“Retail arbitrage” is how Juston and Kristen Herbert are saving $50,000 to adopt a child. They scour big box stores such as Target for clearance deals on products like water bottles, which they then buy for $5 and sell on Amazon for $19.95. Chris Green, a former sales representative for Bosch Power Tools, penned a book that teaches people to use Fulfilment By Amazon to make money through this type of gig.
Why does this matter to HR and organization leaders? Because, undoubtedly, folks like the Herberts are working regular jobs for companies like yours and retail arbitrage is their side hustle. But, some of them, like Green, left jobs at companies like yours to do this full-time.
Still, most work systems in organizations, most social debate and regulations, and most leaders and workers, think of work as a job, and workers as regular full-time employees. That perspective will cause you to miss things like retail arbitrage, which may hold important insights for your workforce planning. We need to break this way of thinking and one important way to accomplish this is to improve how we “count” work in society.
Was the Gig Economy Just A Fluke?
Is the “gig economy” growing much slower than we thought? Is it irrelevant to the lives and choices of workers in large organizations?
Recently, Harvard economists Katz and Krueger walked back their attention-grabbing conclusion from 2015, that “growth in the share of workers classified as self-employed freelancers or working for a contract firm…the percent of workers in alternative jobs rose from 10.5% in 2005 to 15.8% in 2015.” They recently repeated their analysis using more recent data and found the actual increase was just one or two percent.
The more interesting finding may be just how difficult it is to actually capture this kind of work using traditional methods. The authors report an interesting experiment in which 61% of those replying to a typical survey question said they did not hold multiple jobs, but actually mistakenly failed to report working a gig, including writers, editors, teachers, and dog sitters.
As the authors note, “if these workers are added to [those who correctly reported being] multiple job holders, the percent of workers who are multiple job holders would almost double from 39 percent to 77 percent.”
A 2018 New York Times story in June of 2018 noted: “The government’s numbers, by design, do not include people who do gig or freelance work in addition to traditional jobs, and they may not fully capture income-generating activities that people might not consider ‘work,’ like renting out a home on Airbnb.”
Stand outside the departure gates at any major airport, and you get the feeling that everyone is an Uber or Lyft driver. Search the Airbnb app for any major city and it will seem that almost everyone is renting out a room or a guest house. Regulators and communities from New York to Seattle to Paris wrestle with the dilemmas of the sharing economy.
John Younger recently noted that “Upwork estimates the total U.S. freelance population at over 50 million individuals. McKinsey sized the freelance population at over 150 million individuals worldwide. Deloitte and PwC both predict that organizations in the future will have large contingents of freelancers supporting them and Accenture asserts that future organizations may have only a small minority of full-time employees.”
How big is the gig economy? That’s not the right question. Yes, we need better measurement systems, but it’s already clear that regular employees with side hustles are much more common than traditional statistics suggest. That’s just a hint that the hidden fluid workforce offers your organization important work alternatives and that they may be the reason you lose or can’t hire regular employees. Organization leaders and the HR profession need to understand and optimize the mix of regular employees and alternative workers.
Ignore the Lessons of the Fluid Workforce at Your Peril
Regardless of the exact size of the “gig” economy, the fluid workforce is real so organizations must learn to “lead the work”–not just regular full-time employees. Organizations and society must learn to better measure and plan for work achieved through many different types of arrangements, including employment, contracts, gigs, alliances, volunteers, etc. This is even more important when you add work automation to the mix. That means heeding lessons like:
Deconstruct the job
Trapping work in a job description or considering only “people in jobs” will cause you to miss the key patterns. To see why your employees may be leaving to do retail arbitrage or where you can tap freelance platforms to alleviate work shortages, you need to deconstruct the job into its tasks. Freelancers may not fit precisely into a whole job just like your sales associates aren’t substituting their whole job for retail arbitrage. However, these alternatives are clearer when you can see what work tasks inside the job are in play.
Make your employment system more fluid
Your organization may not be ready yet to throw open its labor market to include all kinds of workers, but you can adopt elements of the fluid workforce for your own employees. New work and technology models include on-demand artificial intelligence, extreme personalization, and secure and accessible cloud-based work repositories. These repositories can provide a searchable location where work and workers can be identified and matched using a common lexicon.
IBM’s Open Talent Marketplace allows managers to deconstruct work into short-cycle events, publicize those events to an internal and external population of players (such as those who use the platform to bid for and form communities to complete the work), and track work history and capabilities–and this is all supported by common work language that constantly evolves through a partnership between Watson-like artificial intelligence and human judgment.
One attractive feature of side gigs like retail arbitrage or moonlighting on freelance platforms is that they offer more flexibility and control than regular jobs. One study found that German workers with more control over their work hours actually worked more, not less. Your employees may use their flexibility for side hustles, but that may help them to keep their regular job instead of leaving it.
The Fluid Workforce Remains an Untapped Opportunity
The folks doing retail arbitrage have been described as “flesh and blood robots for Amazon” because using Fulfilment by Amazon means they add their own products to Amazon’s online catalog, ship their products to Amazon’s warehouses, and let Amazon handle it from there. One retail arbitrager said that Amazon “needs people like me to fill all the holes in the marketplace.”
Many organizations already fill holes in their labor market with freelancers doing tasks like market research, product testing, and consumer research. HR and talent planning systems shouldn’t miss such opportunities by restricting themselves only to “jobs.”
There are a number of reasons that companies resort to layoffs, including cutting costs and unavoidable staff reduction. Navigating a reduction in workforce is a tricky and sensitive process. The use of people analytics in reducing headcount can provide HR managers with the data necessary to make the most informed and beneficial decision for the organization.
For one particular financial service provider (FSP), having an analytics-driven strategy has proven incredibly successful. The FSP found that a certain area of their business wasn’t seeing as much revenue as it had in the past and they didn’t expect it to ever bounce back to previous revenue levels.
Their Finance department said they needed to reduce headcount to make up for the loss in revenue. After exploring the data with people analytics, HR came back with evidence and projections showing layoffs weren’t necessary after all, as voluntary turnover (including retirement and resignations) would get them to Finance’s headcount target without having to let anybody go; they just needed to wait for the data projections to run their natural course.
The data proved true—natural turnover brought them to their target only one month over the Finance department’s goal. The FSP’s HR team demonstrated through analytics that natural turnover would achieve the same goal with a lot less risk, loss of talent, less reputational damage, and less cost.
Re-Examining the Headcount Reduction Process
Typically, Finance departments make the call for cost reduction and relay the message to HR leaders to cut costs across the organization. Without the use of data, leaders often go with “gut feel” after considering who will be least critical in achieving departmental goals in the coming year.
Other organizations go with the expected “last in, first out” methodology, in which those with the shortest tenure are the first to be let go. Another strategy is to let go of the most expensive people, thereby saving the most money while letting go of the fewest staff.
In all these cases, an evidence-based process is missing.
PwC’s 21st CEO Survey calls for the use of analytics in transforming and restructuring how organizations work. In the case of layoffs, HR leaders benefit from using people analytics to inform their decision-making.
Below are four data-driven steps to determining the right headcount reduction strategy for your organization.
Step 1: Gather Information for Strategy Planning
While Finance may have a number they expect HR to hit by a certain date, it is ultimately up to HR to determine the strategy of who, when, and how. HR leaders should ask several questions of their Finance team, and then use people analytics to drill deeper and determine the best solution, such as:
Is the reduction across the organization, within a specific geography, or within a specific work type?
Are we trying to reduce overall costs or reduce costs in a particular area due to a shortage of work?
Why do we need to cut costs? Is there less work to do or do we need to do the work at lower costs?
Understanding the “why” behind Finance targets allows you to find the right solution—and it isn’t always reducing headcount! By knowing the prospective future state of the business, HR can produce solutions that match and work towards business goals.
Step 2: Run Potential Scenarios
Once you understand why Finance targets have been set and what future state they’re working towards, it’s possible to start running different scenarios on how best to reduce employee counts.
In looking to reduce headcount, perform “what-if” analyses to explore different layoff scenarios, as well as alternatives to letting people go. For example, you could test how implementing early retirement could deliver Finance’s targets over time.
With analytics, you’re better able to see the possible outcomes of your decisions before you have to make them.
Step 3: Determine the Mitigation Plan
After looking at the planning and what-if data, HR leaders must determine mitigation steps to ensure that reducing headcount is controlled. Keep in mind that turnover contagion can occur as a result of layoffs. Refer to Curbing Employee Turnover Contagion to learn how to use people analytics to ward against this phenomenon.
Consider the following steps in creating your headcount reduction strategy:
Identify high performers that need to be kept and implement targeted retention strategies.
Determine if there is a need to ramp up in one department before ramping down in another.
Step 4: Monitor the Impacts
People analytics is essential in monitoring the effects of your organization reduction. The goal of reducing employee counts is often to save costs or help the business perform better, so it is imperative that you monitor the aftermath of a reduction in workforce.
While data supports decision-making, data is not the decision. In the end, HR must make downsizing decisions, and analytics simply provide insight into the most beneficial reductions for the organization. Don’t let finance drive the decision. Though finance owns the budget number, they don’t own the who, when, and how when it comes to formulating the people behind the numbers.
Use your strategic plan to achieve business targets. There are creative and informed decisions that HR can make using people analytics. By taking control of the strategy to reach finance’s budget targets, HR managers are able to utilize background research and data in support of future business goals.
At Culture Amp, we run a pay equity analysis toward the end of every merit cycle to identify and address compensation imbalances. It’s part of ensuring that we work towards an equitable employee experience and we encourage every organization, no matter what size or industry, to consider running a pay equity analysis. I’m going to show you a popular approach to pay equity with regards to gender in this article.
A gender pay equity analysis is, unfortunately, not as simple as it seems. You can’t simply compare the average of what men make to the average of what women make; you need to take great care in making sure you’re comparing apples to apples. That is, you’ll want to compare what men make to what women make in the same job level, department, and years of experience. There are other factors to consider as well–such as if your workforce is distributed across locations with varying compensation bands.
Start with a clear objective of what you want to answer. For the purpose of this example, let’s answer this question: “Is there a systematic bias in how Men vs. Women employees are paid?”
Step 1: Prepare your data and run a Regression Analysis
Get intimate with your data–use a discerning eye and make sure no outliers exist, currency is equivalent, pay period is consistent, etc.
To capture the gender pay gap, create a dummy variable with ‘1’ for male and ‘0’ for female employees. Run your regression. The coefficient for this dummy (male) variable will provide the gender pay gap between females and males (reference level is female). Look at the log of the total compensation value to get the best approximation for the pay gap.
Step 2: Consider the results of the unadjusted and adjusted model
When your regression model is built with just one variable as a predictor of compensation–gender, in our example–you have an “unadjusted” gender pay gap.
Run a Regression to account for the differences in other demographic factors in your data set that uncover where bias may exist.
Compare and evaluate your two models: Is there a gender gap? If so, does it still exist in the presence of other demographic factors? Looking at the regression coefficient for the gender variable, you can assess the severity of the gap.
Taking into consideration all of the demographic variables–level, years of experience, and role–that may impact compensation in the example above, gender is not a significant factor. There isn’t sufficient evidence to make the conclusion that systematic bias exists. The unadjusted model is rarely robust enough to come to this conclusion!
Step 3: Interpret your results and share them in a way your organization can understand
One thing we’ve observed at Culture Amp is that, even in organizations where pay equity is taken very seriously, there can be a misalignment in the perception of it.
For example, many engagement surveys ask employees if they believe compensation is fair and even when results confirm it is and there is no systematic bias, there can still be a perception that a pay gap exists. That’s why it’s important to communicate the results of your pay equity analysis. A good People Analyst understands that no matter how robust your analysis is; it has no impact unless it can’t be distilled into an easy-to-understand visualization and/or easily communicated by a trusted executive to other stakeholders.
Step 3 (and a half): What if systematic bias does exist in my organization?
The example in Step 2 is an ideal one. The reality is that most organizations do have a pay gap and it affects the livelihoods of any demographic group that suffers from this bias.
What negotiation processes are allowed during the new hire process?
Does your performance management philosophy utilize measures that are highly susceptible to bias, such as measuring “potential?”
Does your performance management tool calibrate across gender? (hint: it should)
Are compensation adjustments allowed off-cycle? If so, examine the circumstances
To be clear; there are no check-the-box solutions to the above questions. The most important thing to consider is how you want to address how your systems and processes can unintentionally contribute to systemic inequalities.
You should rely on HRBPs to diagnose pockets in the organization for individual adjustments where needed (they will likely want to loop in your legal team). Addressing systematic bias should be a responsibility of the office of Diversity and Inclusion, but if that doesn’t exist yet, then look to the leader of Talent and Rewards.
Step 4: Have a facilitated company discussion.
Pay equity analysis is a valuable tool in a portfolio of diversity management strategies. What D&I professionals are beginning to understand is that the presentation of data and facts must be accompanied by healthy discussion; this is because the emotional consequence of equity is equally as important as the numbers.
Buy-in on pay equity will be accomplished by a D&I leader who is willing to create a forum for vulnerable conversations about what pay equity at your organization should look like at a deeper human level.
As Sophocles said, “Heaven never helps the man who will not act.”
Legend has it that if you drop a frog into hot water, it jumps out. But put the same frog in cold water and heat it gradually, and the frog stays put. Lulled into complacency, it simply boils to death.
For business leaders, the teaching is relevant and profound. As change heats the pot that forms our world, our impulse is to resist change. But this could be a mistake. Just look at how we continue to advance global warming—or cling to dying industries. We owe it to ourselves and each other to do better.
What follows are six truths about the future of business I envision as a veteran CEO in the people analytics industry that I would encourage every leader to consider.
1. Decisions will be supported by big data
Executives rely on gut instincts. We have to: The pressure to make rapid-fire decisions is constant. But according to Daniel Kahneman’s Nobel-winning research and his book Thinking, Fast and Slow, the cognitive biases that inform gut decisions can lead to false conclusions. With the mind-boggling volumes of consumer and internet of things (IoT) data being generated with every click, share and swipe, I believe it’s imperative that data become an integral part of how we make decisions. Take, for example, the marketing field; once powered by gut instinct in the age of Mad Men, marketing can now leverage complex data analytics to inform decision making for everything from buying ads to creating social media content.
Collecting data is the easy part. What’s harder is discovering insights—and then learning to believe them. New analytics solutions arrive daily to help curb biases, correct errors and model the best potential outcomes for decision-making. The sooner CEOs get comfortable incorporating these into day-to-day thinking, the better.
2. Virtually every company will be a global company
Globalization is likely here to stay. According to the European Centre for International Political Economy, it can make products cheaper, provide access to growth opportunities, and mitigate cost inflation.
How do we thrive by selling to and buying from other cultures? Do we work through a multitiered distribution system or produce and partner locally? There are many factors to consider. But every country I know of that has ever attempted to exit from international trade has stagnated.
This shift could be particularly challenging to Americans accustomed to the notion that the U.S. is king. In 2013, McKinsey estimated that by 2025, more than 45% of Fortune 500 companies will be from emerging markets, including greater China.This does not mean that the U.S. has to give up its exceptionalism. We can lead by example. Above all, I believe we have to participate in order to be taken seriously.
3. Your company can benefit from being an artificial intelligence (AI) business
Over time, software has become one of the primary levers for generating value in business. We use it to automate manual processes and enable instant and complex communication. All of these advances, from my perspective, arose from the speed and accuracy of computer processing, with little incremental knowledge gained.
Artificial intelligence adds a crucial dimension: machine learning and optimization based on data. Now, to stay viable, every business will likely need to invest in AI. Gartner estimates that 85% of CIOs will be piloting AI programs by 2020.
The question becomes not if you will incorporate AI, but how. Will you invest in automating customer care, greater quality control on production lines or machine learning to help workers learn how to serve customers more proactively? Choosing which functions will benefit from AI, and when, will be of critical importance.
4. The skills gap is at your doorstep
For the first time I’ve seen in recent U.S. history, CNBC reported we had more job openings than corresponding skilled labor in July 2018. A decade ago, many of these jobs didn’t exist. The term “data scientist” was coined in 2008; now, CIO reports that job postings for data scientists on Indeed are up 75% from 2015.
While people over 50 can probably still reliably expect to stay in their trained profession, this is not true for younger workers. To remain relevant, I expect they will need to completely reskill themselves every 7–10 years.
As the half-life of a skill (now two-and-a-half to five years, according to Deloitte) falls, and data and AI proliferate, we need to look beyond simple recruiting to broad-based retention and retraining. Our challenge will be to identify the necessary skills and develop adequate training. It will be difficult for universities to predict demand. Companies must assume responsibility, since they may be the first to recognize—and suffer from—the skills gap.
5. Cryptocurrency could come to your industry soon
Cryptocurrency enables people to buy anything, anywhere—without taxation, an audit trail or government oversight. Today, cryptocurrencies are known for their part in shady business practices. But they are here, likely forever, despite governmental attempts to legislate them out of existence. Businesses may need to figure out how to co-exist with cryptocurrencies—even to the extent of using crypto in lieu of established currencies.
6. You’ll soon be grappling with tough, climate-driven questions
According to The Wall Street Journal, Hurricane Florence and Irma each caused well over $30 billion in damage, taking fatalities and leaving many homes flooded and powerless. Last summer, it seemed at times that the whole North American West Coast was on fire.
Emergency planning is an obvious imperative for every CEO. But as climate-related emergencies become commonplace, considerations and costs will accumulate. Where do you put your manufacturing plants and your computer server farms? Do you concentrate your facilities in one place, or do you distribute them to diversify risk? What happens when entire coasts or low-lying states are inundated with seawater? Do you have an adequate power supply to deal with extreme temperatures?
We are already (literally) feeling the heat. While governments may be unwilling to take unpopular action, businesses cannot afford the luxury of fiddling while Rome burns. Boards may not forgive the CEO who did not prepare for the climate emergency or the cryptocurrency takeover. Let’s take action before the water boils—and it is too late.
People analytics is no longer a nice-to-have, but a must-have for all HR leaders. Whether you’re just beginning your journey or looking to advance your practices, attending conferences is one of the best ways to accelerate the maturity of your data and analytics capabilities.
If you’re looking to learn from top-notch thought leaders, expand your networking opportunities, or find the next innovative technology, our list of 2019 people analytics conferences and events will help you stay on top of the latest advances and trends.
Where: Many cities across the US What data-driven HR leaders can expect: The CHRO Leadership Summit has been designed to facilitate collaboration across industries and to hone leadership skills through the peer-based sharing of best practices from the leading practitioners and thought leaders in HR today.
Where: Many cities across the US What data-driven HR leaders can expect: These intimate and private gatherings offer a trusted platform to establish and strengthen relationships as an extension of CHRO Executive Summits. Each dinner is structured as a Boardroom Forum, Collaborative Forum or a Game Changer Forum. The formats offer compelling content and the rare opportunity to connect with a select group of executives for ample, unparalleled networking.
When: January 31st – February 1st Where: San Francisco, CA What data-driven HR leaders can expect: As data generation and use become more pervasive, it’s now our collective challenge to understand the boundaries and, in turn, choose wisely. Expect this year’s conference to feature the leading practices and ideas shared by many of the world’s foremost People Analytics leaders.
When: February 5-6 Where: Birmingham, UK What data-driven HR leaders can expect: The HRD Summit attracts senior HR leaders looking to share and exchange knowledge on the latest strategies and technologies available to improve their organizational performance.
When: March 5-7 Where: Miami, FL What data-driven HR leaders can expect: From understanding how to align business focus, to balancing the need for agile workforce planning cycles with long-term strategy, to building a people analytics dream team, you will learn to enable data-based decision making at your organization.
When: March 19-20, 2019 Where: Austin, TX What data-driven HR leaders can expect: This is the definitive HR event for forward-thinking people leaders from digital and physical retailers. Discover how to adapt to consumer behavior and exceed the new expectations of your employees and customers.
When: April 24-25 Where: London, UK What data-driven HR leaders can expect: This event is focused on bringing together proven and emerging talent planning & analytics leaders, practitioners, academics, vendors, and other influencers to authentically explore leading practices, emerging innovations, and other trends affecting the future of work and leader decision-making.
When: May 6-8 Where: San Francisco, CA What data-driven HR leaders can expect: Join leaders in data-driven HR at Visier’s annual people analytics and workforce planning conference. Expect case studies and best practices from top global brands on topics such as inspiring change through people analytics, moving to agile workforce planning, creating value through data-driven talent acquisition, adopting innovative learning and development practices, and much more. Early bird is on until March 15 — Register today!
When: June 24-26 Where: Chicago, IL What data-driven HR leaders can expect: Discover how strategic people leaders at the heart of the industry’s most admired organizations will meet to radically rethink the way we work to transform the human experience in healthcare.
When: July 16-17, 2019 Where: New York, NY What data-driven HR leaders can expect: This forum is where the most disruptive senior HR leaders in financial services can challenge traditional thinking around how to radically reinvent HR for the tech-centric, customer-oriented, data-driven financial services business of the future.
When: September 18-19 Where: London, UK What data-driven HR leaders can expect: Designed for CHROsand their teams, Gartner ReimagineHR brings HR leaders together to learn how to drive innovation and transformation across their organizations.
When: October 1-4 Where: Las Vegas, NV What data-driven HR leaders can expect: From SaaS to analytics, to big data to social media, and more—learn from industry experts on business processes and organizational successes enabled by technology. Expect topics to cover everything from best practices for buying and implementing technology to the digital disruptions coming HR’s way.
Every organization needs them — those “go-to” people who are at the hub of collaboration.
Whether it’s a talented coder who is always sharing best practices with junior colleagues or a finance professional who is a wizard with spreadsheet formulas, an employee who spends a large portion of his time sharing knowledge and responding to requests for help can be a major performance catalyst.
While their impact can be exponential, these star employees can be hard to spot. Collaborative employees may not necessarily rank high on the org chart, and they often focus on helping others at the expense of their own productivity. As HR expert John Boudreau states, “even sophisticated talent management systems tend to overlook about half of these central players.”
In other words, highly collaborative employees are often quiet drivers of business performance. And this can lead to major problems.
The Business Impact of Collaborative Overload
As discussed in this HBR post, research conducted by Rob Cross and other academics shows that employees who are “seen as the best sources of information and in highest demand as collaborators in their companies — have the lowest engagement and career satisfaction scores…”
A highly connected employee can easily become overloaded with urgent, ad-hoc requests, undermining her sense of control. Because she may not also be getting the management recognition she feels she deserves, she can end up feeling underappreciated. At the end of the day, she can suffer from “collaborative overload.”
This is the classic recipe for burnout, as identified by the leading experts in the area. It explains why an eager programmer who starts out going “above and beyond” for his team, for example, may eventually head down a path towards exhaustion, detachment, and a general sense of ineffectiveness.
Beyond concerns for individual employees, there are also major business impacts to consider. Collaborative overload results in sufferers doing two things, according to Cross et al. The first is “staying and spreading their growing apathy to their colleagues.” The second is leaving the organization entirely.
This can make a serious dent in the balance sheet. When the direct costs of replacing an employee, interim reduction in labor costs, and costs of lost productivity are all taken into account, the total cost of voluntary turnover is $109,676 per exiting employee for an average US organization, according to Bersin by Deloitte research. An increase, then, of turnover by just 1% in a company of 30,000 employees can cost $32.9 million per year.
Now imagine what happens when employees start leaving in rapid succession after a well-connected employee departs. That’s called turnover contagion, a term experts use to describe what happens when people quit their jobs simply because other people are talking about leaving, job searching, or actually jumping ship.
When several well-connected people leave, it can have a multiplier effect. Think of a virus: the more contact an infected person has with other people, the more it spreads. Every organization has the ability to withstand the loss of talent, but that resiliency has a limit before there are serious business impacts.
Help Your Helpers With ONA
If you are concerned about “helper burnout” within your organization, getting your executive team to support initiatives to address it may be a challenge. You need to show that this is about more than demonstrating concern for employee well-being — it’s also about reducing potential bottom line impacts and maintaining productivity. One way to demonstrate this is by quantifying the impacts of turnover.
To do this, you need to develop a hypothesis (such as “we are experiencing turnover amongst people who are key to business performance due to collaboration overload”), test it, and frame any findings in financial terms. This can’t be accomplished with a couple of data sources. To ensure you are collecting all the relevant data, follow these main steps:
Step 1. Identify the Quiet Influencers
A growing number of companies are using Organizational Network Analysis (ONA), which goes beyond the traditional org chart to identify the true influencers in a business through the analysis of social interactions.
A proper ONA study incorporates data coming from multiple sources, such as calendar, email, instant messaging, sociometric badges, and knowledge sharing applications. This provides a realistic view of how work actually gets done, and which people are the main conduits of productivity (of course, data privacy also needs to be properly addressed).
Be aware that digital streams can provide false positives and negatives. For example, people use instant messaging differently — some write long paragraphs, and others use one-line phrases. An analysis which simply counts the number of sends without considering the volume of words may under or over-state the level to which individual employee communication differs. To properly control for these, you will also need to leverage employee survey or observation data. Consider working with a vendor who specializes in this area, such as Trustsphere, to ensure you are gaining real insights to test your hypothesis.
2. Uncover Turnover Trends
Once you have identified the hidden stars in your organization, it is time to determine whether they are experiencing higher levels of turnover than their teammates and the rest of your employee population. To do this, you will need to calculate and compare resignation rates by pulling data from your HRIS.
If you find that there is indeed a higher level of turnover amongst people with many connections, then you can conclude that collaborative overload is a problem. From here, you can dig deeper into your data to uncover more granular insights, such as whether certain employee recognition programs make a difference in terms of retention.
Of course, the best approach is to nip the problem in the bud in the first place. One early sign that burnout is happening is absenteeism: people who are on the road to burnout are prone to illness or may feel entitled to take unscheduled days off. Predictive analytics can also help you identify those collaborators who pose a flight risk.
3. Quantify the Dollar Impact of the Problem
Before approaching your executive team to garner support for an HR program, it’s important to quantify the dollar impact of the problem and make projections about how it might worsen in the future. Executives will want to know how investing in the proposed solution — whether it involves increasing rewards for previously unidentified influencers or making a repository of resources more readily available to teams — will avoid costs or drive up revenue in the long run.
Instead of saying turnover is at 19% within a particular group, for example, demonstrate that the upcoming increase in turnover will likely impact 3% of revenue, or $3 million. If you lead with that kind of information, you will have a captive audience that is more receptive to hearing about your proposed solution.
Shifting the Burden of Collaboration
ONA has emerged as one of the hottest topics in HR — and for good reason. Without it, the people who truly deserve to be recognized within an organization can be overlooked. When a solid ONA investigation premise is combined with multi-dimensional analysis that combines data from several systems, you can gain powerful and actionable insights that will help you design the best interventions and get the support of relevant stakeholders. In this way, you can alleviate the burden of collaboration — and keep your best stars shining bright.
Massive value can be attained when businesses use data to understand and optimize their workforce, as well as to outsmart and outperform their competition. When data is gathered and analyzed from the entire employee lifecycle, we gain a much clearer picture of the levers to pull that improve employee retention, productivity, engagement, and more. Below are a few examples of how using data and people analytics can improve results.
Develop Better Employee Retention Programs
To find the source of employee turnover and curb its effects, HR leaders don’t have to rely on subjective anecdotes or gut feel. Turnover rates are often significantly higher in one group than in the overall employee population. To determine if you have a turnover problem, use people analytics to look at your turnover rate and drill down by role, regions, teams, and departments.
What’s normal for one group may be unusual for another, but a good rule of thumb is that if you see an increase in voluntary turnover by 3-5%, something is amiss. From there, use data on resignation drivers to uncover why turnover is occurring so you can develop retention programs highly targeted on solving the problems of this specific cohort and incentivizing these employees to remain in their positions.
Create Fair and Competitive Job Offers–Without Breaking the Bank
In a tight labor market, more managers are losing perfect candidates at the offer acceptance stage. This can be draining for the manager and expensive for the organization. At times, increasing the dollar amount in the offer can provide the incentive needed to land the right talent. However, it is not always necessary.
One way to use data in this situation is to find out how the offer compares to the compensation levels for people who perform the same role, at the same level, and in a similar geography, as well as people who hold similar positions but perhaps work in different functions.
Combine this with data from any pre-hire performance assessments to determine whether the candidate has the potential to perform exceptionally and grow with the organization, therefore, justifying a higher salary offer.
Make Career Pathing Conversations More Productive
Many organizations don’t have defined career tracks anymore, either because the ones they have are no longer relevant or because the next rung of the ladder is simply still undefined. This can leave employees frustrated and wondering what kind of future they have at their organization–and whether they should start looking elsewhere for the career growth they need.
To help both managers and employees see where the potential career paths are, dig into your historical data to see which roles others have progressed into, and how long it took them to get there. If there are multiple steps involved, this can be calculated by adding up the average time in each step.
In practice, this means if you see any commonalities, such as female business analysts eventually getting into computer forensics or other cybersecurity roles, you can then encourage line managers to broach the idea of that possible career move during career coaching conversations. The goal, of course, is not to push people into these roles, but to let them know where there are opportunities for growth in the organization.
Support Data-Driven Decisions with Technology
Sierra-Cedar found that large organizations can have up to 75 different HR and business systems, each one producing hundreds of valuable data points every day. Pulling reports from each system, and then combining these together, can be a long, manual process.
To be successful in leveraging all this data for insights, it’s critical that you–and the business leaders you support–have a way to mine for insights quickly and correctly so you can make the best calls for both your organization and employees. Meaningful analysis of employees must be on demand, comparative, and reflect that employees develop and change over time.
Working with data can be daunting for many, but there are people analytics solutions that make analytics more accessible so it’s faster and easier to get the insights needed for all your important talent decisions. Analytics enables you to confidently double down on your strengths and eliminate the areas where you’re weakest so you can meet every talent need of your organization.
One of the most common models in HR
analytics is shown in Figure 1. The model starts with the data, then you draw
insights, and finally tell a story. This isn’t entirely wrong; it’s just not
the best way to approach analytics and it’s caused some heartaches for HR
professionals trying to do the right thing.
Figure 1. The hard way to an analytics story
Here is how this model usually plays out: An HR professional is given a report, usually data drawn from the HRIS, and is asked to look at the data, derive an insight, then tell a story. Normally, HR professionals find this very hard to do, not because they lack the right competencies, but because it’s genuinely difficult to find legitimate insights in rows of numbers and figures. Even if you were a data scientist with lots of data, tools, and time, it would be a challenge to find insights that would have an impact.
The secret to success is this: The story
lies in the business issue, not in the data.
The story lies in the issue–not the data
Let’s imagine you have the following
business issue: customers at a consumer electronics chain complain that the
staff don’t know very much about the products. Everyone seems to have an idea
why this is happening: Some think training is poor. Others think customers have
unrealistic expectations. The regional head thinks it’s because turnover is
high, which has left the stores with inexperienced staff who don’t know the
products. The regional head is pushing HR to improve retention so that stores
have more highly experienced staff.
We now have the right starting point for
analytics: a business issue followed by several hypotheses about the cause of
Since the regional head is the senior
leader in the area, we might test her hypothesis first. HR pros are asked “Is
there any data showing that inexperienced staff generate more complaints than
HR may be lucky and have complaint data for
each associate so that they can directly test the hypothesis. However, it’s more
likely that such fine-grained data will not be available. If this is the case,
they might instead look at the number of complaints by store versus the average
tenure at each location. This isn’t perfect, but let’s see how the insights and
story might play out.
If the regional manager was correct, then
we’d expect stores with fewer experienced staff to have more complaints. But
imagine that the data shows little difference. Now HR should go to the regional
manager and tell this kind of story:
Issue: Customer complaints
Hypothesis: Inexperienced staff are the
source of complaints
Decision we need to make: Whether to
make a significant investment in retention
Data: Complaints by store plotted
against average tenure at the store
Insight: Tenure doesn’t appear to have much
impact on complaints
Conclusion: We can’t be sure, but it
appears likely that a big investment in retention would not solve the problem
of customer complaints; something else is causing the problem.
This list of bullets is the story HR is
telling. The regional head is sitting on the edge of their seat because they
care a lot about the business issue of reducing customer complaints. HR had
little trouble drawing an insight from the data because they knew exactly what
hypothesis they were testing. The story flows from the business issue, which
then guides the data collection and analysis.
Why is the Data-Insight-Story model hard to give up?
If it’s much easier for HR to start with an issue and derive the story from that, rather than staring blankly at a page of numbers hoping for insight, why is the Data-Insight-Story model so popular? I’m guessing here, but I think there are two reasons:
1. The Data-Insight-Story structure is a great storytelling model It sounds cool for a food company to talk about how they combined big sales data with big weather data and, in looking for patterns, derived the insight that soup sales rose after a cold snap. It’s more likely that they started with the hypothesis that soup sales might be related to cold snaps, then looked at the data to see if that were true. The “we started with a hypothesis” version is more accurate; just not as exciting.
2. Analysts may be unaware of the first steps in their thinking Ask a financial analyst about their work and they’ll say they look through the numbers for patterns to derive insights. What they don’t realize is that, based on their many years of experience, they have a whole lot of business issues and hypotheses in their mind as they start going through the data.
They are not just randomly looking for
patterns in data, they are seeking answers to specific questions such as “Cash
flow is an issue. I wonder if we’re having trouble collecting accounts
payable?” They are actually starting with the issue, then seeking insight from
the data, which leads to a compelling story about how to improve cash flow.
Whatever the real reason for the popularity
of the Data-Insight-Storymodel, the
truth is that it’s a hard way to go. In my work in analytics, I’d say this is
the most useful insight I’ve had: When you are working on an issue people care
about, then it makes the whole process much easier and plays closer to the HR
professional’s strengths. When they are clear about the hypothesis they are
testing, you will find them much more
capable of drawing insight from data.
Many HR professionals are bombarded with blog posts and emails reminding them that if they don’t do something about people analytics, then they are missing out. It shows up in every trend report and “prediction” newsletter. According to Deloitte’s 2018 Global Human Capital Trends study, 84% of HR and business leaders view people analytics as important or very important.
But if so many people think analytics is so important, then why do business leaders struggle to find the time and money to properly invest in this capability? Why do organizations move slowly rather than prioritize it over other investments?
There are two primary reasons for this:
It’s a net new capability for HR: In the past, investments in HR technology have been about automating repetitive or recordkeeping tasks — “traditional” HR work. People analytics is a net new capability, and it’s hard to place a value on a service HR has never experienced before. There is plenty of precedence for this outside of HR. Consider a CRM solution such as Salesforce. Years ago, it was a “nice-to-have,” but now an executive wouldn’t even think about running the business without it.
It’s a business investment, not an HR investment: The biggest returns from people analytics are not felt within the HR function’s budget (which pays for it). The returns are felt at the enterprise level. This is a tricky issue, as HR investments are typically funded by replacing existing HR technology or liberating HR headcount. The monetary ROI doesn’t come back to HR (even if the strategic capability ROI does).
“People analytics is a net new capability, and it’s hard to place a value on a service HR has never experienced before. There is plenty of precedence for this outside of HR. Consider a CRM solution such as Salesforce. Years ago, it was a “nice-to-have,” but now an executive wouldn’t even think about running the business without it.”
3 Elements of a Successful People Analytics Business Case
It takes more than a few Moneyball references (although they don’t hurt) to get buy-in from the C-suite and leaders within HR. Those who are most successful at gaining support for people analytics acknowledge that it can’t be self-funded by HR. Instead, they ask for an investment in the whole business.
If you are a people analytics champion, you need to build a business case that is compelling (appeals to many stakeholders and connects with financial outcomes), complete (covers all the options for delivering analytics), and durable (can compete with investment requests coming from outside HR). Here is how to execute each of these three elements:
#1. Align Multiple Stakeholders
Before you address all the competing interests and tackle the C-Suite, make sure you have all of your HR stakeholders aligned.
Through conversations (and not just a PowerPoint presentation), help HR leaders understand how an analytics capability improves both the efficiency of how work gets done in HR and the efficacy of the advice that HR gives to the business. Here is how this might look for different groups within the HR function:
Diversity: finding the unseen patterns that actually prevent females from entering leadership roles.
HR Operations: getting visibility into historical data or delivering what the HRIS can’t.
Learning: understanding which learning choices actually impact retention or performance.
HRBPs: gaining the ability to support decisions in real-time–without having to wait for another report extract and data validation.
When providing practical, tactical examples, break down the use of analytics to explain how surfacinga new insight will compel a people leader to make better decisions, and how the collective sum of those new outcomes contributes to an overall improvement in business outcomes.
For instance, one of our hospitality customers justified their analytics investment because they wanted to change which candidates got offers from hiring managers. Conventional wisdom led managers to hire people with previous experience in hotels, restaurants, or casinos. Anecdotal evidence, however, showed that people without previous experience stayed longer and performed better.
Backing up this anecdotal evidence and getting people to buck conventional wisdom was hard. HR knew that restaurant managers responded well to the data points that connected to cost and revenue trends. HR wanted to make hiring outcome data easily available so managers could watch the progress of these small decisions move the turnover trend.
The company now uses our people strategy platform to look at the common attributes of people who were quitting and putting the results into the hands of people who can actually change the outcomes.
In addition to tactical examples like the one above, strategic examples are also essential. Good strategic examples will emphasize the following:
The personal impact a CHRO needs to make on the business.Acknowledge the frustration around easily delivering compelling people data and defending data accuracy. Paint a vision for providing fact-based, numerical guidance that charts progress against strategic people goals; delivered with the same gravitas as the CFO’s monthly financial reports. (Check out this blog post to learn more.)
The CHRO’s need to influence at scale. Sending out memos, cascading messages through HRBPs or doing town halls is analog. Real digital HR uses a regular cadence of data sent directly to hiring managers, collapsing the distance between the CHRO and every people leader. The actual moment that changes diversity outcomes, for example, happens long after someone has been on a training course. Putting analytics close to the next hiring or promotion moment shows how an urgent choice connects to an important issue. This is a powerful way for the CHRO to provide advice at scale.
It’s important to be humble about your approach and acknowledge that there are many ways to provide the technology and team that delivers analytics.
Get ahead of the issues by showing the relative cost of each approach, the level of content that can make an impact, and the time it takes to get that content into the right people’s hands. Savvy analytics investors frame out all the options from the beginning and focus people on the fastest path to value rather than the politics of sunk costs.
You will encounter several traps along the way. “The ERP system can do this” is a common one. When someone says this, consider telling them the following:
Transaction systems are not built for holistic people analytics. Once we start delivering people insights, managers will start asking better questions. Questions that start with turnover turn into questions about which turnover really impacts our customers. This means going beyond the data available in a single ERP. HR recordkeeping systems are not built for consuming large amounts of business data. They don’t scale to bring in ATS, CRM, and engagement data. And worse, every time we want to answer “better” questions we will have to pay a consultant and do another change order with the vendor.
“Let’s just build a few visualizations on top of the data warehouse” is another trap. Consider making these statements when you encounter it:
How long does it take to build a new visualization? What’s the cost to maintain it? To answer the drill-down question?Most organizations have a queue to add new data sources, cleanse them and answer new questions. And experimentation (answering the wrong question to get to the right question) can be very expensive without a scalable approach. Often data scientists take up 60% of their time with routine reports because the tools are built for experts, not business generalists.
Deployment is not the goal–behavior change is. A platform that comes with pre-built content enables leaders to start gaining insights more quickly so they can collapse the time between implementation and results. Nobody wants to wait nine months to see the first results of a data warehouse project, and then another six to see behavior change in something like employee turnover. Business leaders need insights at the speed of business–we definitely don’t want to wait a year to see always up-to-date data when doing M&A integration.
#3. Demonstrate ROI That Can Compete With Other (non-HR) Investment Priorities
In the end, a people analytics investment needs to pass muster with the CFO, COO, and CEO. And it won’t just be measured against other HR requests–it will be assessed against requests made by sales, operations, and every other department.
This compels you to demonstrate how data-driven people decisions contribute to improved enterprise performance, whether it’s in the form of increased revenue, better employee retention, or enhanced customer satisfaction.
C-suite leaders are looking for “big bets” that can make a meaningful impact in financial performance or can de-risk the execution of a strategy. When communicating with executives, follow these guidelines:
Make sure part of your investment links to one or more meaningful, multi-year financial impacts. Frame your return in the language of investors but show the impact on EPS, EBITDA, NPS, or whatever makes sense for your company.
Don’t shy away from demonstrating a three-year return, as investments that move the needle in a meaningful way might not yield a payback in the first year.
Don’t hang your hat on one simple measure like improving turnover. Connect with business issues specific to a division, product or region. This helps syndicate support beyond HR.
That being said, the notion of a single capability for the enterprise can still be compelling for the CEO and CFO when framed correctly. Nobody will argue that the investment in leadership development, for example, is important. It’s built on the premise that when new behaviors are repeated with enough frequency, they have a big impact.
Consider how, when a professional baseball team hires a batting coach, the focus is on getting one better hit per player per game. When enough players make and sustain these small improvements, the team starts outperforming.
Always reinforce the very simple premise of people analytics: Deliver insights. Get them in the hands of people who will take new actions with those insights. Get people to take new actions with enough frequency to yield a meaningful outcome.
There is nothing more satisfying than seeing an executive’s eyes light up when she sees how small changes here and there can add up to a larger strategic outcome.
Get Everyone in the Right Headspace for People Analytics
A successful people analytics investment means overcoming three decades of entrenched beliefs about HR investments.
It means shifting away from investments that simply make HR more efficient recordkeepers into funding a capability that delivers insights and predicts outcomes.
It means moving away from classic technology-driven projects with go-live dates to an agile approach that is always consuming new datasets and publishing guidance.
Finally, it means moving from report requests fulfilled by many people and multiple systems to placing analytics into the hands of people who make decisions.
When all this is taken together, it adds up to a solid case for people analytics that demonstrates value –not just for HR–but for the entire business.
Over the past year, the full power of people analytics has come into focus.
More organizations climbed up the people analytics maturity curve and gained new insights into business-critical trends related to employee job progression and performance. At the same time, Organizational Network Analysis (ONA) applications entered the spotlight. These tools enable businesses to track and analyze employee data from a variety of sources, including sensors and the IoT.
But with great power comes great responsibility. People analytics is proving to be too valuable to be ignored, and yet, organizations must also consider some key ethical and legal implications.
For our top pick of articles for 2018, we focus on two central themes: how people analytics can elevate the entire business, and what leaders must do to remain conscientious when harnessing its potential.
Data storytelling is “a lightweight way to build trust among stakeholders and bring behavioral science to culture transformation,” state the authors of this HBR post. Here, analytics experts reference several real-world examples to show how people analytics can drive transformation.
Change management is an often overlooked — but important — opportunity for people analytics to add value within an organization. In this blog post, people strategy consultant Morten Kamp Andersen highlights how analytics can be used to build engagement about a practical path forward when organizations are embarking on new initiatives.
When building a people analytics capability, it can be easy to forget buy-in. This post by organizational strategy expert Giovanni Everdui demonstrates why getting senior level and HR community support is key to analytics success.
In this McKinsey article, the firm’s researchers share advice from executives of leading companies who have grown strong data cultures that amplify the power of analytics. It’s a must-read for leaders who are looking to prepare key stakeholders for a major transformation in the way the organization gathers and acts on its people data.
While the number of businesses taking advantage of analytics may be steadily increasing, the average person’s understanding of analytics remains vague. In this Visier blog post, we focus on the differentiators between reports and analytics, demonstrating how analytics can be a much more powerful business transformation tool than reports could ever be.
There is an important distinction between merely having analytics — and using them to get results. In this post, business management expert John Boudreau provides HR leaders with practical advice on how they can deliver impact with people data.
“The key to motivating senior leaders into acting on data is to turn those numbers into a narrative.” This is the main piece of advice given by Kim Saunders, senior people analyst for the British Office for National Statistics, at a CIPD HR Analytics event in London. This article provides an overview of several key takeaways from the conference related to driving business impact with people data.
Featuring advice from experts interviewed at the recent Work Rebooted conference held in San Francisco, this is a must-read for those who want to better link analytics to outcomes such as business performance and customer satisfaction.
In this HR Dive piece, the co-founder of a community for people analytics professionals emphasizes that people analytics should help HR indirectly on the path to a larger strategic outcome. The article also provides advice for those who are just getting started on their analytics journeys as well as a few reminders for more advanced users.
For the data scientist who authored this post, one of the best parts of working with data is “knowing that the work that you did help to make a decision.” Here, he demonstrates how having a data strategy ensures that data is managed and used as an asset — and not simply as a byproduct of the application.
This TechTarget article highlights analyst Stacia Garr’s observations on the role of tech in the #MeToo movement. Specifically, it references how data can measure the impact of programs intended to combat harassment and alleviate workplace diversity issues.
With Brexit and likely immigration restrictions, British employers could face huge skills deficits by January 2021. This Personnel Today article is a good read for any HR leader who is confronted with a confusing labor market and wants to leverage data to chart a path forward.
This article provides an overview of an emerging discipline called relational analytics, which can help businesses identify which key players they need to retain and where silos exist in their organizations. Here, the author outlines the “six structural signatures” of relational analytics as defined by academics Paul Leonardi and Noshir Contractor.
ONA is witnessing a resurgence, thanks to advances in technology and other drivers. In this post, people analytics leader David Green covers the basic definitions of ONA and some tips for those who are looking to harness its power.
In this article, the Chief Talent Officer for General Motors argues that organizations must bring out the best in people, and not just hire the best people. This is a good foundation for understanding the strategic context of ONA.
“As organizations start to use people data in earnest, new risks, as well as opportunities, are taking shape,” states this article summarizing results from the Deloitte 2018 Human Capital Trends report. On this front, the article provides practical advice for how to execute people analytics while being mindful of legal and reputational implications.
As a comprehensive law that is designed to give Europeans more control over their personal data, the GDPR contains significant requirements. It also contains some nuances that may surprise HR pros in terms of collecting employee data. This Visier Clarity blog post outlines seven important facts about the new law that every HR professional should know.
“Just because you can measure something doesn’t mean you should,” writes the president and CEO of Humanyze, Ben Waber, in this HBR post. Here, Waber shares his playbook for the ethical, smart use of employee data — particularly when gathering granular information about employees from sensors and the IoT.