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 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 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.