SESSION 1

Social Media Analytics

•  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.

•  It also differs from reporting offered by services that support marketing campaigns such as LinkedIn or Google Analytics.

•  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.

•  Social media analytics includes the concept of social listening.

•  Listening is monitoring social channels for problems and opportunities.

•  Social media analytics tools typically incorporate listening into more comprehensive reporting that involves listening and performance analysis.

Need of Social Media Analytics

Social media analytics helps companies address these experiences and use them to:

 Spot trends related to offerings and brands

 Understand conversations — what is being said and how it is being

received

 Derive customer sentiment towards products and services

 Measure response to social media and other communications

 Identify high-value features for a product or service

 Uncover what competitors are saying and its effectiveness

 Map how third-party partners and channels may affect performance

Advantages

Our eyes are drawn to colors and patterns. We can quickly identify red from blue, and squares from circles. Our culture is visual, including everything from art and advertisements to TV and movies. Data visualization is another form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers. If we can see something, we internalize it quickly. It’s storytelling with a purpose. If you’ve ever stared at a massive spreadsheet of data and couldn’t see a trend, you know how much more effective a visualization can be.

Some other advantages of data visualization include:

• Easily sharing information.

• Interactively explore opportunities.

• Visualize patterns and relationships.

• 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 behavioural 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.