Talent Retention: how Data and Algorithms can help you with the biggest challenge of HR
The technological disruption our society experiences impacts many areas of our lives, from our daily habits to the way we travel or communicate ourselves. At work, the behavior of new generations towards employment is perhaps one of the clearest examples of this impact.
While “baby boomers” are coming to retirement with 20+ years of experience in the same company, with which they have established almost familiar relationship, younger people, particularly digital profiles, take employment as a “consumption” relationship, more short-term oriented. A job is good as long as they continue to learn, grow and have fun. After 2/3 years in the same position, millennials seek alternatives to continue growing and accumulating experiences, no matter how well they have been in the company at that time.
It is not surprising that, despite the great efforts of companies to retain talent, the turnover of qualified profiles does not stop growing worldwide. In Spain it is 21% per year, according to the Adecco’s “Infoempleo” Report.
That is, one in five employees will not end the year in their current company, or that Spaniards change jobs once every 5 years. If we go to young, talented and digital profiles, it is rare to find cases of fidelity for more than 2 or 3 years.
The cost of this turnover is huge for companies. On average, replacing an employee costs 50% of their annual salary (it includes both direct costs of headhunting, employer brand, selection, legal, etc., as well as indirect costs of lowering performance due to loss of experience in the position). Therefore, increasing the turnover from 20% to 30% (or reducing the average retention of your employees from 5 to around 3 years) means increasing the cost of the salary mass by 5%.
If you can avoid 10pp of higher turnover, you can hire 5% more employees. Imagine what you could do with 5% more employees.
Although the tendency to reduce the time of employment (i.e. to increase turnover) is structural and cannot be fought, you can limit its impact in your company. Although it may sound obvious, the secret lies on whether your hires are truly good fits with your company or not. It is essential to hire profiles that not only meet the “hard” specifications (i.e. have the experiences and qualifications required by the position), but that fit well with the company's culture. Digital profiles value business culture and are not going to perform or stay in a company where they do not fit in. This is easy to ensure in small businesses with a good interview process.
However, if you are hiring more than 10 people every year, great cultural and behavioral fit interview assessments are difficult to scale, as you need exponentially more candidates to find the right ones, more interviewers, more assessments and more trainers, which inevitably ends up in more hiring mistakes. It is here where you should leverage new technologies and candidate data to ensure a “good fit”.
Solutions such as personality tests, gaming or surveys are useful to the extent that you compare the responses of candidates with people who are already in your company. If you can measure how high performers in your company respond to cultural, personality or IQ assessment questions, and statistically infer traits in those people, then you can basically narrow down your funnel to people you know will be a great fit in the company, ensuring higher performance and lower turnover.
Many companies have started using data-science based solutions to match the right person to the right job. One of the most successful solutions out there is pymetrics, which offers a platform with neuroscience games to first infer traits from your company top performers and then assess which candidates fit better with those traits. Their goal is to improve candidate experience compared to traditional IQ tests while improving candidates experience and reducing bias. Similar solutions can be found in HireVue or YobsTech, a platform which enables you to assess personality traits and some other soft-skills and values directly from video interviews.
At Nova, we work with proprietary algorithms to help us with company to candidate matching, enabling us to find members who fit with our clients not only based on background (i.e. studies, professional experience), but also based on their culture (values, reputation, compensation, etc.), preferences (size, location, industry, etc.) and skills (hard, soft, languages).
Our science shows a positive correlation between our matching and the level of performance of employees. Also, higher match is correlated with lower turnover, the biggest challenge of HR in the era of digital disruption.
