Predictive analytics and statistical analysis are based on the concept of relationships between observed and future actions. When analyzing people, we observe a small sample of data on people and build a predictive model to identify a number of shared traits they have. Micro-targeting is the idea of finding relationships among variables to recognize the target audience's shared traits. It helps to identify the right people. The steps included to predict purchase behavior and design a campaign to expand customer base are: 1. Create dataset. 

2.  Once we have a dataset loaded, we will analyze the people who have purchased the cloud solution and find what they have in common with one another. 

3.  To do this, our first step is to create a dependent variable. 

4. Then, we build a predictive model which takes that variable containing cloud purchase information, and compares it to other variables in our data set. 

5.  The regression model we build then compare our cloud purchase variable to whichever other variables used for analysis, and then gives us statistical correlations for each variable. 

6. Initiating the cloud solution licensing, then identify more people who fit this demographic.

For more read the full article at:

http://www.econtentmag.com/Articles/Column/Marketing-Master-Class/Why-Predictive-Analytics-and-Microtargeting-is-a-Game-Changer-for-Marketers-109377.htm

Statistical analysis is being used extensively in all sorts of sports now-a-days. However the growth of analytics in soccer has been quite slow, mainly because of its orthodox nature. Hence, enters Thomas Bayes’ famous theorem, the Bayes’ theorem, according to which, the chances of an event taking place, is updated, by integrating the former chances of its occurrence and the new information that might affect its prospect. The Bayes’ theorem completely encourages the use of analytics to revise our perspectives regarding players and teams. Stats refer to raw data collected from each match regarding both the individual player’s performance and the performance of the team as a whole. Analytics manipulates this data, thereby extracting the desired information, which helps in predicting future game scores. Read more at:

http://www.sportsnet.ca/soccer/soccer-analytics-premier-league/

Data is more than just power. Organisations across all industry verticals are upgrading their data management systems, investing in new resources, and using their rich databases to streamline the practices of their departments. Indeed the message from experts is clear: organisations that fail to adapt and evolve to meet the emergence of big data, face the prospect of falling behind. As with any phenomenon, however, there are lessons to be learnt. 

The way data is being used in sports is a poignant example. Moneyball, the popular book inspired by Oakland Athletics manager Billy Beane, explained the core philosophy of the manager’s vision for the baseball team: using statistical analysis to maximise player acquisition and performance with a low budget. 

The Moneyball philosophy had huge ramifications for the sporting world. People started adopting variants of it in all sports – from soccer to basketball to football. Arguably the most noticeable application of Beane’s philosophy was by Andy Flower, the former England cricket coach. Flowers was known for his admiration of Beane’s work, and he too would use statistical analysis to not only determine who would be on the field but also what decisions players should make once they were selected and enjoyed notable victories also. Both of them have stood by data analytics and the benefits it can bring. Yet, what is often untold is that data was both a virtue and a vice for both men.

His 5-0 defeat at the Ashes last year was one of England’s most disappointing performances to date. As commentators suggested, it was a classic case of overreliance on data, replacing intuition with numbers, and allowing data to dictate rather than inform. Flower ultimately got the balance between trusting people and numbers wrong. He was in good company, those who thrive will not be those who use data most—but those who use it most smartly. But data is emphatically not a substitute for intuition and flair - either in the office or on the cricket field.

These instances of sports analytics are particularly relevant for organisations looking to add big data analytics to their existing operations. The example of Beane and Flower show how data does not have all the answers, and relying too heavily on it can have devastating effects.

Read more at: http://www.espncricinfo.com/magazine/content/story/724435.html