Welcome to the fifth module of People Analytics where you will continue learning about data analysis and motivation. And also learn about yet another driver of performance, employee engagement. Behavioral economists, psychologists and other social scientists dedicated a great deal of time to research how monetary incentives influence motivation and quality of work. Through their investigations, they've learned that sometimes standard incentives that should work on an economic man backfire. Many experiments conducted in the 1970s and 1980s and then repeated again in the 1990s and 2000s show that in certain settings, intrinsic motivation is decreased when extrinsic financial rewards are introduced. This can happen when subjects are paid to do something they consider a good cause like giving blood, for example. Or when they're just doing something they enjoy doing without being compensated. Imagine you're being paid to sleep at night as long as possible, would it help you? Or would you be worrying about sleeping as much as possible instead of actually getting a good night's sleep? Some non-monetary tools are free. It is our goal to understand how to use data analysis to choose the tools that would give you the best return investment. Research has shown that people are motivated by different things depending on various factors. Your employees may have different values and preferences depending on their education and cultural background, age, religion, family status and many other things. World Values Survey, for example, shows that people in different countries give different answers regarding what matters most to them in selecting a job. While people in Russia really care about compensation, 55% of the Swiss say that doing something important is what matters most to them in their job search. That means, as it is usual in people analytics, that there is no one size fits all strategy in selecting a motivation strategy. So let's talk about what can be part of non-monetary motivation. Selecting the right tools is a people analytics problem because not all people are motivated by the same things. Understanding your company's culture, what motivates your team and what increases its productivity should be based on data analysis. If your organization does not have enough data to make predictions before instituting a policy, you could use proxy organizations with similar workforce and try to understand what incentives drive their employees' performance. If this is impossible before implementing a policy, it is critical to select metrics which will help you decide whether a certain policy produced the results you expected and whether it paid for itself or not. All too often, companies copy somebody else's best practices without even understanding their own workforce's needs and without conducting cost-benefit analysis. PlayStations and Xboxes and an onsite daycare center may be great perks, but not if most of your workforce does not appreciate them or even need them, especially not if they believe that you're underpaying them while spending money on mimicking Google office. Using focus groups, surveys, and connecting the data on non-monetary motivation with other HR metrics including performance, you would be able to find the tools to contribute to your organizations. After all, it's not just about having fun, it's about contributing to a stronger organization.