Key capabilities of effective social media analytics

Data Science, AI and ML
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The first step for effective social media analytics is developing a goal. Goals can range from increasing revenue to pinpointing service issues. From there, topics or keywords can be selected and parameters such as date range can be set. Sources also need to be specified — responses to YouTube videos, Facebook conversations, Twitter arguments, Amazon product reviews, comments from news sites. It is important to select sources pertinent to a given product, service or brand.

Typically, a data set will be established to support the goals, topics, parameters and sources. Data is retrieved, analyzed and reported through visualizations that make it easier to understand and manipulate.

These steps are typical of a general social media analytics approach that can be made more effective by capabilities found in social media analytics platforms.

  • Natural language processing and machine learning technologies identify entities and relationships in unstructured data — information not pre-formatted to work with data analytics. Virtually all social media content is unstructured. These technologies are critical to deriving meaningful insights.
  • Segmentation is a fundamental need in social media analytics. It categorizes social media participants by geography, age, gender, marital status, parental status and other demographics. It can help identify influencers in those categories. Messages, initiatives and responses can be better tuned and targeted by understanding who is interacting on key topics.
  • Behavior analysis is used to understand the concerns of social media participants by assigning behavioral types such as user, recommender, prospective user and detractor. Understanding these roles helps develop targeted messages and responses to meet, change or deflect their perceptions.
  • Sentiment analysis measures the tone and intent of social media comments. It typically involves natural language processing technologies to help understand entities and relationships to reveal positive, negative, neutral or ambivalent attributes.
  • Share of voice analyzes prevalence and intensity in conversations regarding brand, products, services, reputation and more. It helps determine key issues and important topics. It also helps classify discussions as positive, negative, neutral or ambivalent.
  • Clustering analysis can uncover hidden conversations and unexpected insights. It makes associations between keywords or phrases that appear together frequently and derives new topics, issues and opportunities. The people that make baking soda, for example, discovered new uses and opportunities using clustering analysis.
  • Dashboards and visualization charts, graphs, tables and other presentation tools summarize and share social media analytics findings — a critical capability for communicating and acting on what has been learned. They also enable users to grasp meaning and insights more quickly and look deeper into specific findings without advanced technical skills.