Optimizing Sales through HR Analytics

Confused how to give incentives to the employees?

Not able to decide how to identify the top performing employees?

Are you facing issues related to employee satisfaction in your organization?

Is your organization not giving proper attention to employee needs?

Are you confused about how to improve the business ROI?

Employee segmentation is used in many organizations nowadays. The idea behind this is simple, to segment the employee in such a way to maximize the efficiency of the workforce as well as the reward management. This course utilizes RFM( Recency, Frequency, Monetary) method of employee segmentation. The segmentation method involves the use of the R language for finding the optimal number of clusters which can be created using the data-set. Hierarchical clustering as well as elbow graph plays an important role in finding the optimal number of clusters.

In this course we will take you to the path where you will learn how to choose and decide the right number of clusters along with the ratings from 1 to 5 to find the best employees and the employees who need training.

We tried to create a very simple structure for this course so even if you have no knowledge or very basic knowledge of analytics then even you won't face any problem throughout the course. Let's take a look at the structure. We will start with

1. What is Employee Segmentation?

2. Why do we use employee segmentation?

3. How to apply Employee segmentation to increase the employee segmentation and reward system.

4. Anatomy of Statistical Model to understand the technique of solving any business problem through analytics

5. Learn how to understand Business problem and what are the key factors.

6. How to collect data to solve that problem?

7. Getting the most out of data using basic analytics technique or you can say Exploratory Data analytics.

8. Applying feature engineering techniques to get in depth knowledge hidden inside the data.

9. You will learn what is feature creation, variable transformation, and variable reduction.

10. Learn the hypothesis techniques required for renege analysis

11. How to apply the RFM analysis using R