 So, I will explain three case examples to convey this point, that how it is more important to pick up the best ratio, best analytical ratio. And if we choose the appropriate analytical ratio, we get business insight, we get HR insight, that has direct impact on our revenue or profitability. So, this is one example given by Rasmussen and Ulrich in their paper. This example is of the Merck's drilling, this is the offshore drilling company. This the challenge this company faced was filling the lead specialist positions due to industry talent shortage and growth. So, for this particular position they were not getting enough talent. They experimented, they introduced a strategic initiative to develop the technical talent for the senior specialist target positions. So, they identified few people and they also acquired more talented people supposedly more talented people from different campuses. They also introduced the programs specifically designed to enhance the technical talent in their organization. And when they applied some simple heuristics, some simple data analysis to look at who are the people doing best in the years in the range of 6 or 7 years, what they found was that company graduate program. So, they had a graduate program wherein they used to recruit fresh engineers out of the campus. They would take them through a thorough training program and they were also having engineers who might have started as a diploma engineer and they have grown in the rank and they have got the promotion. What they found was that graduate engineers coming through the graduate program for a specialist they were able to get promotion their performance was much better in comparison to those who were there with the organization for long years. And because of their tenure they have got the promotion and got the specialist position. So, it was very clear they reduced the internal promotion pipeline and increase the graduate program engineers. They inducted more and more graduate program engineers and that helped them to get the required talent in due course. So, this simple HR analytics who get the promotion of the specialist earlier and they found that people going through the graduate program of the company they get the promotion much earlier in comparison to their peers. So, naturally they went for they put in effort in strengthening the graduating gaming program. Another example comes from a store and this example is given in the competing on talent analytics that is a very famous very successful book written by Devenport and two of his colleagues. It says it describes that some organization can precisely identify the value of 0.1 percent increase in engagement among employee in a particular store. So, they take the example of best buy this is the chain of the retail stores. They looked at the employee engagement results and what was found was very striking and appealing because even the smaller increase in the employee engagement score was associated with increasing income per store. And even the 0.1 percent increase apparently had significant impact on the income per store. So, stores having high engagement scores were much higher in terms of their earning their operating income. So, this was very insightful data and based on this company decided to invest more effort and more focused effort in enhancing employee engagement. So, there is another example where a very successful company by looking at various attributes and doing the systematic calculation based on those attributes over the period able to reduce the attrition very significantly. So, this is an example of this company where they identify different attributes which were able to affect the turnover of the employees. They call it flight risk. Flight risk means the risk of good performing or average performing employee leaving the organization that is not a desirable and desirable turnover that is undesirable employee turnover. And they found that there are multiple factors which affect this team size, team structure, supervisors, performance, length of commute, many factors. So, total 200 attributes were identified when and they had data on all those 200 attributes when the model was run they could very precisely identify who was at risk of leaving the organization. Before that they had to do lot of background work as well because they need to identify what are the factors positively affecting or enhancing the employee turnover or what are the factors which make people stay back in the organization. So, for example, if employees are getting promotion that generally help them to stay back in the organization. If employee as office is shifted away from the hometown or away from the town he is currently residing or away from the place where he is currently residing there is a likelihood that employee may look for switching another organization. So, they looked at these positive and negative factors connected to different attributes. They put those attributes they gave numbers to those attributes run that model and they found that they could very precisely identify the people which are high on the flight risk. And this was rolled out the company reports that 2-3 percent over the the attrition rate came down very significantly and when the attrition rate comes down of any organization it result into major cost saving because new recruitment is a very high cost. So, you have the cost of recruitment itself multiple interviews and engaging external agencies for the recruitment and their induction and for initial few months employee is not very productive. So, all these factors if counted there is a huge cost involved in new recruitment. They were able to reduce the employee turnover that resulted into lowering of the requirement for the new recruitment. And as a result company saved in a year about 800 8000 8 crore 80 lakh dollars to a crore dollar. More number of examples are available on this website you can check out this website aihr.com and its page about HR analytics you can get lot of interesting case examples.