The Future of Measuring Employee Productivity

A few weeks ago, I attended a Workforce Planning work group and one of the attendees asked me if it was possible to track employee productivity effectively – especially in knowledge worker roles. Sometimes receiving these types of questions makes me pause. I’ve been an analyst in the talent management space for several years now, and at times I wonder how it’s possible that organizations are struggling with what often seems like the most basic HR management questions. Upon first glance, this might be a basic question, but taking a closer look reminds me how convoluted most HR processes are. It is no question that as the definition of work changes over time, the way we measure work and determine its productivity will constantly evolve as well.

Traditional Employee Productivity Metrics and Their Limitations

Employee productivity is traditionally measured through the simple formula of units of outputs divided by units of inputs. Organizations often fall into the trap of dividing revenue generated by employees over expenses incurred by employees. This metric is referred to as direct labor productivity, and one of the reasons behind layoffs and corporate downsizing. Employers have long associated that reducing the largest cost component to their COGS (i.e. wages) will result in a higher productivity. 

While much of the industry acknowledges that a single metric approach to measuring employee productivity is myopic and a multifaceted approach is ideal, many organizations struggle with choosing which metrics to include in their indexing. Some multifactorial indexes on the market involve metrics such as revenue generated, number of employees, number of managers, and number of work hours. This is often quite easy to measure in certain industries such as manufacturing, retail and roles in sales or call centers. This is definitely a step forward, but continues the focus on tangible metrics that don’t always impact the quality of the work completed. 

Challenges in Measuring Knowledge Work Productivity

Harvard Business Review tackles the topic of creating a multifactorial index in detail in this “no-nonsense guide”, and does so quite eloquently by recommending organizations to identify which actions turn specific gears that are related to overall performance, and to work on including them in a productivity index that is weighted to a score of 100, with participation from managers and employees actually engaging in that work. 

One case study to call out is Orgvue and GSK, wherein the Global Manufacturing and Supply Division identified manufacturing quality scores as a factor that impacted productivity. GSK created a model to predict and flag potential risks in the systems. The model incorporated workforce data, training data, manufacturing and quality data, and helped produce key indicators to improve quality outcomes. Some of these outcomes that the GSK team identified include the timing and the quality of the training, which is an indirect input towards workforce productivity, but has a clear impact on the quality of the output.

This method of measurement is often achievable and accessible for roles with defined output or responsibilities, but what about knowledge workers? Even the HBR guide admits that measuring productivity for “white collar workers” can be challenging and not straightforward. While it might be possible to measure the number of packages shipped, and the time to ship a package for a line employee, determining the productivity of a software developer by the number of lines they write or the time to ship a feature might be illogical. 

First, it’s important to note some of the main differences when measuring productivity between these two types of roles:

• Measuring Creativity – how do you associate time value to a marketing professional brainstorming ideas on how to campaign the newest product? Or time value to a consultant that repeatedly completes different projects with different clients?
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• Collaboration – often, these roles require dependency on other roles in order to complete their tasks or to collaborate and share parts of a larger project. 
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• Qualitative Work – with work that requires a qualitative solution, direct feedback on the effectiveness of the solution is not available immediately.
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• Focus on Quality Standards – manufacturing employees have to adhere to specific quality standards, but knowledge workers operate without a benchmark, depending on the project.
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Acknowledging the differences in nuance between these types of roles is the first step in developing fair productivity indices. The second would be if you can answer the following question,

“How does this job provide value to the organization and its business priorities?”

A Framework for Developing Productivity Indices

For this example, let us attempt to measure the productivity of an HR Manager, handling the onboarding process at a large enterprise. Out of all the tasks this HR Manager might complete, the most critical is onboarding new employees in a timely and efficient manner. The easiest metric to track may be time to onboard, however, it ignores the quality of the onboarding process. 

So, what makes this HR Manager productive? Is it the number of employees they onboard or is it how well they onboard these employees? Is it how well these new hires perform? Another factor to consider is how long those employees stay at the company, and how much of it is related to the quality of their onboarding? Are great performers agnostic to the quality of onboarding? 

Balancing leading and lag indicators correctly is key in building an index. Leading indicators help identify early success and if there are opportunities to salvage potential employee churn, while lag indicators help provide context to outcomes. Some examples of lead/lag indicators to consider include time-to-engage, new hire satisfaction, new hire retention, and 90 day performance. 

• Time-to-Engage: How quickly new hires initiate onboarding processes, complete learning modules, etc.
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• New Hire Satisfaction: Survey results from new hires on the quality of the onboarding process.
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• New Hire Retention: How many new hires still employed after 90 days, 6 months, etc.
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• 90 Day Performance: Rating how well new hires applied onboarding learnings and their ability to succeed at the job.
   

Another key component is whether the onboarding program itself equips employees for success. Without this evaluation, even the best HR Managers will face low performance and high retention outcomes.

Building Productivity Metrics That Align with Organizational Goals

There is a fair amount of experimentation and data analysis involved with building a productivity index for every major role in the company, and it is a task where the reward is worth the effort. Like most talent management practices though, organizations should lead with the mindset of progress over perfection. Rather than wasting months to evaluate programs, it may be more effective to run a quick survey and focus on collecting the data to measure the metrics selected for the role. Pick your battles wisely and remember that part of productivity is the time and resources spent to identify how productive a role is.

But once you’ve got your indices, the next step is to clearly articulate these expectations to your employees. You want your employees to focus on the aspects of their role that contribute to overall organizational productivity, obviously. If the HR Manager focuses on the number of 1:1 interactions within the first 90 days, when it has no real impact over the retention or performance of a new hire, you’d want to communicate that.

In conclusion, measuring productivity in knowledge workers is possible, but it requires organizations to identify the unique value each role provides towards business priorities. This challenge is even more difficult in this “AI era” where administrative tasks are becoming more automated and knowledge workers are expected to contribute in a more subjective and qualitative manner that are harder to measure. Getting your organization to view work in this lens will ultimately help prepare yourself towards a future where asking the question “how does this job provide value to the organization and its business priorities?” becomes more common.

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