Content Distribution Gets Machine Learning and Big Data Facelift

Most marketers have never heard of the startup, Keywee. However, after closing a $9.1 million Series A round of funding last month, they’re about to. Financing was led by Eric Schmidt’s Innovation Endeavors and Marker LLC, with participation from The New York Times Company and UpWest Labs. The platform offers marketers a new approach to data-driven content marketing by optimizing the return on content initiatives. Its content marketing and content distribution platform uses advanced text-mining technology and a data layer to help both publishers and brands find the audiences most likely to drive real business results.

(Disclosure: I have no financial relationship with Keywee. I’ve only seen a demo of their solution one time in December. As the VP of Audience for Relevance it’s my job to stay abreast of and report on new content promotion news, trends, insights and technology.)

Keywee is one of several startups vying for prominence in the new and emerging ecosystem of content promotion. Many tech entrepreneurs are building solutions for content marketers who struggle to get their content seen by the audiences that matter the most to their brands. The promise of search and social organically driving profitable content consumption has not held true for all brands. Organic social reach is declining and there are only 10 organic positions in most search engine results pages (SERPs).

This is problematic because according to Mark Schaefer’s new book, “The Content Code,” the amount of information created on the Internet will grow by 500 percent by 2020. That’s a whole lot of content vying to be seen, consumed, and engaged with in the SERPs and the smothered organic newsfeeds of social.

Most marketers can no longer publish massive amounts of content, blindly distribute it across audiences and platforms and expect to achieve profitable business results. Keywee’s solution is to appropriately connect prudent audiences with relevant content using machine learning, natural language (long-tail) and the social graph via paid targeting on a variety of channels.

Dror Berman of Innovation Endeavors says, “Like many of our portfolio companies, Keywee is primed to be the next market disruptor. Its impressive results with brands and publishers in the last year prove its potential impact, and we expect the company to quickly drive a dramatic shift in the content marketing industry this year.”

The tool uses complex text mining capabilities to analyze text in published content. It then seeks to understand the native targeting taxonomies for different channels using robust layers of data. This finds the best paid audiences for that marketing asset. An audience’s likelihood to engage with content and its propensity to meet specific business goals are identified across multiple platforms and distribution channels.

“To truly engage consumers and meet business goals, marketers need to better understand what content will resonate with various, and often niche, audiences across different platforms like Facebook, Google, Reddit, Twitter, Yahoo, etc. They need to think of content through the lens of the distribution channels and their specific targeting parameters,” said Yaniv Makover, founder and CEO of Keywee.

What makes Keywee different from other native paid channels like Taboola, Adblade, Outbrain, Nativo and others?  It’s the only native distribution tool with a layer of robust data, captured through machine learning of natural language and the social graph, that powers its targeting across many different channels – in other words, automated research, optimization and targeting.

The end of the first quarter of 2015 is upon us and we already have the next content marketing disruptor shaking things up a bit. While Keywee was the first of the year, they certainly won’t be the last. Rumors are flying furiously about Google’s formal launch of its native advertising program. As content saturation continues to increase expect many more marketers to be challenged with online visibility problems. This will likely drive the next wave of marketing technology entrepreneurs in the content promotion ecosystem.

This article originally appeared on Social Media Today.

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