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How Big Data Slashed Raises for Most Employees

The guaranteed annual raise is increasingly a thing of the past—one reason wages have stagnated overall in recent decades.  For the decades before 2000, salaries went up about 4.1 percent a year, according to data by Aon Hewitt.  But in the last four years, even as companies have recovered from the financial crisis, annual raises have averaged only about 2.8 percent. 

Instead of permanent raises, the best many employees can hope for are bonuses and other cash awards that are increasingly replacing permanent increases in employees’ base pay.   And the kicker is that companies are increasingly using “big data” analysis to dole those bonuses out selectively only to the employees most likely to leave the firm, while slashing additional compensation to the rest of their workforce.

A whole host of companies now scour employee’s personal data, social media and every other source of information on individuals to create comprehensive profiles of each worker.  “[Data] has helped us determine, with ever-greater accuracy, an employee’s probability of quitting,” related Will Wold, Credit Suisse’ Global Head of Talent Acquisition & Development, in an interview. Or as Google’s President of People Operations told the Harvard Business Review, big data lets them “get inside people’s heads even before they know they might leave.”

Knowing what employees are likely to leave is critical in helping companies deploy efforts to retain them, but knowing which employees are NOT likely to leave is just as important, since it lets companies save money by slashing the annual salary bumps for that latter group.   If employees are too timid or too in debt to risk a job switch, companies can use that information to reduce or even eliminate annual raises for them.

McKinsey and Company lays out the logic in a report, Retaining Employees in a Times of Change, where they advocate that companies not waste pay increases and bonuses on employees “who would have stayed put anyway.”  They detail how different companies use data analysis to identify the employees at risk of leaving the company in order to offer them a “mix of financial and nonfinancial incentives tailored to their aspirations and concerns.”   In McKinsey’s analysis, such incentives need only be offered to 5 to 10 percent of the workforce; the rest can either be allowed to leave or those employees are unlikely to depart even if offered no raise. 

McKinsey argues that good use of data analysis will allow companies to avoid hefty pay hikes even for the employees they want to make sure stay.  By getting inside employees’ heads, they can offer training and other incentives promising longer term prospects of career advancement in lieu of immediate pay hikes.  Some of these employees may indeed end up with higher pay down the line to make up for the deferred pay raises but since not everyone can advance to higher positions, many or even most of the targeted employees may end up deferring pay for empty promises of longer-term payoffs.

McKinsey offers this bottom-line projection of savings using the case study of a European industrial company that found when they applied such an approach, they were able to slash their annual budget for compensation increases by 75 percent compared to their previous cash-based approach.

McKinsey is not alone in promoting this new gospel of selective raises.   Consulting firm Deloitte argued in The Dataficiation of HR that, similarly to McKinsey, most employees will stay put in a firm with much lower raises than traditional industry standards, so compensation packages should be focused mostly on the smaller set of high performers most likely to move to another firm.  Ironically, the biggest obstacle for the consulting firm in implementing their recommendations were top managers who resisted eliminating across-the-board pay increases. But, over “many months…over time they realized that data could make them smarter in their decisions about who to hire and promote.”

Datafication of the Human Resources department means that employers know not just who are the most valuable workers in their employ but which employees know their value.  For employees that know their value and are most likely to leave, companies can up incentives to keep them, although psychological profiling can ideally substitute cheaper non-financial incentives for cash in many cases. 

For the larger group of employees who don’t know their value or are too timid to demand compensation in line with that value, datafication has allowed companies to slash compensation and made the automatic annual salary increase a distant memory of a past era while deepening the wage stagnation so many families have experienced.

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