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Can Algorithms Predict the Shareability of Content? Should They?


Unruly Media have just released Sharerank, a "proprietary algorithm which allows advertisers to accurately predict the ‘shareability’ of a video, before it is even launched" and so maximise the impact of their content by calculating in advance the amount of earned media a piece of content is going to secure.

The system uses data from their Viral Video Chart overlaid with viewer response data (over 10,000 data points). Multiple regression analysis showed the relationship between viewer responses and actual share data and enables the identification and contribution of factors that impact on shareability (psychological, social, content triggers and valence). It's claimed that this can correctly predict shareability 80% of the time, a figure that is predicted to increase as more data is added and the algorithm gets smarter.

It's interesting work, but my first thought about it (a question that Econsultancy also asked) was around the risk of clients wanting to create content and ads for the algorithm. Unruly have stressed that the formula is there to help (and not replace) the work that creative agencies do by providing context and insight into the best sharing triggers and content characteristics that brands can use. But it's a thin line between using it in that way and stepping over to using it earlier in the idea generation process and the temptation for clients to apply some degree of predictive capability to something so inherently uncertain will no doubt be very strong.

Serendipitously, the next article I came across after reading about the algorithm was Sarah Carter's excellent piece on renowned BMP creative John Webster (whose creative work is memorable to me and many others more than 30 years after it first appeared). "It's no coincidence", says Sarah, "that Webster's greatest ads were often the ideas that were added into research as the wildcard, 'nothing-to-lose' option, but emerged as the most successful. Most groundbreaking ads, almost by definition now, seem to me to emerge in spite of, rather than because of, regimented creative development systems and processes".

It's a certainty that algorithms will be playing a much larger part in the future of our industry and will touch many areas. But it's important that we remember, I think, their limitations and the value of combining machine-driven learning and optimisation with more human-powered creativity and inspiration.

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