Tagged: data

On Social Media Sampling

In social media sampling, there are many issues. Two of them are: 1) the silent majority problem and 2) the grouping problem.

The former refers to the imbalance between participants and spectators: can we trust that the vocal few represent the views of all?

The latter means that people of similar opinions tend to flock together, meaning that looking at one online community or even social media platform we can get a biased understanding of the whole population.

Solving these problems is hard, and requires understanding of the online communities, their polarity, sociology and psychology driving the participation, and the functional principles of the algorithms that determine visibility and participation in the platforms.

Prior knowledge on the online communities can be used as a basis for stratified sampling that can be a partial remedy.

Big data is not enough data

My argument here is simple – despite the common argument that ”everything is tracked”, marketers face a big data fallacy when assessing their ability to predict consumer behavior.

The reason is simple [1]:

”On any given occasion, everything from personal factors such as how well a person has slept the night before, current mood, hunger, and previous choices, to environmental variables such as the weather, the presence of other people, background music, and even ceiling height can influence how a customer responds. Algorithms can use only a handful of variables, which means a lot of weight is inevitably placed on those variables, and often the contextual information that really matters, such as the person’s current physical and emotional condition or the physical environment in which the individual is tweeting, Facebooking, or buying online, isn’t considered.”

Therefore, what is known is simply not enough to accurately predict an individual consumer’s behavior. On average, however, given the limitation of computable variables, marketing algorithms can enhance marketing performance. But data will never make marketing ”perfect” – just simply because there’s not enough of it.

[1]: Dholakia (2015) https://hbr.org/2015/06/the-perils-of-algorithm-based-marketing