Optimized social publishing is an emergent trend of the past year or two that has a lot of hype surrounding it. Essentially, using software of one kind or another, businesses are promised that their updates will be published at the moment they will achieve maximum reach or engagement.
Statistics teaches us that people behave in measurable and predictable patterns when you’ve got enough of them. We should be able to exploit those patterns to achieve optimal results. But what does that look like in practice? There are tons of conflicting recommendations out there and it can get confusing.
There are three basic approaches to social publishing optimization. The first is to gather up a lot of different social profiles and analyze their collective data in a broad study. The second is to take the historical data of one profile and make recommendations based on past interaction. The third is to dynamically publish using algorithmic recommendations.