Yes, another post about social media data but this one should add value. This is not a post begging brands to use social media or join the conversation. Most brands already do this, and don’t necessarily suck at doing it.
This post is about using social data two inform how you communicate and engage with various social audiences.
Social media data is information is based on users within various social channels publicly share, including location, language, how they describe themselves in their bios, and/or what links they are sharing with their social communities.
Social media data 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 four-step process in using social media technology to drive competitive advantage.
Social Media Data Collection
The first step is scraping social media data across all the platforms. This is easier for some social networks than it is for others. For example, most social listening platforms already scrape Twitter. They have full access to what Twitter calls their fire hose. Most other platforms also scrape URLs which you may or may not consider as being social data.
Unfortunately, you will have to scrape Facebook, Instagram and LinkedIn using a scraping tool. The most important thing to consider within this first step is to determine what questions you want to answer and what is your data source.
Social Media Data Mining
I am a firm believer that’s 70% of time spent on a content analytics project is spent mining the data. This requires a human to use various tools and filtering to manually read and sift through information. This also involves structuring the data so that it could be easily uploaded to a data visualization tool or to create a pivot table.
The result of social data mining is that the person doing it can more easily contextualize the insights based on what they are seeing 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.
Social Media Data Analysis
Some might say that data analysis is the same as data mining but in this context, it isn’t. 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 mentions of the product to see 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
Once all of your social media data is structured and analyzed, you can upload it to a data visualization tool like Google data studio or Tableau. This is good for those who want to tell a story using charts and graphs and other visualizations. It’s also beneficial because it allows access for other people to view and interact with the data visualization.
Social Media Data Report
In many instances, exporting the data into a social data report is the right thing to do. It really depends upon how sophisticated your audience is or whether they have a license to Tableau or other data visualization tool. Whatever you choose, it’s always a best practice to visualize social analysis using some type of 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: it is critical for brands to 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.