We’re Already Living in the Future of Talent Analytics

We’re Already Living in the Future of Talent Analytics

Recently at the Harvard Business Review, management professor Thomas H. Davenport asserted that HR “is right up there with the most analytical functions in business—and even a bit ahead of a quantitatively-oriented function like finance.” Davenport backs this claim with findings from a global survey of senior managers, directors, and VPs at large companies by Oracle, on which he collaborated. The survey found that many HR leaders are well-versed in using data and predictive analytics to make talent management decisions:

  • 51% of HR respondents said that they could perform predictive or prescriptive analytics, whereas only 37% of Finance respondents could undertake these more advanced forms of analytics.
  • 89% agreed or agreed strongly that “My HR function is highly skilled at using data to determine future workforce plans currently (e.g. talent needed),” and only 1% disagreed.
  • 94% agreed that “We are able to predict the likelihood of turnover in critical roles with a high degree of confidence currently.”
  • 94% also agreed that, “We have accurate, real-time insight into our employees’ career development goals currently.”
  • When asked “Which of the following analytics are you using?” “artificial intelligence” received the highest response, with 31%. When asked for further detail on how respondents were using AI, the most common responses were “identifying at-risk talent through attrition modeling,” “predicting high-performing recruits,” and “sourcing best-fit candidates with resume analysis.”
  • These findings suggest that the analytics transformation in HR is farther along than you might have thought, with the caveat that the survey respondents were from companies with $100 million in revenue or more, and are thus more likely to have the capacity to deploy new techniques and technologies that may be out of reach for smaller organizations. It should come as no surprise that more and more companies are adopting AI and analytics into their HR functions; what’s new in this survey data is that HR functions are becoming increasingly confident in the maturity and capability of their analytics programs.

    In terms of where companies are deploying talent analytics, Oracle’s findings track with what we have seen elsewhere: The lowest-hanging fruit is in predicting turnover, while there’s also a lot of promise in AI-powered recruiting, predicting performance, and career pathing. The focus on attrition makes sense, as employees who quit often time that decision to leave around predictable life and career events and drop lots of hints about their plans beforehand.

    If you can use data to detect these warning signs and head off unwanted departures, that can save your organization considerable amounts of money. IBM CEO Ginni Rometty made headlines earlier this month when she told attendees at CNBC’s @Work Talent + HR Summit that IBM’s AI technology was able to predict which workers were planning to quit with 95 percent accuracy:

    IBM HR has a patent for its “predictive attrition program” which was developed with Watson to predict employee flight risk and prescribe actions for managers to engage employees. Rometty would not explain “the secret sauce” that allowed the AI to work so effectively in identifying workers about to jump (officially, IBM said the predictions are now in the 95 percent accuracy “range”). Rometty would only say that its success comes through analyzing many data points.

    “It took time to convince company management it was accurate,” Rometty said, but the AI has so far saved IBM nearly $300 million in retention costs, she claimed.

    But predicting turnover with enough accuracy to add value may not require IBM-level AI capabilities. A new study from Peakon finds that employees begin showing clear signs of wanting to quit a full nine months before they pull the trigger on their resignation. A big-data study drawn from over 32 million employee survey responses in 125 countries, the Peakon report points to several key indicators of attrition that show up months in advance: declining engagement and loyalty, as well as dissatisfaction based on unchallenging work, an inability to discuss pay, an unsupportive manager, and the lack of a clear path to advancement in the organization.

    In a recent interview with David McCann at CFO, data scientist Jon Christiansen notes that it’s much easier to predict who will stay than who will leave, but highlights a few indicators that consistently point toward a greater likelihood that an employee will quit, such as whether the employee feels that their performance is evaluated fairly or that they have control over their workday. Other signs include an employee avoiding conflict, siloing themselves, focusing excessively on rewards over the common goal of the organization, and facing either too much or too little pressure at work.

    The advantage for a company like IBM, which continues to invest heavily in AI, is that it can delegate the detection of these patterns to an algorithm. Predicting quits was the first area the tech giant’s HR function focused on when deploying AI, IBM’s chief human resources officer Diane Gherson explained to Jena McGregor at the Washington Post:

    IBM had already been using algorithms and testing hypotheses about who would leave and why. Simple factors, such as the length of an employee’s commute, were helpful but only so telling. “You can’t possibly come up with every case,” Gherson said. “The value you get from AI is it doesn’t rely on hypotheses being developed in advance; it actually finds the patterns.”

    For instance, the system spotted one software engineer who hadn’t been promoted at the same rate as three female peers who all came from the same top university computer science program. The women had all been at IBM for four years but worked in different parts of the sprawling company. While her manager didn’t know she was comparing herself to these women, the engineer was all too aware her former classmates had been promoted and she hadn’t, Gherson said. After the risk was flagged, she was given more mentoring and stretch assignments, and she remains at IBM.

    IBM is also using its Watson AI for other talent-related purposes, such as learning and development or career pathing, Carrie Altieri, IBM’s vice president of communications for people and culture, noted in a recent interview with Riia O’Donnell at HR Dive:

    AI has been a driving force of innovation for IBM’s HR team. Cognitive talent alerts mine for patterns; it searches for employees who’ve been in a job longer than usual (which could signal flight risk) and can determine whether they need more training to move up. …

    AI also can personalize learning and development for each job role and lead the way in making learning a central aspect of a company’s culture. Altieri said that more than 45,000 learners are visiting IBM’s learning platform every day and 98% of employees access it each quarter. While the company requires 40 hours of learning per year, staff average around 50 hours, regardless of tenure. Learning is a huge part of the culture at IBM, she explained, and the new system gives managers the tools to have more intentional discussions with staff.

    And like other tech companies experimenting with these technologies, IBM is not only deploying its AI capabilities internally, but also selling them as a service to other organizations. Last November, the company announced the launch of IBM Talent & Transformation, a new business venture offering AI skills training in addition to services that “harness the power of AI personalization to guide employees in developing skills and pursuing opportunities to grow within the company.”

    A Ghost in the Pipeline: What to Do About Disappearing Candidates

    A Ghost in the Pipeline: What to Do About Disappearing Candidates

    In recent months, many employers have been noticing a trend of candidates and employees “ghosting” them — a term borrowed from online dating that refers to someone dropping out of contact without so much as a goodbye. Recruiters are seeing candidates make it halfway through the hiring process, then simply stop responding to phone calls, text messages, or emails. Chip Cutter, then a managing editor at LinkedIn, was among the first to spot the trend last June:

    Where once it was companies ignoring job applicants or snubbing candidates after interviews, the world has flipped. Candidates agree to job interviews and fail to show up, never saying more. Some accept jobs, only to not appear for the first day of work, no reason given, of course. Instead of formally quitting, enduring a potentially awkward conversation with a manager, some employees leave and never return. Bosses realize they’ve quit only after a series of unsuccessful attempts to reach them. The hiring process begins anew. …

    Some of the behavior may stem not from malice, but inexperience. Professionals who entered the workforce a decade ago, during the height of the Great Recession, have never encountered a job market this strong. The unemployment rate is at an 18-year low. More open jobs exist than unemployed workers, the first time that’s happened since the Labor Dept. began keeping such records in 2000. The rate of professionals quitting their jobs hit a record level in March; among those who left their companies, almost two thirds voluntarily quit. Presented with multiple opportunities, professionals face a task some have rarely practiced: saying no to jobs.

    It’s not only candidates, either; in December, the Washington Post reported that more employees were also “ghosting” their employers, walking out of work one day and not showing up again, with no notice or explanation:

    Read more

    The Business Lessons in Taco Bell’s Turnover Equation

    The Business Lessons in Taco Bell’s Turnover Equation

    In a session at last week’s WorldatWork Total Rewards Conference and Exposition, Taco Bell Vice President of People and Experience Bjord Erland discussed how the fast food chain has handled turnover—a major challenge in its sector—in recent years. At HRE Daily, David Shadovitz passes along some insights from Erland’s talk:

    Leadership was hearing that pay was a major reason people were leaving. But in order to come up with the right game plan, HR knew it needed more data. So it brought in global consultancy Mercer to better understand the key drivers behind the high turnover and identify ways to address it. When it looked at why workers stuck around, Taco Bell, a unit of Yum! Brands, found that a flexible work environment and strong culture were major drivers. As to why people were leaving, factors such as a high level of stress, lack of training and better opportunities elsewhere emerged as a big contributors. …

    Well, the big “Aha!” for Taco Bell was learning that earnings were far more important to workers than their rate of pay. Were they working enough hours, including overtime, to bring home a bigger paycheck? (Erland noted that Taco Bell’s pay was competitive with others in the industry.) In light of these findings, Erland said, the company began to increase its use of “slack hours” to increase the amount of employee take home pay. “Turnover improved when employees were able to bring home more earnings,” he said.

    Read more

    When Two Weeks’ Notice Isn’t Enough

    When Two Weeks’ Notice Isn’t Enough

    For organizations that derive most of their business value from their talent, the departure of a single employee can be very costly, even more so if it comes suddenly or unexpectedly. In this talent-focused business environment, the traditional practice of giving two weeks’ notice of intent to quit can leave employers with too little time to manage and prepare for an employee’s departure or begin the search for a replacement. Talent Economy associate editor Lauren Dixon highlights the different course being charted by the Chicago-based employee communication software company Jellyvision:

    Jellyvision uses what it calls a “graceful leaving” policy to help both the organization prepare for open positions, as well as departing employees to have a support system for their desire to move on. When an employee begins to job hunt, considers applying to school, thinks about moving, etc., the company’s policy allows them to set up a conversation with their manager about the idea and to explore potential next steps. Managers can then provide contacts for networking and accommodate interview times — all while the employee does their work as usual. …

    This policy also allows managers to better understand what the employee wants from the job, and the two can potentially make that change internally. For example, if an employee considers leaving for a managerial role, they could explore that opportunity within Jellyvision, thus retaining the worker.

    Dixon hears from several experts, including our own Brian Kropp, who agree that approaches like Jellyvision’s “graceful leaving” are preferable to giving employees the cold shoulder once they announce their plans to leave. Letting employees know it’s OK to leave makes them more likely to give ample notice and even participate in training their replacement when they do, and increases the likelihood that they will return to your organization later on in their careers.

    Read more

    Survey: Promise of Talent Analytics Remains Unrealized

    Survey: Promise of Talent Analytics Remains Unrealized

    A recent survey from the New Talent Management Network highlights the difficulty many employers are having when it comes to implementing an effective talent analytics program. The survey found that 85 percent of organizations were already conducting people analytics, while of those who aren’t, 69 percent plan to start in the next 12 months, meaning that over 95 percent of organizations are expected to use some type of people analytics in 2017.

    Unfortunately, the authors write, most organizations haven’t gotten very far beyond the stage of establishing an analytics function, most are using relatively unsophisticated tools, and most are only collecting and analyzing basic data on metrics like turnover, time to hire, and engagement. In summary, their top-line findings were as follows:

    Read more

    The More CEOs Matter, the More Often They Get Fired

    The More CEOs Matter, the More Often They Get Fired

    The New Yorker’s James Surowiecki observes that average CEO tenure has fallen in recent decades, driven to a significant extent by boards’ greater willingness to fire CEOs over poor performance. The “embattled CEO,” he writes, appears to have supplanted the “imperial CEO”of yore:

    The breakdown of the old order began more than thirty years ago, but things have accelerated since the turn of the century. The Sarbanes-Oxley Act, passed in 2002, required greater disclosure to investors, and increased the independence of corporate boards. “In the old days, boards were often loyal to the C.E.O.,” Charles Elson, a corporate-governance expert at the University of Delaware, told me. “Today, they’re more loyal to the company.” The rise of activist investors—who campaign aggressively for change when they’re not satisfied with performance—has exacerbated the trend. One study found that when activist investors succeed in winning seats on the board of directors the probability that the C.E.O. will be gone within a year doubles. …

    The predicament of modern C.E.O.s may seem surprising, given their prominence and lavish compensation. Top executives everywhere are paid more than they used to be, and the U.S. has led the way; American C.E.O.s earn, on average, two to four times as much as European ones and five times as much as Japanese ones. Yet it’s precisely these factors that make C.E.O.s vulnerable, because the expectations for their performance are higher.

    Surowiecki also notes that in the age of social media and the insta-scandal, a CEO can create a PR nightmare for their company with just a few poorly chosen words. He points to recent examples of corporate heads rolling over ill-considered public statements, such as the fall of Saatchi & Saatchi executive chairman Kevin Roberts in late July. Directors may also be more sensitive to the risks posed by scandal-prone CEOs in light of recent research finding that such scandals can have an impact on an organization’s reputation long after the offending executive has been shown the door.

    Read more

    ‘Til Turnover Do Us Part

    ‘Til Turnover Do Us Part

    At the Conversation, Irit Alony discusses a study she conducted in which she applied a method commonly used to predict divorce to predict employee turnover instead. As it turns out, she writes, unhappy employees end up quitting their jobs for reasons strikingly similar to why unhappy marriages fall apart:

    Participants in this turnover study were first interviewed, and their their attitudes (like job satisfaction, commitment, intentions to quit, engagement, and burnout) were measured. A year later, their attitudes were measured again, and another year after that, the study looked at who left and who stayed. The study found that employees who left their jobs didn’t use the following coping mechanisms: they didn’t balance the good with the bad, they didn’t genuinely accept that bad things are just part of life, they didn’t avoid lengthy discussions of the negatives and they didn’t express hope. …

    The predictions of employees who leave organisations in this research are very similar to predictors of divorce. Past research has shown that when there are forms of negativity in a marriage, like disappointment, withdrawal, hostility, or contempt, you know the couple is at a high risk of divorce. Couples who not only accept their struggles but even celebrate them remain happily married, and so do couples who successfully avoid conflict.

    Read more