The document discusses the relevance of social media for market researchers, noting that platforms like Facebook have hundreds of millions of active users generating billions of pieces of user-generated content daily, and that analyzing this unstructured social data can provide insights into what people are saying, their sentiments, and who the influential voices are when traditional research methods are unable to explain why some people like a product but do not purchase it. It also addresses some common myths and challenges around using social media for research.
4. Channel Expansion More than 400 million active users 50% of our active users log on to Facebook in any given day More than 35 million users update their status each day More than 60 million status updates posted each day More than 3 billion photos uploaded to the site each month More than 5 billion pieces of content (web links, news stories, blog posts, notes, photo albums, etc.) shared each week More than 3.5 million events created each month More than 3 million active Pages on Facebook More than 1.5 million local businesses have active Pages on Facebook More than 20 million people become fans of Pages each day Pages have created more than 5.3 billion fans
6. A journey to the relevance of unstructured data When relationships don’t tell us why… “when they like our product but won’t buy” Making cacophony musical… “when a story emerges”
7. When relationships don’t tell us why… “when they like our product but won’t buy Do we need to increase familiarity? Is our brand to weak to convert? Unlikely to Buy We like
8. When relationships don’t tell us why… “when they like our product but won’t buy Do we need to increase familiarity? Is our brand to weak to convert? Unlikely to Buy We like We want to buy but procurement will not let us… Corporate governance prevents us from considering …
10. Making cacophony musical… “when a story emerges” Story emerges from the conversations… Top Mind: Coherent Issues Emerge
11. The Big Issues Surrounding Social Media Analysis Cost Myth Should be a fraction of the cost of traditional survey research Accuracy Myth Most mining software can accurately identify content and sentiment Scope You can find anything you need on the ubiquitous Web Representativeness Online sources reflect the voice of the market
12. SM = consume + create + share Source Classification Sentiment Volume Time Influence Building Blocks of Social Media Analysis
13. Research Questions Source Classification Sentiment Volume Time Influence Where are the conversations occurring? What are people talking about? Are conversations negative or positive? How many people are talking? Is the conversation changing? Who leads the conversation?
14. Business Questions Source Classification Sentiment Volume Time Influence What channel should I target? Can I align messages to market interest? Can I amplify positive reaction? Can I prioritize the top mind share? What issues are gaining momentum? Can I target the opinion leaders?
15. Data Collection Trends: 400+ solutions Convergence Specialization What to watch out for: Harvesting capability Targeting capability Spam controls Full Feed – use of aggregators
16. Text Analytic Tools Trends: 60+ Specialization: domains and calls to action Processing Options Natural Language Processing Machine Learning Entity Extraction What to watch for: Auditing Rule building Clustering Sentiment tuning
17. Addressing the Big Issues Cost Front loaded costs – but expect savings down the road Accuracy Myth Best in class analytics and auditing ensures higher levels of accuracy Scope Limits of TOM - there is still a role for traditional research Representativeness Full feed is critical – but hard to manage