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
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.
These strategies affect a range of business activity:
1. Product development
Analyzing an aggregate of Facebook posts, tweets and Amazon
product reviews can deliver a clearer picture of customer
pain points, shifting needs and desired features.
Trends can be identified and tracked to shape the management
of existing product lines as well as guide new product
development.
2. Customer experience
An IBM study discovered “organizations are evolving from
product-led to experience-led businesses.
” Behavioural analysis can be applied across social channels to
capitalize on micro-moments to delight customers and increase
loyalty and lifetime value.
3. Branding
Social media may be the world’s largest focus group.
Natural language processing and sentiment analysis can
continually monitor positive or negative expectations to
maintain brand health, refine positioning and develop new
brand attributes.
4. Competitive Analysis
Understanding what competitors are doing and how customers
are responding is always critical.
For example, a competitor may indicate that they are foregoing
a niche market, creating an opportunity. Or a spike in positive
mentions for a new product can alert organizations to market
disruptors.
5. Operational efficiency
Deep analysis of social media can help organizations improve
how they gauge demand.
Retailers and others can use that information to manage
inventory and suppliers, reduce costs and optimize resources.
Key capabilities of effective social media analytics
A data set will be established to support the goals, topics, parameters andsources.
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 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.