What it Takes to be a Social Media Analyst
– by Reihardt Schuhmann, October 03, 2015 (Published on Social Media Today)
The rise of social media has created entirely new job opportunities in a wide variety of areas. Creatives, marketers, software engineers have all added new areas of expertise to their professions. New job categories, such as the community manager, have also been created to meet the needs of the rapidly evolving social media landscape.
Another new category of professional that continues to increase in importance is the social media analyst.
Social media analysts mine vast amounts of social data for insights that can both show ROI and drive strategy. They’re often tasked not only with informing and measuring social media efforts, but with using social data to determine the best courses of action across the entire organization. They deal with data that shows human behavior, language tendencies, demographics, psychographics, and paths to purchase.
Analysts and data professionals have had a place in business for some time and have seen an increase in their prominence within enterprises for many years. There are, however, specific skills required for social media analysts that set them apart from other data professionals. Successfully mining social data for business insights requires a unique approach and outlook – let us take a look at some of the key skills and mindset that make for successful social media analysts.
Affinity for Numbers
As with any data profession, social media analysts must be adept at working with numbers. Experience in statistics, data science, computer programming, and econometrics all serve as a strong backgrounds for social media analytics. Anyone who feels overwhelmed by figuring out how to leverage math to sift through large data volumes need not apply. In addition, because social analytics is a relatively new field, the best process for mining a dataset may not yet have been determined. Social analysts will need to welcome new challenges in this regard. Which brings us to our next qualification…
The exact path to insight is not always clear when looking at social media conversations. Social data is largely unstructured – meaning text heavy – with no pre-defined data model or organizational structure. Social media management software does a good job of adding structure to this material, such as sorting by date, text mining to uncovering themes and presenting demographic information included in the metadata of social content. This is, however, often merely the starting point to uncovering meaningful insights. Social analysts will have to rethink how they’re structuring queries to collect data so they can split a larger pool of information into useful subcategories. A willingness to understand language usage and creatively apply this understanding to collecting and sorting data is often the difference between high level and fully detailed findings.
For example, the data might show a spike in activity for a luxury brand on a single day, but now the analyst needs to determine the reason for the spike and what it means for the brand. Maybe it was due to a highly publicized partnership that generated positive press, perhaps a lot of people were sharing coupons. In either case, the analyst needs to come up with a recommendation for the business moving forward.
Social Science Research Skills
While there’s a strong numeric component to social media analytics, it requires a knack for understanding human behavior and relationships. In many cases the numbers can point to several characteristics of a customer group that is active in social, but qualitative research is required to tie these together and produce a fully fleshed out customer profile. Psychographics are learned from actually reading social content tied to an emergent theme. The best social media analysts I know are the ones that can take data-based findings and translate them into insight about the actions, thought, and feelings of people. The data might show thirty emergent trends from people talking about basketball, but a good social analyst will be able to determine that eight of those trends represent the behavior of a single audience segment.
Because social media data is a relatively new field, colleagues will be quick to question its validity and value. This means that persuasive storytelling, to make clear that this information can drive business success, is crucial. As a social media analyst, even when I was delivering reports to other people on the social media team they would often question the data and findings. If you can’t paint a compelling picture to demonstrate the importance of this information, it’ll get pushed away in favor of other data, often data that is weaker and less representative of what consumers do and how they feel.
Nine times out of ten, there’s no playbook for social media analytics. Even in organizations where certain social data deliverables have been established, it’s to be expected that a senior team member can throw the existing process out the window. Getting colleagues to buy-in to social analysis can require frequent shifts in how the data is presented. On top of all that, the social media landscape is ever-shifting in terms of network popularity, usage trends, and data management technology. Oftentimes, as soon as you become familiar with an established collection of social networks and software for managing data, new players join the game. Because social media users dictate what platforms are important, analysts must adapt to the changing climate.
There is a level of patience needed, not only in communicating with colleagues who may not yet see the value in social data analytics, but in mining this information for these insights. The expectations for social analytics are often that it’ll work as quickly as social media content spreads. While collecting and process the data can occur in real-time, it still takes time for a human analyst to draw any meaningful conclusions from the data. Inevitably, some social analytics task will require pouring over hundreds or thousands of posts and interactions. These instances are exercises in patience, both for the analyst and those looking to act on social data insights.