Experience or instinct? Let’s bet on predictive analysis

Analytics, machine learning and the key to competitiveness

We are all aware of it: our companies already use it to analyze customer interactions, to prevent systemic failures, and to anticipate with good approximation new trends in so-called emerging markets. But the analytics offered by machine learning can also fulfill a different, more "intimate" function. Help organizations understand if a talent on board is going to leave. When he'd like to. Why. Important clues to motivate and offer the right counterparts (engagement, growth) to stay. Retention, the experts call it. In addition, by analyzing the best internal customer satisfaction mechanisms, machine learning can also act as leverage to make itself more attractive to talent still outside the organization. Attraction, then. Because, today more than ever, having the right talents makes the difference between a company that wins and one that loses.

Once upon a time there was instinct, compensating managers experience when they had to take an important decision about human capital, as to say resources to be proposed for career advancements and those that, instead, would take another route, leaving the organization attracted by more exciting offers. Previous knowledge and emotional factors, therefore, that had no direct link with concrete empiric data. Today we are in a new world, that of information and technology. The one in which experience and instinct have to deal with the complement of precise predictive analysis offered by "workplace analytics". Because numbers, unlike impressions, can be interpreted differently, but not disproved.

Predictive analytics help the company achieve engagement in a more "intelligent" way, because it reduces the risk of turnover which is a definitely expensive practice. Between recruitment, onboarding and training, a new employee costs the organization up to 1.25 times his salary. In the negative, there is also the risk of losing key resources, and with them the relative return on investment. Harvard Business Review and Engage Talent have done research on the subject, showing how big data can act as a trail to indicate the intention of those who are about to leave. Personally relevant events (family, corporate or even natural). Management issues (and anything that demotivates the resource, directly or indirectly). Work environment (in the sense of "job embeddedness", that is, how connected an employee feels to others, and to the community). Market demand (relative to the specific profile of the resource and its employability).

The problem is exacerbated, if we only consider that often the individual leaving of the organization is part of a broader phenomenon, as in the mikado, which causes waves of turnover to occur. From a risk, however, always arises an opportunity. And therefore, the predictive "internal" analysis can also be applied, as far as possible, outside the company, to its market competitors. Because the War for Talents, after all, has never ceased. In this war, big data, analysts and engineers can aspire to the role of strategic bishops, because they oversee the key metrics that govern the most essential conditions underlying the relationship between organization and resource: strong and recognized as worthy of reliability leadership, stability and resilience of the company, growth opportunities and, last but not least, positive working environment.

Five clues referring to the same, fundamental concept: that of satisfaction. That's the key to carving out a way into the jungle of a competitively driven market.