Today’s post comes to us from Workforce Institute board member David Creelman.

Take a look at this people analytics data prepared by Revelio Labs. It looks at turnover (churn) vs. revenue growth. There is something very odd about Costco at the bottom right.

© Revelio Labs (2021)

One thing that is different at Costco relative to their competitors is a high ratio of junior to senior employees, i.e. a flat organization.

© Revelio Labs (2021)

If you want to read more of Revelio Lab’s piece on Costco click here: https://www.reveliolabs.com/news/business/what-makes-costco-a-great-place-to-work

However, I wanted to focus this blog on a larger take-away than just what’s happening with Costco.

What we’re seeing here are HR analytics that will be really intriguing to business leaders. The ability to use advanced technologies to gather and analyze data from a wide variety of public sources such as Indeed and LinkedIn to get a clear picture of an organization’s talent strategy and its impact on meaningful measures is a huge leap forward.

Historically, HR analytics work has been myopically focused on the data we can pull out of our own HRIS systems. This new work from Revelio Labs points to a new and much bolder direction for HR analytics. We can now do all kinds of analysis that compares our organizations to competitors. We can look at which departments competitors are investing in compared to which ones we are investing in. We can look at where they are getting talent and who is taking our talent. We can look at estimates of diversity data even if our competitors don’t report it.

The sheer volume of public data that is out there in the public sphere is astounding and I’m very excited to see what Revelio Labs and others continue to do to find value in that data.

What sources of data are your company looking at and measuring these days? What do you think are the most meaningful metrics in HR analytics? Share your thoughts in the Comments section.

Today's post comes to us from board member Neil Reichenberg.

A recent research report “Beyond HR Metrics: HR Analytics” issued by the organization where I work, International Public Management Association for Human Resources (IPMA-HR), sheds light on why and how public sector HR professionals are implementing HR analytics programs at their organizations.

Key findings included:

• HR analytics result in better communication with senior leadership and affect the strategic influence of HR either somewhat or a great deal.
• The primary reason for implementing HR analytics practices is to improve HR strategy and performance.
• Data collection focuses on impacting both internal and external processes.
• Data collected is used mostly to predict trends and influence future decision-making, report to both HR and the organization, and to answer specific questions or address problems.
• The most commonly collected HR metrics are headcount (87%), length of service (87%), turnover (74%), and cost of benefits (61%).  

Improvements resulting from the use of analytics in HR functional areas that were cited include:

• Recruitment/Hiring – Shorter time to hire, help HR departments to update their recruitment strategy, and find better qualified candidates.
• Training and Development – Develop more effective training, better able to identify training needs, and improve training curricula. These improvements resulted mostly from tracking participation, undertaking post-training evaluations, and conducting annual surveys.
• Compensation – Ability to provide more accurate and thorough data for market studies, update compensation strategy, adjust salary ranges, and review benefits packages.
• Workforce/Succession Planning – Ability to identify and report on areas where succession planning is needed, improve succession plans, and develop training programs.
• Performance Management – Increased completion rate of performance evaluations, assisting to switch to a goal-oriented evaluation approach, and facilitating the use of more frequent performance discussions.
• Diversity and Inclusion – Ability to track and report demographic data, developing engagement programs, and updating diversity goals.

Obstacles to implementing HR analytics that were cited by the survey respondents included insufficient funding, lack of training, and no access to analytics software. Only 1/3 of the survey respondents are part of an organization that has a person or team dedicated to HR data analysis, and only 20% indicated that their employer provides training to HR professionals on data collection and analysis.

Overall, this report shows that more and more organizations are looking to analytics to increase the strategic value of HR. Despite this fact, organizations have not yet staffed this function appropriately or provided training to their own staff to ensure the measurement is done effectively. Staffing and training of this function are opportunities for organizations in the coming months and years.

What about your organization? Is HR using analytics to make better-informed decisions? Or do you think they should? Tell us about it in the comments.

The following guest post is courtesy of our board member, David Creelman.

With Spring upon us and Summer right around the corner, many businesses are actively hiring seasonal workers to ensure they have coverage for their busy seasons.

For example, just last month, Home Depot, the nation’s largest home improvement chain, announced that it was hiring more than 80,000 workers nationwide to ensure that its nearly 2,000 stores are staffed and ready to welcome spring 2016, its busiest season.

The home improvement industry isn’t alone, of course: golf courses, recreational facilities, summer camps and programs, seasonal restaurants and retailers, along with many others are all in hiring mode right now to match this seasonal business cycle.

Beyond just getting bodies onboard, leveraging this seasonal workforce can be a big competitive advantage. Whenever we are dealing with a potentially big strategic advantage; particularly one involving large numbers of workers; we want to call on the power of people analytics.

So how can people analytics help? The truth is, they can help in so many ways that I could never name them all in a short blog post, but I can say that there are a few questions that every seasonal employer should be asking themselves right now:

• Are we scheduling in a way that minimizes labor costs?
• Are we scheduling in a way that minimizes turnover?
• Are we running an analysis to discover the right trade off between the two?
• Would increasing training for seasonal workers pay off?
• What are the most costly mistakes seasonal workers make, that regular workers don’t make?
• What is the most effective way to screen seasonal workers?

You can approach these questions with sophisticated statistical methods, but you don’t need to. If you don’t have the data, tools, or skills for advanced analytics, you’ll find you can go surprisingly far with the back of an envelope or Excel. Yes, you want to do as good a job as you can; especially when it’s a strategic issue, but my mantra is that some numbers are almost always better than no numbers. Don’t let lack of heavy duty analytics horsepower be an excuse for not thinking about these issues.

My challenge for businesses is this: is it a normal part of your culture to bring analytics savvy to challenges like optimizing the effectiveness of your seasonal workforce? We are all enamored with Big Data and the latest analytics tools; but in my experience the lack of an analytics culture is usually the bigger problem. My advice? Especially if you are starting from scratch, focus less on what the analytics tools are, and more on creating an analytics-focused culture that puts value on numbers-based analysis.

Today's post comes to us courtesy of our newest Board member, John Frehse, Managing Partner at Core Practice and a sought after speaker on the topic of workforce management. John's post aims to help organizations get started in making sense of big data and using it to solve real problems.

As technology advances allow us to analyze every aspect of business operations, many companies are inundated with data. Although we celebrate the advances in technology, this “data revolution” has also blurred the line between valuable insight and mundane non-value added information. As a result, many companies are now buried in indecipherable numbers. To successfully pierce through the mountains of useless data and focus on the strategic insights, it is critical businesses have the tools to translate heaps of distracting data into useful information.

For many organizations, payroll and labor IT systems are a significant source of operational data. As labor is almost always the number one controllable cost, dedicating time to this aspect of your business can yield significant financial and operational improvements. Many techniques, including Lean and Six Sigma, encourage large amounts of data analysis and dissemination of this information to all levels of the organization. The challenge often begins with employees’ ability to efficiently and effectively digest massive amounts of data.

The following three data sets are a good place to start:

1. The Workforce to the Workload Mismatch (WF/WL)

The Workforce to the Workload Mismatch shows how well the employees are matched up to the required hours needed to get the work done. Looking at the Workforce to Workload Mismatch on a granular level can show where labor waste occurs and more importantly why it occurs. Is Friday always understaffed? Are there too many people in the beginning of the month and not enough at the end of the month? Are there seasonal or variable spikes in demand that are not met? Seeing this mismatch and understanding root cause will quickly allow for improvements.

2. Demand Volatility (LVIX)

The Labor Volatility Index (LVIX) is a complex analysis that measures how much and how often the volatility in the demand for a service or product changes. The LVIX is prescriptive in guiding management teams to the best labor strategies based on their current situations and the challenges they face. The analysis provides management teams with how much labor is needed to satisfy demand. When we look at labor effectiveness and utilization, managers and supervisors are often told that they could have done a better job putting the right people in the right place at the right time. The LVIX provides a more sophisticated analysis, yielding a deeper understanding of the degree of difficulty required to achieve this goal.

3. Absenteeism

Absenteeism is loosely defined as periods of time where employees do not come to work. There are both planned activities like vacations and holidays, and unplanned activities like sick time and general call-offs. Unplanned absenteeism in particular negatively impacts the entire organization. Increasing visibility and transparency on this issue will immediately help improve the problem. Scrambling to find replacements or operating with less people than appropriate kills quality, service, and performance for everyone. Do more unplanned absences happen on Mondays after a holiday weekend? How likely are employees to show up for overtime on Saturday or Sunday compared to a Tuesday? Do employees call off on certain days and then show up for overtime opportunities? Identifying the trends can help isolate the behaviors and take corrective action.

By focusing on these three key performance metrics, management teams are able to quickly analyze large amounts of data, avoid data paralysis, and make strategic and well-informed decisions about important labor-related topics. By bringing a little focus to your Big Data strategy, you can reap big rewards.

Yesterday I chatted with our board members David Creelman and William Tincup about what organizations need to do to create HR analytics that matter to the business.  Big Data is one of the linchpins of the big 4 SMAC themes in technology today: Social-Mobile-Analytics-Cloud.  Deployed alone and in various combinations, these technologies continue to transform the way work gets done. For many people,  harnessing the power of big data remains a new frontier.  In this podcast, David and William share their thoughts on some of the following questions:

• If you're new to analytics, how do you get started?
• What skills do you need to implement analytics projects?
• How do you present the results of your data analysis to senior management?

You can listen to our conversation here:

What have you done to embed more data-driven decision making into your organization?