Some statistics I have gathered for a presentation I am working on.
- 53% of Facebook users play games
- 19% say they are addicted
- 69% of Facebook gamers are women
- 20% have paid money for in-game benefits
- 56 million people play daily (more than the population of England)
- 290 million people play monthly (almost as many as entire US population)
- The average time spent per month on Facebook is 421 minutes (7 hrs 1 min)
- 50% of Facebook logins are specifically for gaming
- Roughly 927 million hours per month are spent gaming on Facebook, which equals 105,878 man-years’ of gaming a month!
- Facebook usage was up 40% in 2010
- 3.5 billion pieces of content are shared each week on Facebook
- 65 million Facebook users access via mobile devices, up 100% from last year
- 96% of 18-35 year olds are on a social network 1 in 5 of those are on Twitter
- 78% of of consumers trust peer recommendations with 14% trusting advertisements
- 34% of bloggers post opinions on products and brands
- There are 70 translations of Facebook currently
- Twitter adds 300k users a day
- 25% of search results are links to user generated content
- Over 1 billion YouTube videos are served a day
Was reading an interesting article which represents a good introduction to popular data mining techniques. That is the true beauty of the study of data mining is its not really the tools used. It is more what you do with these tools in terms of how you tie them together and build the models. You could compare it to a painter. Anyone can go out and get an easel and paint. But not everyone can paint a wonderful work of art.
Marketing and sales professionals are beginning to capture and analyze many different types of customer data—attitudinal, behavioral, and transactional—related to purchasing and product preferences to make predictions about future buying behavior.
Today’s challenging environment is forcing more organizations to explore predictive analytics. Commonly used by market researchers when analyzing survey data, predictive analytics can also be applied in real-time scenarios, such as personalizing offers to customers or improving an online customer experience.
There are various approaches to predictive analytics, and most depend on clean databases and the ability to mine data to look for patterns or to create classifications. It is important to understand the various approaches so you know when to use which one.
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