SESSION 2

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.