The Future of Predictive Analytics — the Next Generation of Talent Metrics to Consider (Part 2 of 2)

A comprehensive list of future predictive talent metrics

In last week’s part one of this article that was published on March 9, 2015, I highlighted the fact that the majority of current predictive metric efforts have focused on only a handful of basic metrics. I next provided a list of the top 18 metrics that should be developed during the second-generation of predictive metrics. This final part one covers the future predictive metrics that should be developed during the third generation.

The Third Generation Of Predictive Analytics

If predictive metrics in talent management follow the same path that occurred in supply chains and in professional baseball, we can expect a wide array of sophisticated public and proprietary predictive metrics to be developed over the next five years. Obviously it’s hard this far out to precisely predict the exact metrics that will be developed during this third generation. But I still have been able to compile a list of more than 25 possible advanced predictive analytics that would add tremendous value after being developed. Some of the metrics that are outlined in this section may even prove valuable enough to be developed earlier during the second-generation phase. The metrics in this final section are categorized into the different functional areas of talent management. During this third generation expect that modeling capabilities will become commonplace and if/then scenario planning will allow managers to pretest their talent decisions. You should also anticipate the extended use of heuristics to identify similar problem areas and to learn from data and improve.

Recruiting-related Third Generation Predictive Metrics

The recruiting function currently has the best metrics in talent management, but there’s always room for growth into these new areas.

  • Identifying factors that predict if a recruiting prospect is about to quit — some vendors are already using public and social media information to identify which employees at their own firm are likely to quit. Eventually the same approach will be adapted so that recruiters can identify which recruiting targets may soon be receptive to a recruiting pitch.
  • Projecting new effective recruiting sources — the effectiveness of recruiting sources is continually changing. So predicting where and when source effectiveness will shift will allow recruiters to use the best new sources, which will directly improve their quality of hire.
  • Predicting which employees are most likely to make top referrals — because of the many advantages of employee referrals, a metric is needed that effectively identifies which employees are most likely to be able to make quality employee referrals for each job family.
  • A metric that predicts the likely return of boomerang former employees — rehiring top performers who left your organization has a high ROI because they already fit your organization. As a result, a metric that predicts when the most desirable former employees are most likely to be receptive to an offer to return will result in many quality hires.
  • Forecasting changing candidate expectations — in a volatile world, candidate expectations are continually changing. As a result, a metric that would predict when and how the expectations of our target candidates will shift would allow a firm to change their offerings to recruits to better match those changing needs.
  • A metric that predicts new-hire failures  predicting which new hires are likely to be failures on the job within their first six months can allow recruiters to proactively begin looking for a replacement before the termination date of the employee.
  • Forecasting future employer brand strength — this metric will predict when and why our employer brand strength will increase or decrease over time, compared to others. Projecting your future brand strength is important because employer brand strength is the No. 1 factor in attracting top talent.
  • Identify future corporate acqui-hire targets — if your firm purchases firms for talent, develop a metric that identifies which talent-rich firms will be a good match for purchase or a merger.

Productivity-related Third-generation Predictive Analytics

Increasing workforce productivity is by far the most important talent area that is almost completely ignored by both talent leaders and metric vendors.

  • Identify barriers that limit productivity — one of the most effective ways for increasing employee productivity is to identify and then reduce the easily fixable barriers that keep employees from increasing their productivity. As a result, a metric that identified current barriers and predicted upcoming barriers would have a large impact.
  • A metric that predicts upcoming employee burnout or obsolescence — develop a metric for identifying individual employees who are approaching job burnout so that they can be moved or replaced. A related metric can also identify employees who will soon become obsolete but they can’t be retrained and those employees who have reached the top of their career trajectory.
  • A metric that predicts which employees will likely need to be released for cause — this metric pre-identifies the individual employees who are likely to be fired or released for performance issues. A warning alert to the recruiting function can allow replacement recruiting to begin before the problem employee must be terminated.
  • Develop a metric that projects possible future layoffs — predict the jobs or the business units where high labor costs, a surplus of employees, or a reduction in the work load will likely require layoffs. Use data to pre-identify the specific individuals who would likely qualify for an upcoming layoff.

Retention-related Third Generation Predictive Analytics

The issue in talent management that is about to explode and then continue to be an issue for several years is the rapidly rising turnover rate.

  • Identify the employees who will soon become “overdue” — this metric would track and identify when an individual employee will likely become “overdue” on critical factors that may cause them to quit or be less motivated. Those overdue factors might include a raise, a promotion, more training, new equipment, recognition, a job rotation, etc.
  • Develop a metric that predicts which employees will be targeted by external recruiters — in order to be proactive and to allow time to develop a blocking strategy, create a metric that predicts which specific employees are most likely to be targeted by recruiters from your competitor firms.
  • A metric for forecasting the changing turnover causes and actions — even today I recommend using post exit interviews to identify why key individuals quit. Additional tracking and projections can reveal when turnover causes are shifting. A related metric can reveal what specific actions are effective for countering each major cause of turnover.

Leadership-related third-generation predictive analytics

Unfortunately the leadership function has been one of the weakest when it comes to using data and both regular and predictive metrics.

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  • Predict upcoming leadership needs and availability — develop a metric that can predict where and when there will be upcoming internal leadership shortages or surpluses. Also for succession purposes, be able to predict when your current key leaders will likely need to be replaced because of turnover, promotions, retirement, or because their performance has peaked.
  • Identify who are currently ready for leadership positions — use data to accurately identify the current employees who are ready for either leadership development training or an actual leadership position.
  • Identify those who will soon be ready for a promotion — delaying promotions can frustrate workers and increase turnover. Be proactive and develop a predictive metric for identifying who and when top individual employees will be ready for a promotion.

Training and Development Third-generation Predictive Analytics

The ability to learn rapidly has been identified by Google to be the No. 1 critical success factor across all jobs. Develop a predictive metric that can warn leaders long before employee learning issues become critical.

  • A metric that projects the obsolescence of current skill sets — predictive metrics can forecast when currently valuable skills possessed by your employees will become obsolete.
  • A metric that predicts future skill needs — in a rapidly-changing VUCA world, skills that are vital today will rapidly be replaced with new skill sets. Projecting those future skills will allow the training, recruiting, and retention functions to focus on those new skill sets. Forecasting the creation of new or redesigned jobs can also allow the training function time to create new skill development programs prior to when they are needed.
  • A metric for identifying your firm’s learning leaders — because learning and innovation are so critical to organizational success, it’s important to use metrics to identify the employees in the organization that are creating and sharing new and original knowledge and ideas.
  • Identify the factors that increase learning speed – use predictive metrics to identify where and when the overall organizational learning speed is likely to decline. Also use metrics to identify the factors that your best learners successfully use to increase what they learn and their learning speed. 

Compensation and Benefits Third-generation Predictive Analytics

Because under-compensating is so damaging and over-compensating is so expensive, it’s important for this function to shift to a data-driven decision-making approach.

  • Develop a metric to forecast upcoming excess overtime — predictive metrics can forecast when and where in the organization will excessive overtime usage likely occur. This metric can allow talent leaders to more accurately budget for overtime or to proactively reduce the use of overtime. Related metrics can identify the areas in the business where labor costs and staffing levels will soon likely become excessive.
  • Project the obsolescence of benefits — predictive metrics can project when current employee benefits will cease to have a major impact on employee motivation, attraction, and retention.

Miscellaneous Third-generation Predictive Analytics

  • Predict the talent actions of your competitors — in a competitive world, it’s important to anticipate and then counter the major talent actions of your competitors. Tracking software and predictive talent metrics can forecast future proactive competitor actions, as well as their likely reactions to your own firm’s talent management actions.
  • Predict upcoming labor issues — statistics and predictive analytics can predict if, where, and when your organization may experience upcoming labor issues including union organizing, more grievances, and even strikes.
  • Forecast the obsolescence of your organizational structure and building design — the most effective organizational structures eventually become obsolete as a corporation evolves. Talent management leaders should have metrics that enable them to forecast when and why the current organizational structure and org chart will become obsolete. A related metric should predict the likely date when the design of your physical facility becomes so obsolete that it reduces collaboration and productivity.
  • Project upcoming organizational speed issues — in an organization where rapid product development is essential, the metric team must develop a process for forecasting when and in what areas will organizational speed need to be significantly increased.
  • Forecast weak coordination between talent functions — whenever complicated interdependent work is handed-off between different internal functions or teams, the likelihood of an error or a slowdown increases dramatically. HR has a well-deserved reputation for having restrictive functional silos and poor coordination, so it’s important to track current and to project future talent handoff, coordination, and integration problems.
  • Provide SWOT forecasts — external factors that are constantly monitored on the business side also need to be tracked and projected in talent management. So it is important to continually forecast SWOT factors (strengths, weaknesses, opportunities, and threats) that the firm will encounter in the talent management area.

Final Thoughts

The basic goal of predictive analytics is to get everyone in talent management to develop a forward-looking mindset, where everyone is focused on identifying and acting on upcoming problems. Consider the metrics that have been provided here — when acting in unison they could be a type of early warning system that scans the horizon for upcoming talent management problems and opportunities.

Even though the list of metrics that have been provided is long, realize that no individual firm is expected to implement more than a dozen of these forward-looking metrics. The final metric should be selected in conjunction with the COO and CFO, in order to ensure that they predict and forecast in the areas that are likely to have the largest business impact.

Dr. John Sullivan, professor, author, corporate speaker, and advisor, is an internationally known HR thought-leader from the Silicon Valley who specializes in providing bold and high-business-impact talent management solutions.

He’s a prolific author with over 900 articles and 10 books covering all areas of talent management. He has written over a dozen white papers, conducted over 50 webinars, dozens of workshops, and he has been featured in over 35 videos. He is an engaging corporate speaker who has excited audiences at over 300 corporations/ organizations in 30 countries on all six continents. His ideas have appeared in every major business source including the Wall Street Journal, Fortune, BusinessWeek, Fast Company, CFO, Inc., NY Times, SmartMoney, USA Today, HBR, and the Financial Times. In addition, he writes for the WSJ Experts column. He has been interviewed on CNN and the CBS and ABC nightly news, NPR, as well many local TV and radio outlets. Fast Company called him the "Michael Jordan of Hiring," Staffing.org called him “the father of HR metrics,” and SHRM called him “One of the industry's most respected strategists." He was selected among HR’s “Top 10 Leading Thinkers” and he was ranked No. 8 among the top 25 online influencers in talent management. He served as the Chief Talent Officer of Agilent Technologies, the HP spinoff with 43,000 employees, and he was the CEO of the Business Development Center, a minority business consulting firm in Bakersfield, California. He is currently a Professor of Management at San Francisco State (1982 – present). His articles can be found all over the Internet and on his popular website www.drjohnsullivan.com and on www.ere.net. He lives in Pacifica, California.

 

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