In the race to become data driven, it’s clear that some industries have a tougher time than others. Namely, industries where human intuition is still of prime importance such as journalism or PR (public relations) are particularly difficult to quantify. To be certain, PR has metrics. However, it’s not quite clear what measures like outlet circulation/UMV or social shares for company content actually equate to in terms of business value. More importantly, they offer little in terms of predictive value. What quantifiable elements in a blog post, for instance, are predictive of how many times it’s shared on Facebook? Answers to such questions remain elusive.

Furthermore, many of the metrics in PR are difficult to apply to standard attribution models. For example, while it’s been shown that a positive article about a company or brand can boost favorability, it’s difficult to tell for sure if a well-placed story on your brand actually leads to a certain action being taken. There’s no direct trail between the two, as there often is in paid marketing tactics such as online advertising.

We also have a lot to learn about what shapes media trends. Why, for instance, does one story get covered by every major outlet in the world while another story–which may appear to the objective eye to be just as compelling–go totally ignored.

#ai & machine learning #data mining #journalism #machine learning #pr