This long piece neatly sums up the paradox of the age of algorithmic analytics:
“Algorithms that tell us which topics are trending don’t merely reflect trends; they can also help create them…
“The internet has shown us that the oddest of subcultures and smallest of niches can develop followings… I don’t think readers weren’t interested. It’s that they were told not to be interested. The algorithms had already decided my subjects were not breaking news. Those algorithms then ensured that they would never be.”
This approach of following your analytics is a *terrible* content strategy. By pursuing a mass audience and popularity above all, same as everyone else, you’re doomed to lose your distinctiveness – and relevance to your true target audiences. Even though the algorithms supposedly love relevance above all, they’re still (usually) not sophisticated enough to identify your priority audiences among all those visits.
This is why we’re seeing so many traditional publications fail, and ad revenues collapse: They’ve all become alike, because the algorithms have told them all the same things. That’s made them less valuable, in terms of both price and utility.
Don’t get me wrong: audience analytics are essential. But you need to know how to read them – and their limitations.