A lire sur: http://www.cio.com/article/736361/How_Big_Data_Can_Help_Retailers_Optimize_Mobile
Delivering a dynamic and engaging mobile experience is becoming essential to retailers, and big data marketing applications company BloomReach believes that its new big data application is the key.
CIO
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Mobile devices are working their way into every facet of our lives these days. For instance, according to Accenture Interactive, 72 percent of consumers ages 20 to 40 now use mobile devices to comparison shop while in retail stores.
The problem for retailers? The majority of them leave without making a purchase with their smartphone or tablet; they purchase online—often using a different device, such as a desktop PC.
How do you track the success of your marketing under these circumstances and ensure that you are delivering your customers the best possible experience? BloomReach, which specializes in big data marketing applications, believes big data provides the answer.
BloomReach today took the wraps off BloomReach Mobile, a cross-channel-optimized mobile search and discovery solution built on the company's signature Web Relevance Engine technology.
"Mobile as a channel is almost insignificant from a commerce perspective," she says. "But it's not a direct channel. If you deliver a poor experience on the mobile phone, many customers who have that experience not only will leave your mobile website, they will not use your normal website and they won't go to your store."
BloomReach's answer is a cloud-based big data application that continuously optimizes content mapped to the unique characteristics of a particular mobile visitor and the device that visitor is using. The application is designed to use a combination of web-wide and social data, natural-language processing and machine learning to provide dynamic categories and results that are unique and individual to the user.
For instance, you might search for "green floral dresses" and dynamically receive a set of results of green dresses with floral prints, even though the retailer's website was not set up with those categories. In addition, past transaction results might indicate that you have a preference for sleeveless dresses, allowing the system to present sleeveless green dresses with floral prints as the top results.
The problem for retailers? The majority of them leave without making a purchase with their smartphone or tablet; they purchase online—often using a different device, such as a desktop PC.
How do you track the success of your marketing under these circumstances and ensure that you are delivering your customers the best possible experience? BloomReach, which specializes in big data marketing applications, believes big data provides the answer.
BloomReach today took the wraps off BloomReach Mobile, a cross-channel-optimized mobile search and discovery solution built on the company's signature Web Relevance Engine technology.
Follow Transactions From Mobile to the Ultimate Purchase Channel
Joelle Kaufman, BloomReach's head of Marketing, says that creating an excellent mobile experience and then being able to follow a transaction from mobile to the desktop, for instance, is essential, because consumers make extensive use of mobile while shopping. However, they don'toften don't use mobile devices for the ultimate transaction. Instead, she says, users frequently shop with mobile devices and make the final transaction online."Mobile as a channel is almost insignificant from a commerce perspective," she says. "But it's not a direct channel. If you deliver a poor experience on the mobile phone, many customers who have that experience not only will leave your mobile website, they will not use your normal website and they won't go to your store."
BloomReach's answer is a cloud-based big data application that continuously optimizes content mapped to the unique characteristics of a particular mobile visitor and the device that visitor is using. The application is designed to use a combination of web-wide and social data, natural-language processing and machine learning to provide dynamic categories and results that are unique and individual to the user.
For instance, you might search for "green floral dresses" and dynamically receive a set of results of green dresses with floral prints, even though the retailer's website was not set up with those categories. In addition, past transaction results might indicate that you have a preference for sleeveless dresses, allowing the system to present sleeveless green dresses with floral prints as the top results.
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