We see a lot of studies and “research” in the world of social media. What is the most popular social network? What is the best time of day to Tweet? Do images draw more engagement than text or vice-versa?
We see studies come out from a variety of sources, and we use these sources to inform how we proceed in social media marketing. But do we know how real the numbers are? Are we being critical in our reading? Are we exercising the “responsibility of the audience?
I ask that not to cast aspersions on the survey data being published in various publications and blogs. I want to make sure that if we are repeating data, we are understanding its limitations, it biases and its real value.
Most recently, I noted that Google was touting that Google Plus was now the second-most popular social network. But what was the sample? What were the definitions of “active use?” Knowing who was asking those questions is more valuable to me than the data itself. It tells me who is taking this seriously and who is just lapping up data delivered to them regardless of the quality of the source.
In the case of the Google Plus data, I would want to be sure we are talking about intentional actions rather than the passive robotic motions of people who merely have Google accounts- are people really active? That is the biggest question to me. Google tried to answer those questions here, with some success. Even without complete answers, the trending data seems to show that there is growth in Google Plus, regardless of whether “second place” is accurate.
Years ago I worked for a research company. what I learned there was the value of a “statistically valid” sample in order to project authority. Even when I used our resources to produce research for marketing purposes (a valuable and worthwhile lesson), I had to make a strong effort to put together a survey sample of great enough variety and demographic to represent something meaningful. Even then, the methodology needed to be published alongside the data to let the audience account for some possible biases or errors.
More than knowing what data purports to tell you – question the source, Not because you will debunk the numbers, but because you need to know what you’re talking about if you want to be taken seriously.