In today’s world of digital media, where there are no barriers to entry and distribution is dominated by social networks, how does a company effectively capture consumer time? BuzzFeed’s answer has been an increased focus on machine learning to create efficiencies in content production and distribution.
BuzzFeed is one of the world’s largest digital media companies, generating over 65 billion video views in 2017 across social platforms such as Facebook, YouTube, and Snapchat . They produce 1,200+ new pieces of content every week and distribute them across 400+ social accounts .
Digital Media Landscape
Over the past few years online media consumption has shifted to social networks as publisher owned websites see significant declines in engagement . The open nature of social platforms has created a highly fragmented and saturated content environment. It is extremely difficult for premium publishers to surface their content as they compete for time with everything from low cost blogs to a user’s friends and family. Advertising dollars have followed the trend in viewership. Almost 90% of digital advertising growth in 2017 was captured by tech platforms . These trends are forcing publishers to create more content at lower costs in order to stay viable.
Machine learning can address these challenges in two major ways: cutting content production costs and creating more efficient distribution. Content production can be impacted by cutting out many manual processes. For example, the AP implemented a machine learning program to reduce the 800 hours a week spent converting print stories to broadcast formats  and Forbes and LA Times are experimenting with fully automated basic news content . The distribution process can be improved by effectively predicting when and where specific content should be published to maximize consumer discovery. One case is the New York Times’ Blossom app that decides which stories deserve additional promotion . BuzzFeed is especially well positioned to take advantage of machine learning as their current scale gives them an initial data advantage over other publishers.
Machine Learning at BuzzFeed
BuzzFeed has already implemented a number of machine learning applications in their short-term strategy. For distribution, because they produce so much content and have so many different channels, they need to quickly identify the content that will drive the most engagement. Their current machine learning tools are able to recommend what content will work well on a specific page or what the ideal posting time will be, as well as A/B test thumbnails and titles so posts can be automatically optimized for both . They also incorporate a feedback loop where dismissed recommendations are pushed back into the model letting it learn over time .
In terms of content production, BuzzFeed’s PubHub tool implements time saving initiatives such as auto generating YouTube descriptions . Additionally, they have tools that drive new content decisions by suggesting topics or formats that work for specific audience types. They can also bunch together high performing content to better identify trends. For example, they had an early insight that videos with cheese being pulled were particularly engaging, which helped launch some of their food brands .
Over the long-term, BuzzFeed is working to create a much faster experimentation process to iterate on machine learning algorithms. They believe this will lower the threshold for publication and get products out much faster. Additionally, they are planning to experiment with neural networks through third party applications, giving tools the power to make simple content decisions such as title changes .
One suggestion for BuzzFeed is to give their tools more flexibility to execute decisions. Currently BuzzFeed doesn’t see machine learning as able to actually give production input or pull creative insights . While machine learning cannot create wholly new, creative content, much of BuzzFeed’s content is based on historic iteration for new markets or topics and could be optimized. In the short term I would suggest letting their tools auto post iterated content or cross post to multiple accounts. In the long term I believe the content based on historic iteration should be fully created by a machine learning tool, with some human oversight.
Additionally, BuzzFeed currently has a competitive data advantage and it is important to maintain this. Current plans are to have some future machine learning programs licensed through third parties since the investment to produce in house is too high . But I believe it is important to make that investment themselves in order to capitalize on their data advantage while they have it, and not level the playing field by being reliant on third parties.
When reflecting on BuzzFeed’s machine learning strategy two major questions are highlighted: can machine learning or AI ever fully replicate the creativity of human content producers? and how much financial risk should BuzzFeed take on to invest in machine learning programs?
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 Max Willens. “How BuzzFeed gets its employees data-focused”. Digiday, March 2017. https://digiday.com/media/buzzfeed-gets-employees-data-focused/
 Interview with BuzzFeed Principal Data Scientist Adam Kelleher. Dataframed, February 2018. https://www.datacamp.com/community/podcast/buzzfeed-digital-media