Social media data has expanded exponentially over the last ten years, and for a good reason. Social media platforms like TikTok, Instagram, and Snapchat have become an integral part of our culture, boasting billions of active social media users globally. Today, consumers have no problem adding personal information to their social profiles and publishing content about their personal lives, interests, what they love, and what they hate. This data is stored on the internet and given to advertisers to target with campaigns. Social media data is also becoming easier to work with as new technology is helping researchers manage and analyze it more efficiently.
Understanding Different Data Formats
Data can be separated into four main categories: structured, unstructured, semi-structured, and weakly semi-structured.
Structured data is stored in a database that organizes information with strict formats for each piece of content. This type of data is most accessible for future trends or behavioral indicators.
Over 80% of data is unstructured. Unstructured data is typically text, photos, or videos not organized in a database. This type of data can be beneficial, but it requires different tools to structure, process, and analyze. It is also more challenging to mine the trends because there aren’t defined fields.
Semistructured data is stored in a database, but it must be parsed according to some criteria set forth by an analyst. For example, RSS feeds are kept the same way as unstructured data because they do not have clearly defined fields.
Weakly semistructured data can be found in databases that have information stored, but it also lacks defined fields. This type of data usually has a hierarchical structure, allowing for simple analysis to find trends or variables within a subset. For example, a social media directory would include weakly semistructured data because a particular article may only include the name, URL, and a short description. The hierarchical structure gives marketers more clarity on the social media data used in the analysis.
What is Social Media Data?
Social media data is information published by users on their personal social media channels. This includes name, age, gender, language, location, job titles, and how they describe themselves in their social media bios. It also includes who they follow and the daily posting of text, videos, and photos describing their interests and sharing them with their social communities.
Social media analytics is valuable to marketers looking for audience insights that may drive brand engagement, increase sales, leads, or conversions, build brand reputation, or change consumer behavior.
Over the last several years, I have documented a five-step process in using social media technology to drive competitive advantage.
Social Media Analysis Design
Designing your research analysis should always be the first step in any project. This requires asking and answering critical questions: What is your analysis trying to accomplish? Who will be doing the work, and how much time will they have? What data will you be using? What is your procedure for coding and reviewing the results of your analysis?
These are all questions that must be documented and answered before getting started. Many projects fail because this step was skipped, or there wasn’t adequate time spent thinking through the problem you are trying to solve with social media analytics.
Social Media Data Collection
Data collection is the next step. This requires scraping social media data across all the platforms and structuring and organizing it. This is easier for some social networks than it is for others. For example, most social listening platforms have been scraping Twitter since 2007. They have full access to what Twitter calls their fire hose. Most other platforms can scrape URL data from articles, forums, and review sites, which is also beneficial.
Unfortunately, Facebook, Instagram, and LinkedIn have very strict APIs, so scraping their data is not easy. Some scraping tools can do this, but I would be careful using those because it might be against each of the social network’s terms of service. The most important thing to consider within this step is determining what questions you want to answer and identifying the correct data source.
Social Media Data Mining
70% of the time spent on a content analytics project should be spent mining the data. This requires a human filter, manually searching, categorizing, and reading through social media data. This also involves structuring the data to be easily uploaded to a data visualization tool or creating a pivot table.
For example, in your project, it will be much easier to parse through Twitter data because they have an API. If you are scraping from forums or review sites, you may have to search for specific keywords and phrases within those sites. So you will need to identify the correct search terms for your hypothesis and build a complex Boolean query to capture all the social media data.
The result of social data mining is that the analyst can more easily contextualize the insights based on what they see in the data. The beautiful thing about this is that you don’t have to be a data scientist or even an analyst to mine data. There are tools available to help.
Social Media Data Analysis
Some might say that data analysis is the same as data mining, but it isn’t in this context. Data analysis tests a specific hypothesis and translates those findings into insights.
For example, let’s assume that while you are data mining, you notice some conversation about a specific product. Your hypothesis can go one of two ways. Perhaps it was an anomaly, and the audience only mentioned it this one time, and you found it while mining the data. Or, the audience talks about it a lot.
To test the hypothesis, you might create a filter and look through the data for all product mentions seeing whether conversation volume is high or low. If the volume is high, your insight is that this audience talks a lot about the product, and you may even go one step further and start tracking brand sentiment.
Social Media Data Visualization
Many analysts and BI engineers work with data visualization tools such as Tableau, Qlik, Google Data Studio, or Datorama. These tools allow them to quickly see the insights within their social media data and test hypotheses based on those findings.
Tableau is one of the most commonly used data visualization tools. It allows for easy configuration and changes to be made with drag-and-drop functionality. Within Tableau, you can visualize different data types, including line charts, bar graphs, and bubble charts.
Google Data Studio is another popular data visualization tool. Users can build different reports with Google Data Studio, including data charts and pivot tables. You can also visualize your social media analytics into these reports to discover patterns or significant relationships between variables.
While Tableau and Google Data Studio are popular data visualization tools, many free options available online can be just as powerful.
Social media data visualization tools are good for telling a story using charts, graphs, and other visualizations. It’s also beneficial because it allows other people to view and interact with the data visualization itself. It’s important to note that data visualizations may also be referred to as a social media dashboard or social scorecard.
Social Media Data Reports
There are two main types of social media data reports: Campaign Performance and Social Media Scorecards.
A social media scorecard is a report and visualization that uses one or more metrics to determine an organization’s success in each of the three areas of social media marketing — relationship, listening, and engagement. Generally, these are assessed by audience size, share of voice or conversation, social media engagement, and customer care metrics like complaints received, etc. Many social media scorecards are designed around monthly or quarterly reviews that assess progress toward pre-defined goals.
Social media campaign performance is the practice of identifying, collecting, organizing, analyzing, and reporting on a program or campaign. It is the process of evaluating, measuring, and interpreting social media results and will include financial metrics like sales and ROI. It will also have social media data like clicks, CTR, impressions, clicks to the website, conversion rates, and unqualified leads.
In any case, exporting the data into a social data report is the right thing to do. It depends upon how sophisticated your audience is or whether they have a license to Tableau or other data visualization tools. Whatever you choose, it’s always a best practice to visualize social analysis using social software.
The great thing about putting the data into a report is that you can control the whole narrative and tell a story from one slide or page to another. The bad thing about putting it into a report is that some people might not read it in the file sizes are too high.
The biggest takeaway is this: brands must use social media data to inform how they engage with audiences. This can go a variety of different ways. You could use this data to inform headlines, social media content, blogs, and you can also prioritize media relations strategy based on what the audience is reading and sharing online.
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