Social Media Analytics

Analytics is one of the biggest buzz words in the social media community right now. To new social media managers, it’s a lot to understand (especially because there’s a reason I’m in social media and not math), but don’t worry! The platforms do all the math stuff for you and all you have to do is apply it to your content strategy. You got this!

Social media analytics is the ability to gather and find meaning in data gathered from social channels to support business decisions — and measure the performance of actions based on those decisions through social media. Social media analytics is broader than metrics such as likes, follows, retweets, previews, clicks, and impressions gathered from individual channels. Social media analytics uses specifically designed software platforms that work similarly to web search tools. Data about keywords or topics is retrieved through search queries or web ‘crawlers’ that span channels. Fragments of text are returned, loaded into a database, categorized and analyzed to derive meaningful insights. By knowing what posts and strategies perform the best, you can better plan campaigns.

These analytics are gained from research. Relying on research to make more informed marketing decisions is standard practice for marketers. Tuten (2021) listed two types of marketing research to make decisions. Secondary research is information already collected and available for use (p. 336). It may be internal, published publicly, or available via syndicated sources, and might include background on the market, industry, competitors, and the brand’s history. In contrast, primary research collects data for the research purposes at hand (p. 336). Primary data can help marketers to understand consumers in the market, including psychological makeup, spending and media consumption patterns, and responsiveness to message appeals and offers.

Brand and Consumer Index Spotlight by Industry. SOURCE: Sprout Social

Social media research is “the application of scientific marketing research principles to the collection and analysis of social media data such that valid and reliable results are produced” (Tuten, 2021, p. 337). It encompasses any form of research that uses data derived from social media sources. Companies can utilize these social data by strategically using social listening and monitoring, concepts that go hand in hand.

Social monitoring is the process of tracking mentions of specific words or phrases on social media sites. Social listening also identifies and collects information shared on social media sites, but for listening applications, the data collected is analyzed for insights to inform strategic marketing decisions (p. 337). Though both activities mine social data, monitoring is reactive while listening is proactive.

About 22% of marketers use social listening as a social media tactic (Tuten, 2021, p. 337). Social listening is used for brand monitoring, measuring the effectiveness of specific campaigns, understanding customers, providing customer service, gathering ideas for future campaigns, identifying risks that could lead to public relations crises, gathering competitive intelligence, and identifying ideas for new product development or product improvements.

Social monitoring and social listening aren’t the only approaches when it comes to social media research. Sentiment analysis is a similar approach that emphasizes how people think or feel about an object such as a brand or a political candidate. Tuten (2021) explained “Sentiment is heavier on emotion than reason but it captures an opinion on something” (p. 341). Marketers can use sentiment analysis to examine product reviews to obtain insight into the mix of features people want, and the product’s strengths and weaknesses.

In contrast, content analysis is an approach used to identify the presence of concepts and themes within data sets. Tuten (2021) described that analysts assign codes to classify pieces of information they gather so they can determine any themes that are reflected in a lot of users’ comments (p. 344). When I heard the word codes, my mind automatically went to computer coding, but that’s not what Tuten meant here. Codes are simply labels that classify and assign meanings to pieces of information. A few types of codes are context codes, relationship codes, and respondent perspective codes. For an example of content analysis, a researcher might test a hypothesis that TV commercials reinforce sex-role attitudes by sampling a large number of ads that aired during a certain period of time and comparing the occupations that male versus female actors portrayed.

Coding Categories for Content Analysis

However, be careful. Like with most anything, there are common errors and biases associated with social media research. There are three different kinds of common errors: coverage, sampling, and nonresponse (p. 346). Coverage error occurs when there is a failure to cover all components of a population being studied. Sampling error is the result of collecting data from only a subset, rather than all of the members of the sampling frame. Nonresponse error is somewhat self-explanatory; it’s the potential for those who did not participate to differ significantly from those who did.

Research is a lot of work. In order to make it easier, brands develop social intelligent systems that are capable of capturing, managing, and analyzing social data to identify and apply insights to business goals (Tuten, 2021, p. 351). Tuten highlighted the top tools for free and simple social media listening:

  • TweetDeck
  • BackType
  • Twitter Search
  • Hootsuite
  • Topsy
  • Google Alerts
  • Google Trends
  • Social Mention
Hootsuite’s dashboard, my preferred social intelligence system

A social intelligence system should ideally include four layers: social listening, data management, analytics, and distribution (Tuten, 2021, p. 352). Social listening is the foundation of social intelligence because it sources data. In the data management stage, data are classified and organized according to relevant categories such as specific campaigns or product lines. Data can then be used for advanced analytics and predictive modeling. Lastly, the distribution layer deals with how information is then translated into easily digestible insights, delivered as needed to the appropriate departments and/or incorporated into other data needs. Brands may develop in-house capabilities or may partner with a vendor providing enterprise social listening.

Understanding social media research is important because it allows brands to most effectively learn what their audience cares about and what influences their purchasing decisions. These insights allow marketing departments and social media managers to craft more personalized and relevant marketing experiences.

Of course, you could always forget everything you just read and hire me to do it for you instead. Have an amazing week, and like I always remind you, the information to contact me is down below!

Jordan Price

Price Media



Tuten, T. L. (2021). Social Media Marketing (4th ed.). London, England: SAGE Publications Ltd.

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