Machine Learning and Digital Marketing: Melding Human and Machine

In digital, you can easily spot two opposing camps — the artists and the quants.

Artists are folks like the New York Times: Pulitzer prize-winning journalists use their intuition and skill — their unique talents — to create one-of-a-kind stories, and the judgment of the Chief Editor is pure gold. Artists create incredible brand value; true loyalty — lifelong fans.

Quants are folks like eHow.com. They use Wall Street-style algorithms to identify long-tail Google queries that have weak competition, and pay amateur writers $5 to create short posts that address those queries. Queries like “how to remove gum from clothing.” Their quant models tell them that stories like this will make $7 on ads in the next year, so they pump out millions of such stories.

Both approaches have problems. If a New York Times writer gets hit by a bus, there’s no replacing them. Their talent dependency is not scalable. eHow stories — millions of them — inspire no loyalty, create no brand value. Let’s face it, it’s crappy content. No wonder Google did everything in its power to kill it.

You might might ask then, how do you get the best of both worlds? There’s clearly a spectrum here; is there such a thing as inspiration at scale, data-driven AND human powered content creation? This question is relevant for more than content publishing — the same applies to:

E-commerce. Artists: Apple with their Jony Ive videos; quants: Amazon with their algorithmic marketing.
Recruiting philosophy. Artists: Netflix with their culture deck. Quants: US Army with their personality tests.

Leaders in the field realize the dangers of dogma of both extremes, and try to find the middle ground. Let me share a few examples of folks that do this right.

Hubert Burda Media, a European media conglomerate with their flagship German news brand Focus.de, curates stories and follows the news cycle just like New York Times. Unlike the New York Times, though, they are extremely quantitative about what they publish on their social channels and they use sophisticated machine learning tools to make decisions on timing, volume, and merchandising of that curated content.

Uber is a quant-first company: their complex mathematical models predict demand for cabs and direct their drivers to hot regions before demand occurs. And yet, their marketing has plenty of “soul” — just look at the Uber Kittens campaign and try telling me that this isn’t the most creative idea ever.

LinkedIn is a quant-first company: their relevance algorithms determine the best stories to show out of myriads of updates that your connections share. However, with the widely successful Influencer program, they are bringing top writers (“artists”) and exclusive content into the mix, thus allowing their machine learning algorithms to pick from the best raw materials.

eBay, with its strong merchant roots, curates deals and hand-selects the most appealing merchandising for each deal. But the composition of the deals that should be offered to each customer? That’s a job for a machine learning model.

These folks understand that winning is about art AND science. Human AND machine. Human does the creative work, machine takes care of personalization at scale.

P.S. This article was originally published on GeekWire; I also gave a talk at New Tech Seattle based on this article: