Combating the attrition issues by answering how what, and why questions of your organization and leveraging data analytics is now the smart thing to do!
There are several examples that show that collection and analysis of data have been a prominent contributor to a number of problem-solving activities and decisions, not just today, but even the events that go way back. Data Analytics is the magic potion for almost every industry and organization that believe in the evident power of technical prediction and analysis of problems as well as system-generated solutions and solving models.
HR Analytics, Workforce Analytics, and People Analytics have proven their worth by proactively predicting hiring actions, dealing with employee engagements, aiding the employee experience processes, and enhancing the career development strategies for several companies. With firms embracing the leveling up of technology, Data Analytics is gaining prominence in reducing the attrition rate of the company by reaching the core of the problem and then reforming processes to boost effective retention.
Let’s start with the basics.
What is Data Analytics?
According to its definition, Data Analytics is the process of examining and analyzing data sets to draw out patterns, identify trends, and extract deep, meaningful insights that can be used to solve problems ranging in different levels of complexity.
Businesses generally use data analytics to understand consumer behaviors, evaluate the competition, evaluate risks, and manage spendings.
Organizations are deploying data analytics and resorting to data-driven strategies in order to reduce the employee turnover and attrition rates.
Moving to the question of the hour, how can data analytics help with employee attrition and contribute to decreasing the same?
Let’s dice this question into 3 smaller phases that in totality reveal the role played by data analytics in employee attrition.
Phase 1 – The Whats?
Under this space, all the whats will be answered. Questions such as –
- What is the current attrition rate of your company?
- What is the present retention rate of your organization?
- What is your top performing retention strategy?
- What is your current retention approach and method?
And most importantly, what is the attrition rate that you are trying to reach? (E.g., From 20% to 10%)
These are the type of questions that will need you to carry out a descriptive analysis of the human resource of your organization in order to form the basis of your Data Analytics for reducing attrition. This foundation is what will help you in forming the models that do not have multicollinearity in their features and result in the formation of rich preprocessed data.
The thing with data analytics is that the results are always going to be as good as the quality of data input. And a thorough organizational evaluation to feed the data models will give outputs that are accurate and insightful.
So, ensure that your whats are answered with utmost precision.
Phase 2 – The Whys?
The why questions will be responsible for using your historic data for predictive analysis of future scenarios or events.
- Why are the employees leaving?
- Why is the baseline retention model not working up to the mark?
- Why is there a correlation between a specific feature and employee attrition?
- Why are employees staying with your company?
The answer to these questions will generally be delivered to you by your data analytics solution.
Data Analytics will use your descriptive analysis and run an exploratory analysis to display patterns that are not visible to the naked eye.
The results can then be input to derive a predictive analysis of the likelihood of the occurrence of a scenario.
For example – Your exploratory research indicates that the reason for your employees staying in the medical benefits, and the reason for them leaving is the extensive nature of the work, with salary scoring low on the correlation index.
So, this debunks your perception that an increase in the pay will reduce the attrition. And you can form a retention strategy that involves decluttering the workflows and emphasizing the medical benefits.
These results can be fed for predictive analytics that uses deep learning and machine learning to look at the possibility of a reduction in attrition if there is an increase in the facilitation of wellness benefits vs the possibility of an increase in retention if workflows are simplified.
Phase 3 – The Hows?
This phase has more to do with you as an organization and less to do with the algorithms of data analytics. How you may ask? Well, let’s take a look at the questions.
- How much is the cost of replacing one employee at every level of the hierarchy?
- How much is the workforce budget?
- How much of it are you willing to utilize for combating attrition?
- How is the current strategy working as compared to the previous one?
- How else can you leverage data analytics for retaining your employees?
- And, how do you plan to structure your organization in the near future? (Expansion, Growth, Diversion of verticals, vacating job roles, etc)
Answering these questions will enable you to understand the present as well as the future landscape and exposure to your analytical and talent management models. You can compare different models and reconstruct the ones that work best for you by integrating different attributes that can lower the attrition rates. The hows will also equip you to think long-term and make hiring and developing decisions accordingly.
For example – You, a hardware selling firm, are planning to diverge into delivering software in the next two years. So, you can use this insight as an input to data analytics to identify how many profiles will open up and who all (from your current workforce) can be developed to fit into any of the vacant roles.
Businesses are always looking at climbing the success ladder at the least possible cost and with the most efficient workforce. Attrition of quality talent can not only be heavy on your pockets but also lead to a downfall. Data Analytics is resulting in making organizational improvements when it comes to decision making as well as working towards developing models that can keep the attrition as minimal as possible.
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ABOUT THE AUTHOR
Tanvi Tirthani is a content writer and strategist with a special foray into technology. She has been a keen researcher in the tech domain and is responsible for strategizing the social media scripts to optimize the collateral creation process.