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Monday, January 13, 2014
Intel Touts New Retail Technologies


Intel showcases new intelligent retail concepts including a full-length digital "mirror," gesture-based product customization and a solution to eliminate long lines in stores.

Intel also says its technology enables consumers to shop the looks from favorite television programs from their couch using a tablet or smartphone and play games to earn digital currency.

The announcements were made by the company today athe National Retail Federation (NRF) Annual Convention & Expo from Jan. 13-14 in New York.

With Intel-based Shopping Anywhere, Intal says consumers can shop the looks from their favorite television programs right from their couch. By using the Intel Retail Client Manager, along with technology from NCR and ACTV8.ME loaded on a tablet or smartphone, viewers can identify the clothing an actor or actress is wearing in an episode and receive special offers, play games to earn digital currency, or purchase the outfit instantly and have it delivered directly to their door.

If a consumer prefers to bring the ease of online shopping with them in-store, the Intel Core? i7-based MemoryMirror full-length, digital "mirror," allows store shoppers to virtually try on multiple outfits, and view and compare previous looks on the mirror or via smartphone or tablet. The MemoryMirror uses Intel integrated graphics technology to create avatars of the shopper wearing various clothing that can be shared with friends to solicit feedback or viewed instantly to make an immediate in-store purchase.

Technology can also deliver a more interactive experience in larger retail settings, such as the car dealership, by offering the benefits and ease of online exploring. The Intel-based Intuitive Product Customization uses the power of gesture recognition to blend the online and in-dealership experience. With Intuitive Product Customization, consumers use hand gestures to browse and interact with digital product information, including various paint colors, interior options and add-on features to customize their car and visualize with their desired options before ordering their car.

Intel is also showing at the show a range of tablets for in-store retail settings that enable a more proficient and knowledgeable sales force, as well as a more efficient and readily available checkout process.

Intel is also working with retailers to help make better use of big data to deliver a more personalized experience across all channels of interaction with customers, while optimizing inventory management.

For example, to avoid shoppers arriving at a store to purchase a particular product only to find it's sold out, retailers can better anticipate the right product mix for each store, determine optimal pricing and gain insight into the real-time status of inventory with Intel big data solutions. With the Intel Distribution of Apache Hadoop software and analytics toolkit, retailers can mine patterns from data, build an emotional and behavioral 360-degree understanding of the customer, and reduce instances of lost opportunities.

Another way big data can help retailers deliver a more personalized shopping experience is through context-aware marketing. Intel Context Aware Marketing makes digital signage more effective by dynamically changing advertisements displayed on a sign based on the age and gender of the person viewing the sign. When a consumer walks by an Intel Core-based digital sign, the Intel Advertising Framework technology can analyze information including weather trends, social media, and the shopper's phone data to change the content and user interface to make it more relevant and personalized to the viewer. Intel Context Aware Marketing can also be tied to store inventory systems to display only advertisements for products currently available in-store.

Waiting in long, frustrating checkout lines could even become a thing of the past by effectively making use of big data. Intel-based Dynamic Staffing Optimization (DSO) can measure and analyze data including real-time traffic in and out of the store, queue length, the number of active and open registers, historical transaction data, and labor scheduling information to provide predictive recommendations to open or close registers based on expected customer traffic.




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