SNA to tackle churn rate in Information Technology Industry

Problem:

The companies in technology industry face a very high attrition rate among their employees for various reasons. we can use SNA to analyze the employee loyalty towards the company and also helps us to predict their career motives and expectations, which helps us to build a strong loyal community or the organizations can have a head start to replace/withheld the employees who chose to resign their jobs.

The attrition rate in technology industries can become a serious challenge to companies since the industry is flooded with constant change and innovation, since the employees in the tech industry passively look for constant new career opportunities and also would like to explore the new technologies in the market. Few Other reasons for higher attrition rate would be, higher pay and benefits, imbalance work life balance at the current employer, ruptured relationships with the team members, less challenging work with regards to employee’s skills etc.

The consequences of High churn rate in technology companies:

1. The cost of hiring a new employee
2. Redistribution of work load
3. Damage to brand image of organization
4. Loss of clients (in few instances)
5. Issues with the quality and productivity of the projects
6. Delay in the project deliverable.

To avoid all the consequences or at least to reduce the damage of the churn rates in the organizations, we can use social network analysis to predict the employee behavior and satisfaction levels in organizations.

With the help of SNA, we can try to find and analyze by using the following Questionnaire:

1. Please indicate how often you have turned to this person for information or advice on work-related topics in the past three months.      
2. How satisfied are you with the company’s work culture and work life balance?
3. How would you rate the organization helps to achieve your career goals?
4. How satisfied with the technology you are working on?
5. Given a chance how much likely you are willing to change the technology or teams you are working with?
6. How satisfied with the compensation and benefits provided by the organization?
7. How much likely would you prefer going for higher studies?
8. How likely would you recommend the company to work to their friends and family?

Additional questions for team leader’s/Project managers:

1. How satisfied are you with the team they are working with?
2. Given a chance how much likely are you willing to change a team member or team?

Adding to the above questionnaire we also include, gender, age, Marital status, Location, Travel Time to Work place.

Post having all the data with respect to social network and the attribute values, we can dichotomize the data with often and very often frequency levels and calculate the centrality measures using the social Network analysis tools and we can come up with hypothesis that helps the company to reduce the churn rate or predict the employees who are more likely to leave the company for any of the various reasons stated above.

Few key measures to look at are:

Degree: Since this measure tells us the people who are most reached by the employees or the ones who reaches to other employees in the organization. This measure can help us to identify the most active employees in the network with substantial knowledge on the projects they work on.

InEigenvector: This measure gives us the information with respect to the best-connected employees in the network, which helps us to focus on the employee to understand the behavior or opinions of the network.

Betweenness: This centrality measure provides us the important nodes in the network that gives the shortest path to reach all other nodes in the network.

Additionally, we can also use subgroups and cliques to further analyze the network, which helps us to form a hypothesis, since social network analysis tools are diagnostic tools.

Dharmaraj Aravind Raj,

MIB, Hult International Business School.