BloomReach wants to save your site traffic with big data

Hot Mountain View, Calif., startup BloomReach emerged from stealth mode on Wednesday with a message about how its marketing-optimization engine will help ensure that companies get their web pages noticed above the noise online. Using a potent brew of big data techniques presented as a software-as-a-service application, BloomReach says it can significantly improve the amount of traffic on product web pages by making them more relevant to consumers.

Raj De Datta

The problem right now, BloomReach Co-Founder and CEO Raj De Datta told me, is that companies just cannot know how to best present their product catalogs or other content in a way that best aligns with what customers are looking for. In fact, he said, less than 25 percent of web pages see any traffic from natural search or paid search in any given month. Companies are missing out on large swaths of customers because they can’t display their content in a meaningful manner, and the problem only gets worse as content volumes grow.

“Understanding relevance of content to the way people express themselves turns out to be a difficult problem,” De Datta said.

In BloomReach’s view, this ultimately boils down to a data problem: if a company wants to display just 1,000 products across 100 pages, De Datta explained, there are 10-to-the-28th-power (10 octillion) possibilities for how to do that. When it comes time to describe those products, there are 10-to-the-30th-power (10 nonillion) possibilities. BloomReach will analyze all of them and figure out the optimal methods for organizing and describing products on web pages to ensure customers want to click.

According to De Datta, its early customers — which include household names such as Neiman Marcus, Crate & Barrel, Orbitz and Williams-Sonoma — see 75 percent of the pages getting search traffic within a month. He said BloomReach generates 80 percent average incremental traffic for users.

How does BloomReach work its magic? With lots of big data, of course:

De Datta said the company runs 1,000 Hadoop jobs a day that interpret 1 billion web pages and 1 billion consumer interactions.
It has developed all sorts of algorithms for tasks such as determining relevance and optimizing the connections between pieces of data.
It has a library consisting of more than 12 million two-word synonyms, which the company claims is 73 times more words than the Oxford English Dictionary contains.
BloomReach even runs regular Monte Carlo simulations, a complex but effective method for determining the possible outcome of events involving many loosely coupled variables.
Two-thirds of BloomReach’s engineering team has a Ph.D. in computer science.

BloomReach calls the entire package of big data techniques its Web Relevance Engine, and it now powers three distinct products. There’s BloomReach Search, the company’s flagship product that figures out and creates the most-relevant web pages based on what consumers are searching for. As of Wednesday, there’s also BloomReach Lift, which takes consumers clicking on search ads to the most-relevant pages rather than a static landing pages (which have 55 percent bounce rates) and also tells companies what search terms they should bid on, and BloomReach Social, which helps create pages consumers are more willing to share.

As an example of the latter, De Datta suggested that customers searching for “umbrella” might actually be concerned with planning a picnic, and an “experience” page dedicated to everything-you-need-for-a-picnic might make that customer more willing to share it socially.

BloomReach, it appears, has a solid grasp on the challenge of figuring out what consumers actually want, but there is no shortage of use cases for big data, nor of techniques for doing it. We’ll be discussing many of them over two days at our Structure: Data event next month in New York.

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