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Courtesy of Bloomreach


Online Personalization Has a New Name: Bloomreach.

E-commerce is just the beginning; the mid-size company’s new personalization engine uses real-time analytics to streamline any buying process across multiple devices.

You’ve probably never heard of BloomReach, a mid-market creator of big data applications for retailers. But if you’ve shopped online at Neiman Marcus, Kohl’s, or, you’ve experienced BloomReach’s tools in action. The company debuted a mobile solution aimed at turning those tiny screens into mini-powerhouses for search, discovery, and spending last year. In April they added SNAP, a service aimed at personalizing site content, regardless of the consumer’s device, through analytics that allow marketers and merchants to see how their customers search, browse, and buy.

BloomReach is building on that concept with the launch of its Personalized Discovery Platform. The company claims it is the first of its kind to draw in data from across the web to make the most relevant recommendations for shoppers. Think of it as a kind of Google search, except it’s happening when you’re shopping. Google’s algorithms are at work behind your search bar. When you start typing they’re busy scouring the trail left by your IP address, all your previous searches, and other web content you’ve consumed to bring up the stuff that you most likely want to see.

BloomReach’s Personalized Discovery Platform works in similar fashion. The company’s proprietary software uses machine-learning and natural language processing to understand content and algorithmically match it with shoppers’ searches –even on smartphones, fat fingers notwithstanding. BloomReach says this beats out other technologies that market to consumer personas and rely on users logging in to retrieve and analyze their demographic and historical data.

Penny Gillespie, research director at Gartner, says personalization has changed a lot over the last few years moving from the simple “other customers who purchased this also bought” to a sophisticated schema that draws from existing information, current location (thanks smartphones) and other behaviors. Gillespie says, “[Analytics] is moving from the concept of where the merchandiser actually sets the rules for how an application should work, [to the] concept of smart machines, where the technology actually takes on the role of learning and analyzing speech and behavior, and continually learning” to optimize the system for what a particular customer’s next purchase opportunity might be.

And consumers are eager for the technology to separate the wheat from the chaff when they hit up e-commerce sites. The more they keep seeing items that pique their interest, the more time they’ll spend browsing –even on their phone. BloomReach customer Tilly’s, a specialty retailer for surf and skate athletic wear, saw their conversion rate jump 11 percent on mobile after implementing SNAP. Independent of those results, a recent Monetate/Econsultancy survey on online personalization found that companies that customize their site experience report an average increase in sales of 19 percent. Those who fully invest in online personalization will outsell their competitors by more than 30 percent in the next four years according to Gartner.

Gillespie says customers want two things when they shop online. “One is they want to be recognized. They believe that they have a relationship with the seller. And because they have a relationship with the seller, the seller should know something about them and not waste their time by showing them content or giving them offers for things in which they’re not interested,” she says. They also want to be valued for their repeat visits, Gillespie observes, and that can translate into discounts or rewards. Above all, they don’t want their time wasted.

Even Zappos, virtual forest of thousands of shoe styles, recognized the need to help customers curate their experience beyond ticking boxes for color and size. For BloomReach founder Raj De Datta, “Personalized discovery is ultimately a data problem because a retailer with thousands of products attempting to reach hundreds of millions of consumers where every product can be described in hundreds of ways cannot process data at that scale.” According to De Datta, the solution has arrived.

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