Strengthening the Social Media Strategist

Bosque de Chapultepec, Mexico DF - Mexico

Picture taken by Jonathan Acuña (2019)

Strengthening the Social Media Strategist

Going beyond the community management

Do you ever wonder how social media data contribute to brand campaigns in your organization? If you do so, you probably question yourself about how data is supposed to be read, about what is worth around the tons of data a social media channel can produce, and how these data can sit alongside the community management of your company. The fact is that data analytics is not like rummaging your wardrobe to get dressed -in a hurry- to go to work; it is like spotting a tiny, wily insect in the brushwood if you are not equipped with the right tools.

What produces data in a social media community channel? Based on Joe Cothrel from Lithium (The University of Sydney, 2020), “really any feature that you allow a user to use in an online community creates data.” That is, data’s lair in the community media outlet contains information produced by any feature that can be scattered and just accumulated somewhere. As the eagle, it is necessary that the social media strategists pounce down upon the pieces of input to start making sense of them. But going beyond the conceit of a creator of a successful online community, favorable outcomes are only attained when data is correctly read and interpreted; it is not just a lucky strike.

What type of data can be dug up from an online community profile? For Cothrel there are two types of data that can obtained (The University of Sydney, 2020): Qualitative and quantitative data. Walking into the qualitative side of data is like entering a vestry for the first time; you barely know what you can expect to find in a place like this. You can be appalled at the sight of data and communities’ qualitative insights. This qualitative data stored as part of the history of an online community is connected to what happens in it around people’s reactions towards content that is being posted, e.g., by a community manager. And all this input appears “in the form of words or opinions or topics that people are talking about” says Cothrel (The University of Sydney, 2020). Finding the commonalities among these words, opinions, or topics is the challenge of the data analyst and the social media strategist.

But finding commonalities is not all; statistical analysis of data can also provide useful insights, too. Part of data digging is making sense of the things people are doing online when visiting an online community. What is it that your viewers are looking? What is it that your visitors are posting on your community wall or reposting from your community? How are your followers or first-time visitors rating your brand campaigns? All these generates statistical data that can be pulled out of the system, read, and interpreted and see how all this input can be used to continue to serve your community and make it grow as a healthy environment free of trolling vassals and shorn head trolls looking for trouble.

Before a shudder passes through your system and you start to feel you are just wearing a loincloth, consider the following questions regarding your quantitative and qualitative data. By asking yourself similar questions like the ones bellow, suggested by Joe Cothrel, media strategists can get ready before a brand campaign is launched.

a)	“How might I use that data to engage my community of users?” (The University of Sydney, 2020)
b)	Before the metrics, “what are my objectives in the system?” (The University of Sydney, 2020)
c)	Why was the community created?
d)	What is the organization trying to accomplish?

Now, using an art gallery as an example for a social media strategist, let us review the same questions and think of how they could be answered.

a)	How might the art gallery use its data to engage its community of users and art lovers?
b)	Before the metrics, what are the art gallery’s brand campaign’s objectives in its social media community?
c)	Why is the art gallery’s community’s brand campaign created? What is the goal in this campaign? And how is it going to be measured?
d)	What is the art gallery trying to accomplish with its social media community channel and its users?

And one more example to examine; let us now pay attention to a language school’s campaign regarding the creation of learning infographics for its learners. How can these questions be answered?

a)	How might the language school use its data to engage its community of learners and instructors?
b)	Before the metrics, what are the language school’s brand campaign’s objectives in its social media community with its infographics?
c)	Why is the language school’s community’s brand campaign created? What is the goal in this campaign? And how is it going to be measured?
d)	What is the language school trying to accomplish with its social media community channel and its users (students and instructors)?

And the examples of sets of questions for all varieties of brand campaign could go on and on.

The cradle of data lies in the operational metrics and in the content metrics. The forefathers of social media communities probably were not aware of how these online communal spaces were meant to be used when compared to the way they are being used today. This is not about failing and simply saying that if the worst come to the worst, you still have a second chance around. This is about being sure on what data is being produced by the social media platform that has been chosen (operational metrics) and what content is going to be published for the community of users (content metrics). And when these metrics are understood, where do you want to direct your community for its own benefit and for the organization’s? How do you plan to move on to the next level of proficiency and arrive at the pinnacle of your brand campaign’s success?

Reference

The University of Sydney. (2020). How do we incorporate data analytics into user engagement strategies? Retrieved October 23, 2020, from FutureLearn.Com: https://www.futurelearn.com/courses/ethical-social-media/1/steps/824166