One company, faced with high attrition among its managers, was able to discover the root cause, address the issues and improve retention. Another successfully performed a diagnostic of its enterprise learning capabilities to determine which forms of training would have the greatest impact on performance.
Still another analyzed how to improve customer satisfaction and workforce performance in its retail channel and was able to achieve double-digit growth in sales productivity. In each case, the companies were able to avoid other remedies that would have been more costly and less productive.
How did these organizations do it? Through talent analytics—a precise, science-based analysis of employee performance and how a workforce is supported. This ability to process literally millions of data points can help executives make decisions based on objective data rather than hunches and intuition. And by giving organizations new insights into the patterns in workforce and HR performance that spur innovation and improve business performance, talent analytics can also provide them with a real talent advantage.
In addition to offering significant organizational improvements, talent analytics is giving HR executives not just a seat at the table but the basis for strategic discussions with the C-suite based on the substantive insights provided by hard numbers and the ability to more accurately predict the likely business outcomes of different kinds of talent initiatives. It is also helping companies prioritize and focus on those initiatives that are more likely to have a significant impact on productivity, customer experience and cost management. Furthermore, it is improving hiring and retention by identifying the factors and capabilities that are best matched to a particular job. And it’s supporting business strategy in more substantive ways, such as increasing topline productivity.
The journey to better ROI
Not surprisingly, companies are at different points along the journey to a better return on their talent analytics investments. Where they are is a function of not only the maturity of the organization’s data environment but also the analytics tools being used, level of available analytics skills, and the ability to push insights into key processes and decisions (see Sidebar).
Companies can begin by improving their basic reporting capabilities; then work on improving such functions as recruiting, training and retention; and finally move into broader areas, such as workforce planning and organizational change. Ultimately, the goal is to develop more advanced analytics capabilities that enable companies to optimize performance and even predict which workforce changes and investments are likely to produce the best results.
Improving HR reporting
Many companies are still struggling just to get basic reporting right. In some instances, they may not even know exactly how many people work for them, in what jobs and in what regions. And many still rely on first-generation spreadsheets to manage HR data.
This problem could be rooted in having disparate information systems where data is in incompatible formats, or even in something as simple as having two parts of the company using different titles for the same job. These companies face a longer-term challenge: to consolidate HR information systems and to develop, across units and locations, an intelligent and consistent “datamart”—an archive that organizes data in ways that are relevant to specific business needs and easily accessible to users needing information. These steps could dramatically advance the companies’ ability to perform meaningful talent analytics. But in the meantime, a number of actions can be taken.
For example, companies can design pilot analytics projects that involve cleaning up only some of the data and running limited sets of numbers. Developing sound talent analytics capabilities is a longer-term journey, but pilots can help companies acquire the skills they need (or identify where those skills could be sourced externally) while demonstrating the business case for analytics and improving the reporting environment from an HR perspective.
There are a number of tools in the marketplace that can help organizations achieve what is often referred to as “intelligent reporting” and can aid them in improving their overall data environment. Such tools can provide alerts about anomalies in business transactions, identify locations or units that are underperforming on key metrics, and also track performance trends on key business activities.
Analytics capabilities built into software-as-a-service (SaaS) HR applications offer another option for jump-starting talent analytics capabilities. SaaS solutions can enable organizations to correlate multiple data streams (such as HR financial and CRM data) and analyze them in light of business goals. Administrators at one technology university knew that a high percentage of the academic workforce would be retiring soon. The analytics embedded in the school’s SaaS HR solution enabled the administrators to develop a proactive HR strategy, working in a global talent marketplace, to identify promising prospective hires. When HR professionals can deliver analytics-based information it increases the respect conferred to them by administrators and the finance department.
As an organization’s data environment improves, analysts can look for patterns in HR data that can help companies improve areas related to hiring or attrition, or pinpoint areas where labor-related savings can be found. Improving performance in specific areas of the employee lifecycle can produce impressive financial results, considering that the cost of replacing a worker who leaves can be as high as 150 percent to 300 percent of the employee’s annual compensation. At the leadership level, the cost of correcting a bad hiring decision and replacing an executive can run into the millions of dollars.
Applying analytics to specific HR areas can yield impressive results. Take the case of a communications company that set out to understand the drivers of employee absenteeism at its call centers as a means of improving overall call center productivity. One hypothesis was that a major cause of absenteeism was the distance between an employee’s home and the center. As it turned out, a much more important factor was the employees’ family obligations, including caring for aging parents or young children.
The finding was critical. If the company had based changes in hiring policies or employee support programs on the original hypothesis, it could have wasted money, constricted the hiring pool and even increased rather than decreased absenteeism.
Better, more predictive hiring has been another benefit of talent analytics, especially as HR departments have come to realize that many traditional data points in assessing candidates—college grade-point averages, standardized test scores and even interviews—are notoriously poor predictors of future performance. Instead, companies such as Google are using analytics to determine why their current top performers are doing so well, and then feeding that information back into the hiring process.
Other analytics-based resources and tools are being developed to assist in the hiring process. Matchpoint Careers, for example, provides psychometric assessments of job candidates’ reasoning aptitude, behaviors, emotional intelligence and other factors to identify, at the application stage, those who have the potential to be top performers in a particular job. By comparing the assessment results against a database of profiles and answers from millions of candidates, HR can have greater confidence about the potential of new hires.
Another company, Silicon Valley startup Knack, uses games to help identify recruits with a higher probability of becoming successful employees. Recruiters use the games to look at a variety of capabilities and traits, from cognitive ability to creativity to learning to decision making. One of them, “Wasabi Waiter,” asks the player to assume the role of a waiter at a sushi restaurant. Players take orders and then analyze customers’ facial expressions—such as “happy,” “angry” and “sad”—to decide how to proceed. How applicants play the game indicates how well they read social and emotional signals. Performance is benchmarked against the top performers among a company’s current employees to help identify the most promising recruits.
Beyond these kinds of discrete offerings is a bigger trend, known as Big Data, which is about mobilizing and managing large volumes of data that are internal and external to the enterprise. Big Data analytics enables companies to run analyses of datasets as large as many exabytes and see patterns and causality well beyond what the human brain can process on its own. The HR department can begin with a hypothesis and see if it is supported by the data. But HR can also just say, “Let’s see where the numbers are taking us and what story is being told,” toward the end of extracting hidden, differentiated value. Big Data analytics is key to delivering on the full promise of talent analytics: predict workforce and marketplace developments and take action in advance.
With these tools and technology capabilities, companies are discovering important trends about their workforce. For example, a large technology company performed an extensive analysis on a representative sample of its workforce to study the multiple factors that went into whether an employee stayed or left. The analysis was complex, looking at the interrelationships among approximately 200 different HR and finance factors and more than 100,000 individuals over two years.
The results, not surprisingly, were also complex. In one part of the company, certain factors (such as opportunities for advancement, training offerings and the quality of supervision) made a difference between an employee staying or leaving. But in another part of the company, different factors were at play. Analytics can help parse these complexities into meaningful information for decision making.
An Asian life insurance company used talent analytics to make better decisions about employee recruiting and retention. The company began working with an HR outsourcing provider, primarily to improve the efficiency of its recruiting and onboarding processes. As the relationship progressed, however, the company asked the provider to help with a more serious business problem: retaining people in one of its most critical workforces—managers at its affiliated agency locations. The company was experiencing 100 percent turnover among these employees, with many leaving after only six months—a situation that was impeding overall corporate growth.
Executives had several hypotheses. Perhaps it was about compensation, or maybe it was an issue with the quality of life at the locations. The provider was able to use predictive analytics to determine the actual predictors of success in terms of the performance and retention of these workers. Among the insights: Educational requirements for incoming managers had set false expectations about the kind of work actually involved. The company had been requiring that all new managers have MBAs. In fact, the analysis showed that these recruits were often among the company’s poorest performers. This data enabled the company to alter the job profile, which also opened up the position to a broader group of candidates.
An important lesson of the company’s experience is that the results of an initial talent analytics program need to be revisited over time to continue to validate the findings. One point of comparison had been between managers working at branches within their home state and those working in a state other than where they had grown up. An initial analysis seemed to indicate that managers who had moved around frequently and had previously been forced to establish new networks were performing better. A year later, a more complete analysis of the parameters found that the reverse was actually true: Managers working within their state were outperforming the outsiders.
The results of this talent analytics program were impressive. Six months after the company changed its hiring profile, new-hire performance shot up by more than 100 percent and new-hire attrition went down by 50 percent.
Managing the organization
The patterns found using analytics can also help executives with larger-scale organizational planning and performance. Workforce planning, for example, has become a much more challenging part of HR’s charge, in part because of the expanding footprint of many companies and also because organizations are more likely to use a mix of internal employees, contractors or contingent workers, vendors and consultants.
Preventing temporary skills gaps can be critical to a company’s competitiveness, and analytics can help prevent talent shortfalls or shorten their duration.
One leading international insurer, after setting a new course for growth, had realized that its strategy could be undermined if it did not have the right people in the right numbers. An analytics initiative enabled the company to perform a detailed assessment of the most critical roles and the business drivers of workforce demand across functions. The team then estimated future needs; analytics also enabled the team to project attrition, retirement and promotion rates. The insurer used the analysis to consider different approaches to resolving anticipated workforce gaps, and to determine if such gaps should be addressed by retraining existing employees or through external recruiting.
Another important benefit of the new generation of analytics: helping executives keep their companies on track during periods of significant organizational change, especially periods when multiple change programs must be managed and coordinated. Although it is well known that the success rate of major change initiatives is low, most organizations persist in using such traditional tools as project management software and employee surveys. Analytics technologies offer an alternative: a data-based, insight-driven approach that provides objective information about progress and uses predictive modeling to give executives insights they can use to steer the organization more effectively through a complex journey of change.
For example, one digital analytics system that supports organizational change uses a research-based questionnaire to collect data from the organization about its change goals and internal capabilities. It then uses pattern-recognition technology—based on a database of more than 600,000 individual questionnaire responses— to predict the optimal path to improved performance for that organization.
As noted, the ability to handle large datasets is a unique and critical characteristic of the technologies that support predictive analytics. Although the findings are provided in easy-to-understand, graphic “maps” of the change progress, the map that serves as the basis for ongoing analysis of organizations was based on more than 33 billion calculations.
The feedback is configured so that management can identify the particular drivers of change for their organization covering the impact of growth, restructuring, cost reduction and technology implementation programs. Using predictive modeling techniques, executives can test the effects of various strategic choices on overall company performance.
Results can be impressive, especially as organizations are able to use the system to link financial information (key performance indicators from reporting systems) with the subjective data gleaned from the scientifically designed questionnaires. One company was able to turn around a $100 million IT implementation that had gone $4 million over budget. The analysis pinpointed the areas to address, and the actions based on those results brought operational costs back within budget in six months. Customer service also improved, and unit costs were reduced by 50 percent.
Supporting business strategy
Talent analytics can also take companies beyond improving specific aspects of the employee lifecycle and organizational management to identify, in a more open environment of potential ideas, ways that different HR strategies and initiatives can better support strategy and business performance.
One business environment where talent analytics has proven to be especially important is the corporate call center. These workforces tend to be large, so companies look for ways to optimize their spending in terms of getting the best performance for their investment.
Using analytics, companies can, for example, compare the performance of full-time and contract workers—not just in terms of cost but in terms of real performance metrics, such as customer satisfaction and first-call resolution. The analysis can look at the type of call, how it was handled and what category of worker handled it. Thanks to this talent analytics approach, a company can often deploy a new workforce segmentation strategy with the potential of delivering better customer satisfaction while also saving millions of dollars.
Another story about using talent analytics to support overall business improvement comes from another telecommunications company with a significant consumer retail channel. The analytics team worked with management to consider core channel performance issues, how to drive growth and customer satisfaction through the retail channel, and the role the workforce could have in such growth.
Three years later, actions taken as a result of the analysis, supported by large-scale change management efforts, delivered workforce productivity improvements of more than 19 percent in the retail channel. This translated to several hundred million dollars in additional operating cash flow.
Results like these, driven by talent analytics, are game changers for companies looking for insights that can improve the productivity and performance of their critical workforces. From the perspective of HR practitioners, they now have unique tools in place to deliver significant business impact rather than just a few percentage points of savings or productivity improvements here and there. And from the perspective of the BPO field, providers can use their analysis of talent and other factors to, in some cases, be bold enough to make contractual obligations for certain business outcomes around a variety of talent factors, including retention and new-hire performance.
For years, the human resources function has shouldered much of the responsibility for managing people, but it has often had to do so with too little real information and too segregated from the business. Talent analytics can change all this—revolutionizing not only the practice of HR but also how insights about workforce performance can be derived and applied to achieve real improvements in business performance.
Sidebar | Lessons from leaders
By Jill K. Goldstein, Samir Raza and Omesh Saraf
As companies continue on their talent analytics journey, there are a few important lessons they can learn from those that are further along.
Don’t wait for “perfect” data. For most organizations, standardizing global HR systems is a long-term challenge. But that’s no reason to wait to do talent analytics. It is possible to see useful results by doing some basic data cleanup and analysis. Indeed, this approach is really the only way to get better in the long term. Talent analytics is about continuous improvement over time. But that means starting sooner rather than later.
Begin with pilot projects. HR should begin with pilot projects focused on the top two or three issues their organization is facing with its critical workforces—attrition, perhaps, or the inability of new hires to perform well quickly. Identify these problem areas and begin a pilot to prove the value of the longer-term talent analytics enterprise. Such initiatives can demonstrate value to the business and help justify investments in data infrastructure and business intelligence technology for human capital management.
Align with business strategy. Don’t think too small. Stay aligned with business strategy and constantly question how better insights from your talent data might help improve business performance. Cost savings, improved retention—these and other specific HR points are important, to be sure. However, talent analytics is enabling HR and talent executives to have entirely new—and more important—conversations with the business.
Take an enterprise view. Talent analytics should be viewed from a holistic, enterprise perspective rather than as a one-time project. The lessons learned from banks and retail organizations that have developed customer analytics capabilities suggest that the benefits of talent analytics can be sustainable if an organization creates an overall roadmap that identifies the data, analytics and organizational capabilities required to advance on the overall analytics journey and to address the most strategic HR issues being faced across the organization.
- Consider how to develop an analytics team and where it should be placed in the organization. One of the key issues being debated today is how to structure and place the analytics unit in the organization. Many HR organizations are struggling to place analytics, a nontraditional function, within the traditional reporting structure.
One high-tech company has been successful at moving HR toward better data-based decisions by having its talent analytics team report directly to the vice president of HR. The team also has a representative in each major HR function. So whether it’s performance management or recruiting or training, someone from the analytics team is available to provide guidance in using data to make decisions.
Another alternative for companies is to begin with a central group—sometimes called a “center of excellence”—which can be a way to develop deep skills faster and then apportion them appropriately to parts of the organization where the potential for business impact is greatest.
- Don’t forget the people. It is a paradox, but in the rush to execute in new ways based on insights from talent analytics, it is possible to overlook real people. This applies in several ways. First, of course, experienced analysts are required to interpret the data and reach sound conclusions. Second, the team needs more than just analytics and business skills. The data is, after all, about human beings, so having people who are trained in the social sciences and in change management is critical to a successful talent analytics initiative.
One of the findings from a retail workforce productivity project conducted for a telecommunications company was that several stores were significantly overstaffed and others understaffed. From a pure analytics perspective, the answer might have seemed obvious: move people from one store to another. In reality, however, there is much more to consider. Depending on location and other personal factors, such transfers would not take place successfully without taking into account the dimensions of change management. (Back to story)
About the authors
Maureen L. Brosnan is the managing director responsible for Human Capital Management within Accenture Talent & HR Services. She is based in Boston.
Catherine S. Farley is the Seattle-based managing director responsible for Accenture Talent & HR Services.
David Gartside is the managing director responsible for HR offerings and capabilities in Accenture Talent & HR Services. He is based in New York.
Himanshu Tambe is the managing director responsible for the Accenture Management Consulting Capability Network, Talent & Organization. He is based in Delhi, India.
Jill K. Goldstein is responsible for HR and Learning Business Process Outsourcing within Accenture Talent & HR Services. She is based in Miami, Florida.
Samir Raza is a Chicago-based senior manager and the North America lead for Human Capital Analytics in Accenture Analytics.
Omesh Saraf is a senior manager and the Human Capital Analytics lead for the Accenture Management Consulting Capability Network. He is based in Bangalore.