Aetna Gen Y Retention

Aetna: Generation Y Retention

Business Issue

Like many other companies, Aetna is having trouble retaining Generation Y talent. In order to enhance the future leadership of the company, it is important that Aetna learns to better understand this generation and what it takes to attract and retain them within the company. A quick look at the Aetna Careers home page shows individuals working alone, while Generation Y type organizations such as Facebook and Google emphasize Generation Y working together in a relaxed environment (Appendix A). These messages are important to potential employees and speak volumes to the culture of the company, Aetna needs to include images that will resonate with Generation Y characteristics.

Due to the environment that Generation Y grew up in, this group is very different from the generations that came before them. It is important to understand these characteristics and be able to incorporate Generation Y strengths into Aetna's culture.

Technologically Savvy and Dependent

Generation Y grew up with technology embedded in every aspect of their lives. Because of this, they have developed skills that allow them to work and communicate more effectively and efficiently than employees not as technologically adept, this causes them to expect results to come much quicker.

Value Work/Life Balance

Generation Y places a lot of emphasis on having a work/life balance. They value their free time and flexibility because they understand that other aspects of life are just as important to their overall happiness. They are willing to work longer days if they can work from home or enjoy 3 day weekends.

Achievement-Oriented and Attention-Craving

Generation Y craves constant feedback and guidance and because of this, they seek frequent evaluations and quick promotions. Aetna's current evaluation and promotion structure will need to be evaluated in terms of how feedback is offered to employees and if this can be used in order to develop stronger relationships with Generation Y.

Data Collection Issues and Challenges

As a team, we split up the data. Two team members took the combined data set supplied by Aetna, where one looked at the Employee master file, and the other looked at the TM data. Then we combined the rehire data supplied by Aetna with the termination data. When we looked at these two data sets not all of the rehires showed up on the termination data set. In addition, the amount of missing data of these sets for the generation, the topic of interest, was significant (1261).

Data preprocessing

For the combined rehire and termination data, we deleted rows that were duplicates such as ethnicity, sex, and generation in the two data sets. Additionally, we deleted rows that didn't provide value to the data set. For instance, there was a date column that we deleted because it was always the end of a given year and we could obtain that data elsewhere in the data set. We also removed the rehire City and State, since this data set was similar to the original hiring location. The two data sets were not identical, but the impact on our project was thought to be minimal. The data was recoded to correct spellings and combine terms that were similar. Since there were many job titles, the jobs were re-coded by department to understand more of a departmental impact versus a specific job title. The last step we took with the data step before beginning to run models was to break it up into 3 different sets from which to work. We used a training set, test set, and validation set.

Data visualization and pattern discovery

General demographic distributions from the data set show that there are approximately 3 times more females working at Aetna than there are males. The predominant ethnic group at the company is White, followed by Black and Hispanics. The data set shows that approximately 46% of employees are in Generation X, followed by 38% Baby Boomers, 15% Generation Y, and the remainder in the Silent Generation. This information is broken down even further into Age Bands, with 33% of employees between the ages of 40-49, followed by ages 30-39 with 26%, ages 50-59 with 24%, and the remainder made up of similar proportions of age 60+ and age 20-29 employees.

Cluster analysis broke the employees down into the following five groups. Cluster 1 was made up of employees about 45 years of age that have been with Aetna for 12 years and have a salary between $100,000-110,000. This group would be considered successful Generation X employees with high-level jobs that have been dedicated to climbing the career ladder. Cluster 2 is made up of employees approximately 28 years in age with 7 years of service with the company and a salary of approximately $40,000. This group consists of Generation Y employees who started working with Aetna right out of college and have not had much advancement in their careers at this point, we could consider them to be less motivated. Cluster 3 is made up of employees around 47 years old with 12 years of service at Aetna. Their average salary is between $45,000-50,000. This group of Generation X employees has moved around in their career, but seems to be settling with mid-level jobs at Aetna. Cluster 4 consists of Generation Y employees age 33 with about 8 years of service at Aetna. These employees have had advancement opportunities and are earning about $70,000 a year, they are motivated to excel in their careers. The final cluster that was developed consists of Generation X and Baby Boomers with about 30 years of service at Aetna. This group has been dedicated to Aetna for most of their career and is earning a salary of about $70,000 a year. This group seems to be satisfied with their current positions.

An overall distribution of why Generation Y employees left Aetna shows that about 30% left for personal reasons, 12% left for advancement opportunities, and approximately 8% were entering a new field. In order to get a clearer picture of why employees are leaving, past employee evaluation information was incorporated into the analysis.

The tree map below shows the data set grouped by Generation and again by Potential ratings. Potential ratings tell an employee how suited they are to be in a senior position in the company in 1 year. These groups were then sized by their retention risk factor. This image shows that those employees with lower potential ratings (Well-Positioned and Medium) have a greater retention risk than those employees with High potential ratings. This means that when an employee is told they do not have a good chance of moving

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