A comprehensive list of current and future predictive talent metrics
The use of predictive analytics is a hot issue and a developing trend in talent management. But unfortunately as a longtime thought leader in the area, most of the current prediction efforts are extremely shallow. And as a result, they will have a minimal impact because they only cover a few basic areas like predicting employee flight risk and identifying the selection factors that predict hiring success. What will eventually be needed is a broader array of second- and third-generation predictive metrics covering many more advanced talent management factors.
If you’re curious about what factors must be measured in the future, here is a comprehensive list of the predictive talent analytics/metrics that should eventually be developed by forward-looking talent leaders.
The First Generation of Predictive Analytics
The best firms like Google and the leading-edge vendors are already providing predictive metrics in these “first generation” areas. Consider the current limited round of predictive metrics as part of a first step toward the inevitability of a completely data-driven talent management function. The data-driven approach is inevitable because every other business function long ago shifted to that type of decision-making, which is one reason why HR is rated by executives as last among all strategic business functions (from Simon Mitchell of DDI). The most common current metrics in talent management include:
- Identifying which key employees will soon be a flight risk — perhaps the easiest of all talent factors to predict, this metric requires that you identify the factors or precursors that forecast that a key employee will soon consider quitting (Google is a benchmark firm). The resulting metric should assign a probability number to each targeted employee covering the odds of them leaving within the next few months.
- Identifying the selection factors that predict on-the-job performance of new hires — a hiring algorithm that can successfully predict the characteristics of applicants and interviewees that will result in successful and higher than average performance on the job. A similar algorithm needs to be developed for college hires (Xerox, Google, and GateGourmet are benchmark firms).
- Forecasting when a change in employee survey scores will begin to impact productivity — this predictive metric determines the correlation between productivity and employee survey scores. It then forecasts when significant change in the scores will positively or negatively impact a team’s productivity and retention.
The Second Generation of Predictive Analytics
Currently few firms (Google is the benchmark firm) are providing metrics in these advanced areas. Although these second-generation predictive analytics are more difficult to develop, it’s hard to argue against the potential business impacts that these predictive metrics will eventually provide.
The calculation and the delivery of metrics will also change during the second generation. Even though most talent metrics are now delivered to HR users, in the future they will be accessed via mobile phone and used directly by operating managers. Data will be gathered using scientific sampling rather than measuring every case or employee. These next generation metrics will be presented in both report and visual trend-line formats. There will also be an alert process to give managers sufficient time to prepare before an upcoming problem or opportunity occurs. And finally this new approach should report the correlation between a manager’s use of predictive talent metrics and their improved business results. The top 18 most-likely second generation predictive metrics are listed below:
Article Continues Below
Driving Better Recruitment Through Exceptional Experiences
In this paper you’ll get insights to:
· What makes a Talent Experience· An outline of the experience touchpoints within the recruiting journey · How to ensure a quality experience for all key recruiting stakeholders
- Projecting the dollars of business impact resulting from talent actions — this most important second-generation metric converts HR results into dollars, because that is the common denominator throughout the corporation for measuring business results. This metric results from a process of identifying and quantifying (in dollars) the revenue impact of each of a firm’s talent programs. Work with the CFO to ensure that the conversion process and the revenue impact dollar estimates are credible.
- A metric revealing the current and projected improvement in revenue per employee — the Holy Grail of talent metrics is a single number that serves as an indicator of both the current and future productivity of the total workforce. That single metric is likely to be projecting the yearly improvement in the “revenue per employee” calculation (which is corporate revenue divided by the number of FTEs). Or alternatively, projecting the improvement in the ratio between corporate revenue and total labor costs. This revenue per employee dollar number has great value because it can easily be compared both year to year and between different companies. (Note: the current revenue per employee for major firms can be found by typing in the stock symbol in the search box on MarketWatch.com).
- An easy-to-compare talent management performance index — outside of HR, indexes are widely used to combine multiple metrics into a single standardized number (e.g. the Dow Jones Average is an index). Having a single talent index will allow executives to easily and quickly compare the talent performance of different business units, teams and managers. The talent index would combine the performance of a unit in each of the key talent areas including recruiting, retention, development, innovation etc. into a single number. The talent index score would be a single number based around an average score of 100, where 80 would reveal talent performance problems and a 120 score would show excellence in that business unit. This single “talent performance index number” would allow talent leaders to easily identify and then focus on problem business units and managers.
- A “WAR” metric that places a numerical replacement cost on individual employees — perhaps the most influential recently developed metric in baseball is the WAR metric (Wins Above Replacement). It projects the likely value of an individual player if they were lost and were then replaced by a readily available minor league player. In talent management, although complicated, a single index called “output value above the average replacement” would make it crystal clear which employees you couldn’t afford to lose (because their replacements would likely underperform them). Calculating and placing a single numerical value on key individual employees who could quit/retire would help leaders also determine an employee’s relative actual value for compensation, retention, and succession planning purposes.
- Predicting upcoming productivity issues — employee productivity is rarely measured and reported in today’s corporations. In the future, an algorithm that statistically determines which factors positively impact employee productivity can add great value. A related algorithm that predicts where and when within the corporation that major individual and team performance/productivity problems will likely arise will also be valuable. Predicting upcoming productivity problems will give leaders sufficient time to develop approaches to mitigate those upcoming problems. Adding a visual trend line showing the trajectory of the productivity curve will make upcoming problems and opportunities easier for managers to spot. A similar algorithm and trendline revealing predicted decreases in innovation will also add value.
- A metric predicting upcoming employee behavioral issues — allowing employee behavioral problems to fester can be expensive. So providing a metric that predicts the specific areas where employee behavioral problems (i.e. excessive absenteeism, excessive sick leave usage, sexual-harassment, engagement, safety, low morale, a high error rate) will likely occur can allow leaders to act proactively. Using data to identify the most effective remedies to these behavioral issues would also add value.
- Plotting the career trajectory of new hires and employees — projecting how high and how fast a new hire is likely to progress upward is critical in a growing organization that needs employees who are capable of moving up fast. A similar career trajectory could also be plotted for current employees for career development and succession planning purposes. Plotting how long a new hire or an employee will likely stay in the organization would also be valuable.
- Identify the factors that impact manager success — having effective managers is essential for productivity, innovation, hiring, and reducing turnover. Rather than using the current unscientific approach, first use multiple regression to identify the factors that top-performing managers have in common. And then use an algorithm with those characteristics (e.g. project oxygen at Google) to guide your selection of new managers. Those predictive factors can also be used to identify and improve weak managers and to identify the ones that should be on the succession plan.
- Identify the factors that identify leaders or leadership capabilities — identifying high-potential leaders among new hires and current employees has frequently failed because the typical approach has not been data-driven (Research by the CEB revealed that “more than two-thirds of companies are misidentifying their high-potential employees”). An algorithm that successfully identifies the characteristics of potential and actual leaders can be used to more accurately screen applicants and interviewees for those with leadership capabilities. A similar algorithm applied to current employees would also be invaluable for accurate succession planning.
- Identify the selection factors that predict an innovator — innovation has a large and growing business impact. As a result, recruiting must become more effective in identifying innovators among those who apply. It must develop a hiring algorithm covering innovation characteristics that can successfully predict which candidates will be successful innovators in key jobs after they are hired.
- A metric predicting an upcoming large volume of position openings — going beyond predicting individual turnover, effective workforce planning requires that you can also predict which jobs will likely have a large number of upcoming openings. Knowing which jobs will soon have many openings because of turnover or growth will allow firms to proactively ramp up training, internal movement, or external hiring before those excessive vacancies have a significant negative business impact.
- A metric predicting upcoming external talent availability and talent opportunities — most firms hire exclusively when they have a current opening, while the best firms proactively “ramp up their hiring” whenever top talent is highly available. As a result, a metric that predicts time periods where there will be a talent surplus in key areas would allow a firm to “load up” on top talent. A similar metric revealing when there will be reduced competition for that talent (because of reduced hiring or a hiring freeze by your competitors) can also allow a firm to hire an exceptionally high volume of high-quality talent.
- Predicting areas where significant internal redeployment will be required — because business needs vary and change throughout a corporation, some business units will need to expand while others will need to shrink. A predictive metric can forecast when and where in the organization there will be a surplus of talent, so that it can be redeployed into areas where it would have a higher impact. Individuals who are soon to become “overdue for internal movement” can also be identified.
- Projecting the appropriate % of contingent workers — in a volatile VUCA world, the ability to rapidly increase talent capabilities or to rapidly reduce labor costs are both critical agility factors. As a result, a metric forecasting the appropriate future contingent labor percentage for that time period and business growth rate can add great value. A related metric predicting when outsourcing work is appropriate would also improve talent agility.
- Predicting the viability of technology substitutes — for years having employees was the only solution that HR had for every “I need work done” problem. But now that hardware, software, and the Internet have dramatically evolved, the time will come when HR must also routinely consider technology substitutes for labor. A metric-driven formula that can successfully determine when technology solutions are a superior substitute for labor will someday become common in HR.
- Forecasting upcoming retirements — unexpected large-scale baby boom retirements may create severe internal talent shortages. An effective predictive metric that can forecast retirement trends and accurately predict when and in which jobs those upcoming retirements will likely occur in will help with recruiting and succession planning.
- A metric predicting when an employee will become underpaid — feeling underpaid is a major cause of both turnover and lower productivity. Proving a correlation between an employee’s “underpaid status” and a decrease in their productivity and retention will be a valuable argument for convincing managers to proactively bump up pay. With such a relationship confirmed, a metric that forecasts into the future precisely when employees in each job family will likely reach “underpaid status” (compared to regional market rates) would allow leaders to act proactively before compensation became an issue. An algorithm that successfully predicts when key individual employees will (without a raise) reach an underpaid status would also add value.
- Identifying diversity roadblocks — as convincing evidence continues to emerge on the positive business impacts of diversity, it will become essential to increase diversity hiring, promotion, and retention. A process that can scientifically identify the barriers that restrict diversity hiring, promotion, and productivity will be needed. An algorithm will also need to be developed that determines the optimal percentage of diversity in a team and in which specific jobs does having the target diversity percentage have the largest business impact.
I am excited about all of the current attention being given to predictive metrics, but at the same time, I am concerned because most talent management leaders seem to be overly satisfied to be simply “working on” or “by having a few” predictive metrics. Most in the field who I encounter have simply not taken the time to identify the many additional areas where predictive metrics will add even more value. My goal is for this list of second-generation of predictive metrics to stir your thinking and to lead you to develop metrics in new and uncharted areas of talent management.
Next week: Part 2
On March 16, 2015, ERE.net will publish the second part of this article, which will cover the third generation of talent management metrics that are likely to emerge after several more years.