Before we drop into the meat of the course here on people analytics and talk about details, and how and why you should do these analytics, we want to give you a few examples of firms that are using theses techniques. The first is Teach for America. Teach for America is an organization that provides teachers to underserved, underprivileged school districts around the country. They do this by hiring students straight out of college and training them and then putting them into these schools for a couple of years. And they've been doing this for 14 or 15 years now. An interesting thing about Teach For America is this, you might be surprised, but they're actually very analytic savvy and they've been paying a lot of attention to their hiring process and the performance of their teachers, for years now. Specifically, they evaluate, they compare their performance of teachers over time to the predictions they had when they hired the person about how they would perform. They do this in order to improve the hiring process. So they see tens of thousands of applicants every year. In recent years, they've seen 50 and 60,000 applicants. This leads to a funneled process, where they vet the CDs, the resumes at one stage, they invite some of those to do a phone interview, they invite some of those on site to do a face to face interview, and then they hire some subset of those. By looking rigorously at who makes it through the process and how good their assessments are in comparison to what actually happens down the road, they've been able to optimize this hiring process. They're much more efficient now about bringing the right number from the resume level to the phone interview level. And then the right percentage of those to the on site, allowing them to make better decisions and use their resources more efficiently. Another example is a little bit less surprising. This example comes from one of the most analytic savvy companies in the world, Google, who a few years ago also wanted to evaluate their hiring practices. They were going through for years, significant growth and spending considerable time within the firm interviewing potential hires. They eventually decided, do these interviews actually make any difference? They had read the literature which raises the question about how diagnostic interviews really are and ran the numbers. What they found was they were not very good at predicting interviews, were not very good at predicting employee performance down the road. This is a real problem whenever you're spending ten managers interview time on every potential hire that comes through. As a result, they did something that is very un Google like. They provided an edict from above which they never do that said, you can't schedule more than four interviews with managers when you have a potential hire come through. You just can't spend that amount of our resources because we've run the numbers and it's not worth it. It's not worth, the analytics say it's not worth it. Final example comes from the financial services industry. This has been the industry probably second most enthusiastic about people analytics after technology. And an example from Credit Suisse. Credit Suisse believes that a 1% increase in their retention, if they can retain their employees, they can save the company 75 to $100 million dollars a year. So financial services, one of the most important assets is the people. They're going to try to retain their best people a little bit longer and a little more effectively. They did this with a three year study, looking at factors that led the people to leave the firm, or led people to stay within the firm. One of the things they found, one of the surprising results was that those who changed jobs internally were more likely to stay with the firm. They call this stickiness. They said changing jobs increases an employee's stickiness. Once they identified that, they had some levers they could pull to push for more stickiness. So for example, prior to that analysis they had posted internally, less than 50% of the new jobs that came up. But because of that analysis, they realized we need to be placing our people, moving our people more rather than letting them leave the firm. So they increased the postings to more than 80% now of the new openings get posted internally, leading to hundreds more internal hires and according to the models more sticky employees, lower retention, and ultimately, higher earnings. Those are just three examples, there are many, many others across industries. Firms and technology, financial services, telecommunications, automotive, consumer packaged goods, energy, Shell oil. You wouldn't know it, lots of people analytics, not-for-profits, not only Teach for America. They are finding better leverage for retaining key employees, more diagnostic methods of hiring, who their most valuable employees are, how to compose most productive teams, and many other things. These are just some of examples. These should provide some motivation for all of us to get better at this. Which is exactly why we hope you're here and exactly what we want to talk about as we go through the course.