Workforce Analytics Optimizes Human Capital

August 26, 2011 by  Filed under: Management 

Most companies spend copious amounts of time evaluating the performance of their investments in areas such as R&D, capital equipment and even sales and marketing, but they never analyze investments into what is probably their greatest area of expense: human capital. For services firms, this may comprise 85 to 90 percent of total costs, and even for manufacturing firms, human capital costs are often twice as high as other capital costs.

The last few years have been strategy-altering for companies struggling to survive in the recession, as many have been forced to reevaluate the basics in order to run lean and stay ahead of their competition. Cost sensitivity has been a core survival tactic. In that type of environment, can your organization afford not to pay attention to human capital costs? The issue is no longer whether to focus on the returns of investment in human capital; it is how to measure it.

Traditionally, workforce-related decisions have been subjective and involved little involvement from IT. The lack of easy access to data, combined with the dearth of in-house analytical resources become a barrier to more objective decision-making in workforce planning and management. With more organizations feeling the increasing need to quantify workforce costs and benefits and integrate workforce initiatives into their overall financial planning process, many organizations are paying increased attention to this type of analytics work. Any organization, regardless of size, that agrees with any of these statements could benefit from taking a data-driven, analytics-based approach to its human capital strategy:

  • Hiring expenses are on the rise due to increased or unanticipated employee attrition.
  • Even though employee turnover seems to be low, the people leaving are top talent.
  • Umbrella retention strategies are in place but not generating the desired results.
  • Substantial budget outlays for training are made, but management is unsure which areas to focus on.
  • Hiring budget is across several channels and vendors – but management is unsure which ones are cost-effective.
  • Operations centers struggle with staffing at an optimal level to meet service-level commitments.
  • Performance and talent management could benefit from more objectivity in the process.

The ultimate objective is to sync human capital strategy with business strategy.

Organize, Structure, Analyze and Optimize

Getting started is often the most daunting part. The first step is to identify all the relevant data. A typical progression is: organize, structure, analyze and optimize.

Many organizations find themselves with employee data, hiring data, compensation data, training data and contact center data sitting in isolated and incompatible platforms. The journey toward data-driven decision-making in workforce management starts with organizing the data sources in accessible data warehouses and data marts. This is followed up with structuring the information around established metrics and key performance indicators that help provide an understanding of the pulse of the HR organization.

A snapshot view of the metrics measured against industry benchmarks can identify areas of improvement, whereas tracking trends over time shows early warning of problem areas. For instance, if trends show seasonally low attrition rates around the holiday season, a sudden spike around the months of November/December should trigger further exploration into the matter. Tracking such trends also helps address issues proactively – if seasonal trends show higher attrition during certain periods, human resources can plan ahead and boost retention and recruiting efforts in the preceding months. A monthly dashboard with an organization’s top KPIs is an excellent way to keep a health check on the workforce and the performance of the HR organization. Those KPIs should be related to the top HR functions, such as resourcing, compensation and benefits, operations and business support.

Customer Analytics Parallels Workforce Analytics

Performance gaps identified through the regular reporting of such metrics or deviation from past trends often trigger the need for more advanced and sophisticated analysis. The advancements made in statistical and econometric modeling, and optimization in the areas of marketing, credit risk and finance can be easily adopted and applied to modeling workforce issues. It is appropriate to consider an employee as a customer in an internal environment. Hence, all the analytics that apply to a customer life cycle can easily be applied to an employee life cycle – from acquisition, to growth, to retention, to post-attrition to reacquisition.

Consider growth as an example. For a customer this would imply account-monitoring analytics such as purchase likelihood scoring, risk-based pricing and behavior scoring models. The parallels for an employee would be succession-readiness scores, optimal compensation models and engagement scoring models. Often the employee scoring and analytical models turn out to be a lot more robust than their consumer counterparts owing to the richness and authenticity of the data that is captured in-house.

Calculate Benefits of HR Programs

Analyzing the information helps identify key drivers, which when acted upon lead to optimized workforce strategies. If today, as part of the learning and development team within an HR organization, one spends $20 million annually across 250 different training courses, an evaluation of these courses with respect to their impact on productivity or compliance benefits can help rank-order the training programs in terms of efficacy and determine required frequency of classes.

Further analysis can provide insights into the minimum number of participants required per training class to break even, cross-training needs of trainers to maximize resource utilization, effectiveness of instructor-led versus Web-based training, and cost-efficiency of in-house training versus outsourced training. Inferences drawn from any of the above, if implemented, result in a direct and substantial bottom-line impact. By way of example, one well-known computer manufacturer and retailer was able to increase sales per store by more than 15 percent just by optimizing its training program using a sensitivity analysis around productivity lift.

Balancing Hard and Soft Data for Better Decision-Making

A serious set of objections to the philosophy of objective decision-making in workforce management emphasizes the sensitivity around employee issues that can never be captured through hard data. This is a valid concern, and workforce analytics does not do away with the softer issues surrounding employee sentiments, aspirations and motivation. Instead, it provides HR personnel who deal with day-to-day employee issues with a set of data-driven tools that can support decision-making. A retention-scoring model, for instance, can identify the list of employees who are at highest risk of leaving the organization, as well as the top factors that are driving such high-risk behavior.

Two employees might end up with the same low score that classifies them as high risk, but for one of them the most significant factor could be the lack of growth potential, while for the other it could be work-life balance. Knowledge of the specific drivers can make retention actions more timely and powerful. Thus armed, HR staff can then work to implement more targeted and actionable retention strategies, which are more effective than traditional methods and can potentially save the company millions of dollars they might have spent on umbrella retention strategies. It is the combination of the subjective and the objective that makes the strategy and its implementation successful.

High-performer turnover costs for a Fortune 500 company can run into several million dollars annually once productivity losses, training time and recruitment costs are all factored in. For an organization with 3,000 employees and an average salary of $45,000, even a one percent increase in retention rates through the implementation of these actionable employee-level strategies would translate into savings of more than $1.3 million annually.

Transforming Human Capital into a Source of Competitive Advantage

According to recent research, businesses that implement analytically driven employee strategies experience 22.1 percent higher revenue growth, 23.3 percent higher profit growth and a 66.8 percent reduction in turnover, as compared to companies that did not use similar practices. Optimized human capital strategies grounded in strong workforce analytics show positive results in bottom lines and customer loyalty. Organizations that are able to integrate analytics into their workforce planning and management will be the ones to capitalize on their human capital assets as the source of their competitive advantage.

Dhiraj Rajaram is the Founder, Chief Executive Officer and Chairman of Mu Sigma, which works with market-leading companies across multiple verticals, solving high impact business problems in the areas of Marketing, Supply Chain and Risk Analytics. Mu Sigma is head-quartered in Chicago with its main delivery center in Bangalore, India and is arguably the world’s largest pure-play decision sciences and analytics Services Company.

Dhiraj is responsible for the organization’s vision and strategic direction, building teams, aligning organizational resources to a customer centric vision and delivering profitable growth. Dhiraj has built Mu Sigma from the ground up, during which time he has executed activities that included raising seed and growth capital for the venture, securing key Fortune 100 customers, incubating a delivery unit and hiring key leadership members.

Before Mu Sigma, he advised senior executives across a variety of verticals as a strategy consultant at Booz Allen Hamilton and PricewaterhouseCoopers. Dhiraj holds an MBA from the University of Chicago. He also received an M.S. in Computer Engineering from Wayne State University and a Bachelor’s degree in Electrical Engineering from College of Engineering Guindy, Anna University.

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