How big data can help retailers optimize mobile

Mobile devices are working their way into every facet of our lives these days. For instance, according to Accenture Interactive, 72% 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.

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.

Cross-Channel Optimization Allows Retailers to Follow Transactions

Using cross-channel optimization, all this can take place across multiple devices, even if users never authenticate themselves. Cross-channel optimization identifies anonymous, individual user profiles and determines if customers are likely the same person using multiple devices without customer authentication. BloomReach Mobile then uses these insights to create dynamic categories based on the shopper's on-site web and mobile activity, in addition to presenting the most relevant products and search suggestions.

"With the cross-channel optimization, if we've identified the user, we can tell you what the mobile user bought when they went to the desktop," Kaufman says.

"Over 50% of people we identify go to the same category page in both places. And over 30% go to the exact same product," she adds. "Often they'll discover on the mobile phone and buy on the desktop or go into the store."

Avoid the Creepy Factor by Discerning Intent

Of course, the danger of such targeting is in making the customer feel spied upon. But Kaufman says retailers can avoid the "creepy factor."

"When you give somebody what they want, they appreciate it," Kaufman says. "When you try to infer what they want based on who they are, they're offended. Who says I'm shopping for me? Intent is everything. And the problem is that it's hard to discern. You have to understand language and behavior. We've built a system that's able to hone in on the scent of intent. The scent of intent is a really good thing to be able to inform what content you show me. Then give me the ability to naturally refine it."

To hone in on that "scent of intent," BloomReach collects data from 150 million web pages and more than a billion consumer interactions. When a client signs on, BloomReach deploys a pixel on the customer's site and then simply listens for a period of 14 to 30 days while performing a deep crawl of the site.

Once that period is done, BloomReach mobile is capable of automatically creating personalized dynamic categories that group products relevant to a shopper based on shared attributes like brand, color, style, category, or even a shopper's preference for on-sale items, pins and likes.

As Kaufman likes to say, Crayola has a color library of 164 colors. BloomReach has a color library of 1,100.

Thor Olavsrud covers IT Security, Open Source, Microsoft Tools and Servers for CIO.com Follow Thor on Twitter @ThorOlavsrud.

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This story, "How big data can help retailers optimize mobile" was originally published by CIO.

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