer lifecycle including acquisition, cross-sell, up-sell, retention and next best activity. Proven with over 500 deployments at companies such as AAA, Allegro, Bank of America, Barclays, Belgacom, CBS Interactive, ING Direct, Lowe’s, Meredith Corporation, Mobilink, Overstock.com, PT XL Axiata, RealNetworks, Rhapsody, Rockwell, Rogers, Sears, Shutterfly, Stage Stores, U.S. Cellular and Vodafone, KXEN solutions deliver predictive power and infinite insight. KXEN is headquartered in San Francisco, California with field offices in the U.S., Paris and London.

 
 
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KXEN Announces Support for Geo-Location in Predictive Analytics

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