Scaling Content Curation
Scaling Content Curation with the B2B ContentEngine

A very interesting piece by  entitled, “This Curation Trend has One Big Problem: Scale“. Now, what does this have to do with B2B content marketing or curation for B2B marketing? That is a good question.

Algorithm Curation Fails to Scale

What really interested me in the article was the title and the problem with scaling curation. Of course, we often look to assist marketers and marketing agencies scale their curation marketing efforts — through thought leadership, social sharing, SEO content, driving traffic, etc. It turns out this particular blog post was more of an art-focused article, but it kept my attention nonetheless as curation was its theme. The focus is on algorithmic content curation and how this usually seems to fail.

I agree algorithmic curation is doomed to fail at anything other than the lowest common denominator. Even though software has AI back-end technology that analyzes content, we refrained from using this for finding similar content (it’s not  that hard) — and yes, many of our competitors do do this. You can certainly do this and there are some good similarity algorithms out there, but the real value of curation is in new but related content. That is what gives you value as a curator and why your followers, customers, and prospects will look towards your content over any algorithmic curated content. Granted, if your goal is to scrap every article about the latest pop sensation, automation should work just fine.

Scale Curation with Humans in the Loop

If you want to show leadership, provide value to leads/prospects, and drive quality focused web traffic, you’ll need both a curation and research vehicle.

Erin explained where algorithmic curated business models often fail and why there is a need for human in the loop curation professionals.

Curation continues to gain momentum in the tech world. I’ve seen startup after startup assess their algorithmic recommendation engines and conclude that a human touch just works better. While Amazon, with its robust recommendation engine, still peddles suggestions based on past purchases and searches, other smaller companies have found that it takes a human to know what other humans find interesting.

Take Songza: the company tried user-generated playlists and Pandora-style algorithm-built playlists. But its service didn’t take off until it employed a team of experts to curate its mega-popular activity-specific playlists. Sulia surfaces its news from experts — it picks those experts based not on followers, or engagement data, or whatever black box Klout uses to measure influence, but from the one human-built indication of expertise on Twitter: Twitter lists. Apparel trade show Capsule has become an exclusive fashion event of the year because of its limited, curated group of presenters. Brand new startup Fun Org began with algorithm-driven activity recommendations for people but quickly switched to a select set of offerings gathered by humans. Behance uses a team of full-time curators to choose which creative work to feature on its homepage. YouTube’s homepage is no different. And on and on.

The problem these tech companies — and their investors — struggle with is that curation requires humans. Humans are expensive, and they don’t scale. The whole promise of Google was that IT could use data to automate just about anything. As tech companies begin to look beyond the algorithm, they’re grappling with the problem of scale and human capital. Computers can’t always do the job. That was the promise of crowdsourcing — everyone from news organizations to online communities like Digg turned to the wisdom of the crowds to automate work once done by a human. Now they’re learning that’s not always the best solution.

A business based on human curation will always be smaller by definition than one that scales through algorithms and computers. But that human element makes what they’re building that much more valuable. High end luxury products companies like BMW, Chanel, and Rolex will never be as big as Ford, H&M, and Casio.  But that’s fine, indeed desirable. Because tech can be replicated. Taste cannot.

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