Social media has become an integral part of life for many of us, whether for personal use or doing business. The use of social media has especially taken an upward turn during the pandemic, as people spend more time at home and communicate almost solely through the internet.
It’s long been conventional wisdom that having a social media presence is an all-but-mandatory part of doing business online — but also, social media has grown vastly more sophisticated and widespread, with algorithms and data science playing an ever more critical role in social media.
The Intersection of Data Science and Social Media
First of all, let’s take a quick look at what data science actually is. In short, data science is shorthand for the array of multidisciplinary techniques designed to glean insight from large amounts of collected data (aka big data), and determine strategies and courses of action based on the analysis of that data.
Big data is used in product development, predictive maintenance, machine learning, fraud detection, and the improvement of customer service and efficiency.
Social media marketing, in particular, relies on data: demographic information, browsing and shopping habits, and customer profiles all come from collected data.
Of course, collected data isn’t really meaningful by itself, in general: it must be prepared, analyzed, contextualized, and presented in an accessible manner to people like stakeholders, owners, and social media teams. A lot of collected data contains vast amounts of what is basically chaff: information that’s irrelevant, redundant, or too oblique to glean any insight from. It’s the job of a data scientist to separate the useful data from the noise, and then organize that information into a useful, comprehensible form.
Part of this process is cluster analysis; the grouping of certain social media habits or trends into particular categories, such as community. Data scientists can use cluster analysis to determine what subjects are being discussed positively within a certain group or demographic in order to find relations between individuals having social media conversations. If individuals are connected enough within a certain community, ads can be more specifically targeted for ads — or if they’re too loosely connected, then a different link between them can be found to work toward an effective marketing strategy.
Once the data has been sufficiently organized and analyzed, the next step is to create a visualization, to aid in understanding the meaning of the data and present it more neatly to people who aren’t data scientists. Visualizations, usually in the form of graphs, can be used in any number of ways, such as scatter plots to show correlations, line graphs to illustrate trends, pie charts to show proportions, and (perhaps simplest of all) tables to show absolute values. In this way, raw data can be made much more digestible and easily translated into actionable insights by members of the marketing team.
Another common means of data collection by social media marketers is what’s called “social media listening.” This is the process of learning what customers are talking about when it comes to your industry — whether it’s your brand, your competitors, their customer service experience (positive or negative), and so on. Social media listening isn’t merely about looking at what customers have said in the past — it focuses on anticipating future needs based on an analysis of what’s being said right now. This kind of predictive analysis is another reason why it’s beneficial to have a data scientist working on your team.
A good example of social media listening in action comes from McDonald’s, which implemented its all-day breakfast largely because the demand on social media was so prevalent and ongoing. By looking at posts and tweets on social media, the company responded to customer demand and made a change in its business strategy.
This technique works because it not only responds to a demand a company might not otherwise have known about — requests like this have previously been the provenance of suggestion boxes and comment forms, which relatively few customers bother with — but it also makes customers feel like the brand is responding to them and engaging with them, which in turn makes a positive impact on brand loyalty and a company’s reputation. In short, customers like it a lot — and the numbers on the marketing data bear that out.
What Type of Skills Do Social Media Managers Need?
So knowing the ever-increasing importance of data science in social media marketing, what skills should employers look for when putting together a social media team?
In the frontier days of social media, it was frequently enough for a business to have one or two employees whose job it was to tweet the occasional promotion, upload a video to Facebook, and manage the occasional customer service inquiry. Those days are long past, and contemporary social media managers need to be familiar with a host of skills, including:
- Communication
- Creativity
- SEO
- Writing
- Strategy
- Analytics
- Marketing and advertising (both traditional and digital)
- Customer relationship management
Most social media marketing specialists typically have a marketing qualification, but advanced knowledge of data science is becoming more of an in-demand skill set. Social media marketers with a data science certificate can interpret social media data, create deep learning algorithms to better understand customers and communities, and help their marketing team develop more effective and relevant marketing campaigns.
Each of these has its own sophisticated skill set, which is why so many larger companies, and even prosperous small businesses, hire entire social media teams to handle every aspect of their marketing efforts. These teams generally break down into a series of discrete roles, such as:
- Social media manager/specialist
- Digital marketing manager
- Copywriter
- Graphic designer
- Data analyst
- Community manager
- Marketing analyst
Creating the Right Social Media Team
Most social media marketing specialists typically have a marketing qualification, but advanced knowledge of data science is becoming more of an in-demand skill set. Social media marketers with a data science certificate can interpret social media data, create deep learning algorithms to better understand customers and communities, and help their marketing team develop more effective and relevant marketing campaigns.
About the Author
Martin Brown is a digital marketing and digital asset management specialist. He has been in the industry for over a decade, helping people understand digital technology and apply them to their business through guest posting. Martin is married with three children. He enjoys playing basketball and scuba diving during his leisure time.
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