Chapter 5
Why Analytics Comes First
Marketing has gone through a remarkable transition to become a function obsessed with analytics. The CMO of People model mirrors that transition. It puts analytics first so that HR can focus on affecting the business.
Why Analytics Is a Priority
Do you add analytics capability after HR is set up or does HR analytics come first?
Normally, a growth company would hire HR business partners to start getting the work done, and only later hire an analytics team to measure the work. In the CMO of People model, you would hire the analytics team early on. At DocuSign, we hired a full-time analytics person right after we had a talent acquisition team, a total rewards leader, and a technology professional online—and we would have filled the role earlier if we’d found the right person sooner.
Analytics comes first because HR can’t be part of the core leadership team without having the numbers. The CMO of People can’t say, “I feel that talent acquisition is going well, all things considered,”—they must have data on the recruitment pipeline. They can’t say, “We have a great onboarding system”—they must have data to back this up. This need for data is no different for any other business function.
This is not to say that HR business partners are not a critical element of the HR team; however, in a new company where difficult choices need to be made about priorities, analytics comes first. As the company matures, the usual HR roles will be filled in—because analytics will have already been in place as the roles are filled, they should naturally adopt the habit of using data to support decisions. HR won’t have to unlearn the old “data-free” way of operating to become analytics savvy; the new hires will grow up that way.
Key Reasons Why Analytics Comes First
Credibility, confidence, and collaboration are a powerful combination for maximizing business impact. These “three C’s” are best established from a basis of facts that ultimately lead to a clear, realistic plan for the future. That’s why investing early and often in a high-caliber people analytics team at any organization size can be the difference between a good HR team and an amazing one. Simply put, moving from feeling to knowing develops credibility; knowing your customer allows you to operate with a high degree of confidence and, concurrently, your colleagues’ confidence in your service growth, leading to high-impact collaboration. Even from the most basic reporting, you can learn and make investment decisions. Remember, knowing your customer and measuring what you want to become will drive accountability and results.
In a mature company that did not have the opportunity to add analytics capability early in its history, the “analytics-first” mindset shows up as prioritizing sufficient investment in analytics that the HR side of business issues are always grounded in data. This is not about making a huge investment in advanced technology; it involves demanding that HR must come to the table with numbers—and that’s a matter of both capability and culture.
In Section 2.4 on the CMO of People’s priorities we introduced the concept of “measuring what you want to become” which stands in opposition to the more common practice of sharing what data you happen to have. This follows from the marketing concept of “knowing your customer.” The customer for HR is the business leader—they don’t particularly care about HR metrics such as how many courses were given or how many positions were filled unless it directly affects the results they’re accountable for. If quality of hire metrics is important to the business, but you don’t have good data, share what you have anyway. The business will be happy to have something rather than nothing, even if there is considerable uncertainty, and everyone will be motivated to improve the data collection in the years ahead.
How to Get Started on Analytics in a Growth Company
The key to getting started on analytics is being willing to present the data you have, even when it’s far from perfect. It’s almost always the case that some data is better than no data, and if you wait until you have the perfect analytics, it will be many years before you have anything to show the leadership team.
The people you hire to do analytics need a background in quantitative work, such as experience as a business analyst or training in economics. They don’t need to be data scientists—just good with numbers.
Pointing the Analytics Team in the Right Direction
The analytics team exists to help the CMO of People make decisions and effectively tell the story of those decisions. For example, the CMO of People will be involved in decisions about where to locate new operations. You can’t make those decisions without some data on, for example, the cost and availability of talent. That’s where the analytics team comes in.
Imagine going into a meeting where the top team is looking at different locations and the head of HR says things like, “I believe the quality of talent is higher in location A,” and “The cost of labor is bound to be much cheaper in location B,” and “It’ll be a longer trip to visit location C.” HR can’t be credible if they step through the presentation without numbers. The analytics team provides the numbers to support a sound decision as best they can, given the resources at their disposal.
There are a couple of points to note in this example. First, the analytics team isn’t being asked to create a sophisticated model that will pick the best location; they are providing a mosaic of numbers that help to tell an overall story. Second, the analytics team is not off in a bubble somewhere “doing analytics”—they are by the side of the CMO of People, helping to answer an important business question.
Test and Iterate
It’s natural to want to impress people with HR analytics; however, when you are starting out, that shouldn’t be the goal. The idea is to get some numbers, put them out there, find out what is useful and continually improve. Especially in a young company, the feeling should be, “Look, this isn’t perfect, but it’s what we have. Does it help?”
Test the value of HR analytics by putting the data in front of decision-makers, and then iterate to constantly improve.
Design Perspective
In analytics, the design perspective keeps us from getting dragged into the weeds about tools or data sets to the point that we lose sight of why we are doing this. The point of analytics is to help the executives get stuff done, which means that analytics must always be focused on what they need to know to make better decisions.
For example, an analytics team will often produce data about how many people are dropping out at different stages of the funnel. You might think that I’d be thrilled with that information because the funnel is a dominant part of the napkin diagram. It told me, “Yep, a lot of people are dropping out here,” but unfortunately, it gave me no idea about why or what to do. All the recruiting folks had different theories about the problem but there was no way to know what changes to prioritize.
With a design hat firmly on your head, it becomes obvious that “How many?” isn’t enough—we need to know why, which means gathering information such as manager and candidate experience feedback.
This design perspective avoids the common analytics trap of producing a lot of numbers that leave managers saying, “Cool, but so what?” The design focus on the bigger picture gets us to confront the “So what?” before we begin designing data collection or reporting.
What Can You Do Today?
Ask yourself if you have the resources to go into meetings with the basic analysis required to inform a decision. If you don’t, then you have a case for reallocating resources from less important work or adding resources by hiring someone to focus on this type of analytics.
An Analytics Dashboard
If analytics is just a report full of numbers, no one will care. It has to answer questions.
Let’s get into some of the specifics of what a talent analytics dashboard looks like. Figure 5.1 provides a full view of the talent analytics dashboard. Here are some essential dashboard design elements:
–The first thing to notice is that it uses the end-to-end employee experience as an organizing principle: it starts with the “top of the funnel” where talent is brought into the company, then into the cycle of learning, performing, being rewarded, and so on.
–Second, it is all pointed toward business impact metrics. As I said in the introduction, the CMO of People has shared accountability for business outcomes. An HR dashboard that doesn’t include business measures would be misguided.
–Finally, the dashboard is organized around questions, not metrics. The questions arise from a discussion about the important things a manager needs to know instead of the usual approach of gathering a lot of numbers and hoping that something a manager needs to know is buried somewhere within them.
Now let’s take a more careful look at what is in this dashboard. Figures 5.2 to 5.4 show the individual pieces so that you can study these in more detail. Let’s start with the “Top of the Funnel” in Figure 5.2. and work our way through the dashboard.
When we talk about the talent funnel, we’re talking about how one starts with a broad pool of possible candidates for a job and then slowly funnels them down from many applicants to a long list of screened candidates, to a short list we want to interview, until we reach the final individual hire. It is very similar to what marketing does in acquiring customers.
In the domain of the talent acquisition function, one of the first questions we ask is “Do we have a healthy talent pipeline?” That’s a broad question, but it focuses on whether or not we’re going to be able to meet the talent needs of a growing organization. There’s no single number that answers that question; fundamentally, we want to track how many people we have at each stage of the funnel and from there assess whether we are confident that we have enough people coming through the process that we will have the right number of good quality candidates at the end of the day.
The next question is “Are we filling positions when they need to be filled?” This relates to the more common metric of “time to fill,” which tracks how long it takes us to fill a position from the time a requisition is given, to talent acquisition, to the time someone is on the job. But the question here isn’t focused on the talent acquisition process—the question is about the business need and so it comes down to this: Given the needs of the business, is talent acquisition meeting these needs? Of course, one of the elements of meeting this need is how long it takes to fill a position, but that’s only meaningful in relation to when they need to be filled; if the job is filled quickly but still not quick enough, we have a problem.
Next, take a look at this question: “What are the implications of missed and delayed hiring?” This is very much a business question, not a question about HR processes. The reason we’re asking this is that if the implications are small we won’t invest unduly in fixing them. Imagine a retail store with ten cashiers and we’re short one. Yes, delayed hiring has left a vacancy, but it won’t have a big impact on the store. On the other hand, if you have a new product coming off the production line and it can’t be shipped for lack of a quality assurance expert, then that begins to have serious implications for the business. With this kind of question, you answer it in a more subjective or qualitative way than you would with some other metrics. That shouldn’t prevent us from asking the question, because a subjective answer is better than no answer.
The next question is “Are we cost-efficient with hiring?” This is similar to the traditional “cost per hire” metric, but again notice there’s a different angle on this—that difference helps illustrate how a businessperson thinks versus how, perhaps, HR is taught in colleges. What we really want to know is whether we’re being efficient with the money we’re spending, which is not the same as spending as little as possible. It’s about making the best use of that money to get the business results we need.
Then we move to the question, “What are our best sources of hire?” which is a classic talent acquisition metric. It can be trickier to answer than you might think because sometimes candidates use several different sources of hire before they end up on your doorstep. Nonetheless, if a candidate is using several different job boards, you want to know which ones are most effective; if a potential hire is looking at campus recruiting rather than a job fair, again, you want to know which method is delivering the best talent in the most efficient way.
Next, rather than using the common metric of “quality of hire,” we reframe that more broadly as “Are we hiring the right people?” which, again, is a subjective question. Part of the answer will come from discussing with managers whether we’re bringing in the quality of people they need, and it also can extend into other questions. For example, perhaps we hired someone who met the job specification but in fact that job specification wasn’t really what the organization needed—that means we weren’t hiring the right people. All of this gets bundled into that broad question.
We then ask, “How is our candidate experience?” As I’ve explained, the employee experience—which we extend to include prospective employees—is a critical part of getting the best performance out of an organization, so we measure candidate experience at various stages in the process. With these measures, not only do we know if their experiences match what we want to deliver, but if they are falling short we also know where they are falling short—thus, we can take action to fix them.
And the final question in this series is “Are we efficient with hiring?” which in this case was how long it took to fill jobs (i.e., the classic “time to fill” measure).
In the “Demographics” hexagon below the Funnel questions, we have some traditional contextual information about whether we are meeting our diversity goals and a look at how employees are distributed regionally. Leaders need this contextual information to keep a clear picture of the company at the current state, which is particularly important in rapidly growing global companies where you may find things change quickly. For example, at DocuSign we went from what was a predominantly US workforce to a global workforce, and sometimes managers needed the dashboard to remind them just how much had changed.
The talent lifecycle metrics as shown in Figure 5.3 are divided into some of the major areas of responsibility within HR: learning and development, performance, rewards, career growth, retention, the organizational structure, and, at the center of it all, engagement as a unifying goal. I won’t walk through each and every one of these questions, but we’ll discuss many of them so you can get a sense of the thinking that led to the dashboard.
If we look at “Learning and Development,” you’ll want to begin with the basics of just knowing whether you are actually successfully completing the core activities. Are people taking the courses and completing them? That basic information is easily tracked. At a deeper level, we asked, “Do the courses affect performance?” Now this is a notoriously hard question to answer, but it is of course the most important question of all—so it goes on the dashboard even if at times we struggle to answer it. We want to become an organization where we’re clear about how courses affect performance, and in the absence of hard numbers, we can collect subjective data from managers, employees, and trainers. We’re looking for clues that there’s a link between the training employees get and their subsequent performance.
In the “Performance” hexagon you’ll see that the focus is on revenue and sales. That focus contrasts what you’d normally see in an HR dashboard on performance management, where the questions are usually operation metrics such as how many people have completed their performance appraisal forms. What we’re capturing with these questions is what is most important to the CEO at that point in time. You’ll notice the question, “How are we delivering to our product roadmap?” is one that falls completely outside of normal HR and the answer would be found in some other department. Yet ultimately the point of performance management is business results such as hitting those deadlines, so that’s what we focus on in the dashboard.
With “Rewards,” we start with the basics of “Are salaries competitive?” That’s something we need to know, and we get that by looking at salary surveys. With the next question you can see that we’re very interested in how top performers and high potentials are being retained; we’re zooming in on what’s most important to the organization. The final question in the set has to do with the discipline of the reward process. You may find in many organizations that there is a policy for giving merit increases and then when you look at the results at the end of the year, somehow the actual amount distribution of increases doesn’t match was planned. Asking, “How have actual rewards compared to target rewards?” is a simple check on whether we’re doing what we say we intended to do.
Career growth looks at the usual mobility questions about whether we are moving our employees laterally. Those metrics are easy to gather but notice how the questions are phrased in everyday language that a manager would recognize, not in terms of the HR concept of “mobility,” which perhaps disguises what people actually care about.
Next, let’s consider what we we’re interested in when it comes to retention. First, we look at how our attrition is trending. What’s interesting here is that often, managers are less interested in the absolute number than the trend; if that’s what managers are using to make a decision about whether they need to take action on attrition, that’s the number we’re going to give them. We follow that by asking the crucial question, which is not so much how many people are leaving, but whether we are keeping our best people. We moved from there to asking why we lose people. We’ll get part of the answer to that question from exit interviews. Finally, we ask what attrition is costing us. Here, we want to be truly business-savvy about how we make that estimate—not just come up with the highest number we can think of, which is often seen in HR textbooks. We want to truly understand where turnover is hurting the business, or if it is hurting the business at all.
The set of questions on “Organization Structure” reflects the issues that a growing company of our size was likely to face. One concern is becoming top-heavy—having too many managers and not enough workers. You can just potentially eyeball the answer by looking at an organization chart, or else use numbers of senior, mid-level, and junior staff. However, imagine if you just presented a table of numbers of staff by grade level—that wouldn’t mean much to managers. We need to frame it as the business question of whether we’re top-heavy, and then everybody will know why they’re looking at the data. Just as being top-heavy is a common problem; when the organization is changing rapidly, spans of control can become too narrow or too wide, and it’s easy to add too many layers or to have a layer missing. For the dashboard, it is less a matter of just producing numbers as it is focusing people on the important questions. Finally, the dashboard asks if we are gaining economies of scale as we grow, which is an issue that can plague a young, growing organization.
For the final, central hexagon, “Engagement,” we asked the obvious questions of what engagement is and what drives it, and then we turn to look at engagement from outside the company by seeing what it says on Glassdoor.com.
We have also a couple of boxes at the bottom of this series of hexagons to capture some important things, such as looking at the employee experience and HR service efficiency more closely. For employee experience, we look at orientation and onboarding. We also have a metric on something important to the CMO of People model: We believe the CSR activities are an important part of the company culture and we want to be sure that we’re creating that culture of volunteerism that is part of the brand promise we make to ourselves and to our employees.
Under “HR Service Efficiency,” we have an element the CFO would want to know, which is how our HR expenses are trending relative to the number of employees. Again, notice that the trend is more important than the absolute number. Next, because employee benefits are a big expense, we want any insights on whether that money is being used efficiently. Finally, something else you normally wouldn’t see in an HR dashboard is some data about the efficient use of facilities; this reflects the fact that in the CMO of People model, facilities fall under HR.
The business impact hexagons at end of the dashboard, as shown in Figure 5.4, get right to the point of HR. The point of HR is not to perform a series of activities—it’s not even to do things like drive employee engagement. Ultimately, the point of HR is to have an impact on the business. Whether or not it is easy to answer these questions on the dashboard, these are some of the most important questions for HR and should be prominent as part of the normal reporting activities. All of these questions on business impact will be answered in a partly subjective way, which is fine.
Just to round out this explanation of the dashboard, we use the common coloring scheme of red, yellow, and green so that we can highlight any areas where there are problems. Notice the addition of a fourth color, blue, for data not available. The use of this fourth color is to encourage us to ask the right questions even if we’re not currently in a position to answer those questions with any confidence.
How We Used the Dashboard
As head of HR, I spent what might seem like an inordinate amount of time with the dashboard; that’s because it was a key tool for quickly grounding discussions in fact. For example, a question like, “How does this affect top-line revenue?” would quickly move discussions away from secondary matters and focus attention on the issues that were driving business success.
The dashboard also enabled me to hold my team accountable. For example, starting at the top-left of the dashboard, I’d want to see evidence that my team had built a healthy talent pipeline. The team knew I’d be looking at this, and they’d know I’d be reviewing data, not just looking for overall assurances.
More often than not, trends mattered most to us. The trends told us if something needed to be changed and helped us to predict outcomes.
Going from the Initial Dashboard to a More Advanced Version
There is no magic measuring stick behind the questions in a dashboard. For a question like, “Do we have a healthy talent pipeline?” we’d look at data, such as the number of candidates at each step in the talent pipeline for various departments (e.g., Sales, Engineering). We’d then make a judgment as to whether it looked healthy or not.
When you start out on a dashboard, there will be missing data, inaccurate data, and data that, while correct, is misleading. The trick is to have the courage to get the data you have in front of people and start making judgments on what it means. Over time, you test, iterate, and get better—although you’ll never get to the point where you can dispense with judgment in interpreting the data.
A more advanced dashboard begins to look at linkages between different types of data. For example, in an early dashboard, you might have data about how often managers are having coaching conversations with employees, as well as data about on-time delivery of projects. In a more advanced dashboard, you might present data that shows whether there is a relationship between more coaching and better on-time performance.
There are many analyses you can do—however, as a rule, look for analyses that answer a specific question, such as, “Is this activity delivering the result we want?” or, “If we want more of this, what will get us there?” An early dashboard that shows how long it takes for someone to get through the hiring process is somewhat interesting, but an analysis that shows whether a long hiring process causes good candidates to drop out is very interesting.
What Can You Do Today?
Take an existing HR report and write out three or four questions that you wish the report would answer. What would it take to redesign the report so that it did a better job of answering those questions?
Top-of-Funnel Analytics for Talent Acquisition
Here’s how we use analytics for talent acquisition.
In rapidly growing firms, the top HR priorities are talent acquisition and onboarding. As a result, in the early days of a new firm, the analytics team will focus on those two areas.
Strategic Question 1: How Can I Prevent Bottlenecks in the Hiring Process?
In the firms where I led HR, an early issue was preventing bottlenecks in the hiring process. For example, we had a lot of candidates pre-screened, but the process was still held up because hiring managers were slow to schedule interviews. We looked at the hiring process in the same way that Sales looked at the selling process: It starts at the top of the funnel with prospects, and then looks at each step of the funnel until the time the deal is closed.
We’d look at where we were at each step in the funnel (e.g., how many applicants we had for a given job requisition, how many interviews had been scheduled, and how many candidates completed interviews). This let us know if there were any bottlenecks that prevented filling vacancies at the necessary rate.
We measured how long each step took. This let us know when we could expect to have vacancies filled and hence give managers reasonable forecasts on when they’d have their staffing needs met.
Strategic Question 2: How Can I Increase the Efficiency of the Hiring Process?
We also tracked the efficiency of each stage of the hiring process. How many applicants would we get from a particular source? How many applicants would make it through pre-screening? How many candidates would accept an offer? How many job requisitions could a recruiter fill?
By monitoring these metrics, we could see if parts of the process were inefficient. For example, if a job advertisement pulled in a lot of applicants, very few of whom made it through the initial screening, then it was probably not a very good advertisement. We could then continually improve the process.
Strategic Question 3: How Quickly Is the Sales Team Ramping Up Sales?
A growing firm doesn’t care only about how long it takes to fill vacancies, but it also cares about how long it takes people to get up to speed. In the firms I worked with, there was a particular interest in how quickly sales representatives could get to the point that they could hit their quotas.
This analysis enabled the business to accurately forecast revenue by looking at how quickly talent acquisition could fill sales rep jobs and how quickly they could become fully productive.
Notice that this question involved Sales and HR. Doing this analysis required partnership. It reflected the mindset that we were never doing “HR analytics”—we were always doing “business analytics,” which would often involve partnering with other departments.
What Can You Do Today?
Instead of thinking about what metrics to monitor, determine what business issues matter most to the organization right now. In fast-growing firms, analytics focuses primarily on talent acquisition; you might have a different business priority. Since analytics talent is a scarce resource, you must be confident that you have them working on the right strategic questions.
Lifecycle Analytics for Brand and a Predictive, Immersive Experience
The main goal in assessing the brand is to move toward a data-based assessment as opposed to relying on anecdote or gut-feel.
̶̶ Brad Brooks, CEO of OneLogin former CMO of DocuSign
Two of the most important questions we asked employees about their experience were:
–Does the workplace enable you to perform at a higher level?
–Do you feel inspired by the workplace?
Notice that the questions are not primarily about employee satisfaction. The first question concerns productivity. Remember that the whole point of the CMO of People model is to outperform the competition though superior talent management. If employees feel that the workplace isn’t enabling them to perform at a higher level, then the employee experience isn’t delivering what it needs to. The second question sets a similarly high goal—it’s not about having satisfied (or even very satisfied) employees. It’s about whether the environment inspires them to do their best work.
For example, at Shutterfly, we had a 300,000-square-foot manufacturing facility for production of cards, photoshoots, etc. It was tempting to save money by making this a bare-bones location. However, that wouldn’t have been consistent with the brand. We decided to make the workplace mirror the look and feel of the corporate office as much as possible and bring to life the mission of bringing joy into people’s lives. Engagement and productivity data reinforced the wisdom of this decision, because as good as data is, nothing beats a good anecdote. One Christmas season, a plant manager realized that some photo gifts wouldn’t make it to a customer in time for Christmas, so the manager drove to the customers house to make a personal delivery. If the company lives the brand, then so will employees.
One time at DocuSign we experienced a low engagement result related to employee enablement and access to information, which drove the immediate decision to invest in the infrastructure to scale knowledge management. One time at DocuSign, we got shockingly bad data from employees regarding whether they could perform at a higher level. It turned out that, due to rapid growth, we’d hit a point where people couldn’t find the information they needed. The informal processes for finding information weren’t working, so we built an intranet site to create a scalable solution for all employees. This is not a dramatic story, but it simply shows how a commitment to gathering data leads to action that improves productivity.
Other Metrics Used to Assess the Employee Experience
We used metrics at every stage of the end-to-end employee experience. For example:
–Candidate experience questionnaire. Get candidate feedback on their experience applying for a job.
–Close rates. What percentage of job offers are accepted?
–Net Promoter Score. This answers the question, “Would you recommend the company to a friend?”
–Turnover. What is the turnover rate and how does it compare to the industry average?
As always, with metrics we are not looking for simple answers—we are trying to paint a picture that will let us know if we are on track with creating an effective experience. If we’re not, then we’ll dig deeper to see what we have to fix.
It’s tempting to hope that one metric will reliably tell us the health of the brand. It’s unlikely that one such metric exists; we are usually better off assembling a mosaic of measures that builds a picture of how the brand is doing and what we need to work on.
Greenhouse’s Candidate Experience Questions
I have used Greenhouse recruiting software and they’ve given me permission to share the questions they use to assess their candidates’ experience. They focus on their NPS (Net Promoter Score) result, which is, “Overall, my interview experience was a positive one.” This answer summarizes the experience in an overarching way.
Using a “Likert” scale, they ask these three questions (which are asked in the form of a statement for respondents to agree or disagree with) that impact the net promoter score:
Do you agree with the following statements?
–I was treated with courtesy and respect.
–Overall, I have a more positive impression of the company having gone through their recruiting process.
–The interviewer(s) got an accurate sense of my strengths and weaknesses.
Additional topics we’d recommend asking in candidate survey questions are:
–Were things on time, organized, and professional?
–What are the candidate’s feelings about the actual experience?
–How well does the candidate understand the role and the organization?
Greenhouse thinks a lot about how companies can improve hiring outcomes, and measuring candidate experience is a part of that.
Note that these questions are straightforward. There is very little in HR that is technically very difficult. The issue is whether or not HR cares enough about the candidate experience to ask the obvious questions and then to act if the candidate experience was not a good one.
In terms of the employment brand, some useful metrics include:
–Employee Net Promoter Score (eNPS). The metric in its simplest form is the answer to one question: “Would you recommend this company to a friend?” It’s a clear, simple metric that captures a lot about what employees think of the company.
–Number of employee referrals. The number of employee referrals is a difficult metric that complements the Net Promoter Score. It can be seen as the answer to, “Did you actually recommend this company to a friend?” which is a step up from “Would you. . .?”
–Engagement metrics. Employee surveys often deliver a single engagement number, but they also have many sub-factors that allow you to understand why engagement is what it is. These sub-factors are useful for assessing the effectiveness of the brand.
–Exit surveys. The exit surveys provide insight into what, if anything, is amiss. Usually, the trends matter more than the specific points of data, and collectively they inform management if things are on track or if something needs to change.
It is not that the metrics are remarkable—what matters is using as many of them as you can, taking seriously what they indicate, and then acting on them.
What to Aim for in an Exit Survey
Ah, the elusive exit survey. Important as it is, it’s a piece of the puzzle, not the whole picture. Most exit interview processes or questionnaires aren’t unique, yet the company’s culture of openness and welcoming feedback will be factors in the degree of honesty a departing employee has. As the CMO of People focuses on enabling credible, genuine communications and demonstrated bias for action, the odds of more valuable, productive insights increase. Pair those insights with a leader’s ability to recruit new talent, along with their engagement results, turnover statistics, promotion rates, and so on, and you begin to get a more complete picture of a leader’s effectiveness.
Marketing Parallels
Measuring employment brand is very similar to what marketers call “brand-tracking.” Brand-tracking looks at a mosaic of metrics that are relevant to understanding how the brand is doing. For example, brand-tracking might measure factors such as:
–Brand awareness (e.g., Do customers recognize the brand? Do they recall it when it’s mentioned?)
–Brand usage (e.g., frequency of purchase; future purchase intent)
–Brand image (e.g., what consumers think about the features)
Pull all these together, and a company can make informed decisions on what it needs to do to strengthen the brand.
A small company might capture just a few brand metrics a few times a year while a large company might constantly track a large number of different measures. It’s a matter of doing what’s practical for the situation. The same thinking applies to measuring employment brand. You should gather as much data as you reasonably can; however, if you can’t get a lot, then, as Brad Brooks says at the start of this section, anything that moves you from the world of anecdote and gut-feeling to data is a good thing.
Frequency
DocuSign looked at employment brand numbers quarterly (with the exception of the employee survey data, which was annual). That is likely a good cadence for most firms. The numbers don’t change so quickly that there’s a need to do it more than quarterly—however, if something is going wrong with the brand, you don’t want to wait a year to find out.
An Illustration
It’s not the individual metrics that tell the story—it’s what a group of metrics collectively implies. For example, I worked in one organization where we noticed that in Engineering, the length of time to close a job offer was trending up, attrition was increasing, leadership ratings were falling, and referral rates were low. We might have explained away any one of those metrics, but as a mosaic they painted a picture that the employee experience wasn’t what it should be. Further investigation showed that engineers were burdened with too many menial tasks. We were able to outsource those tasks, which made sense immediately in terms of improving engineering productivity, and in the long run brought the employee experience back to where it needed to be.
What Can You Do Today?
Find out what metrics you are using to track employment brand and the employee experience, as well as how often they are reviewed. See if someone can remember a time when they used the insights from brand-tracking to make an important change.
Lifecycle Analytics for Corporate Social Responsibility
How does HR assess the effectiveness of the investment in corporate social responsibility?
Corporate Social Responsibility (CSR) is important to both the customer brand and the employment brand. How can you use data to track how well you are doing and to illuminate where you need to make changes?
Amy Skeeters-Behrens, Executive Director of Philanthropy at DocuSign, built the CSR program around three pillars: employee donations, employee volunteering, and product donations. To see if these programs were on track, she gathered data on the following:
1.Employee donations
○Percentage of employees requesting matching donations
○Which causes employees donated to
This showed if the matched donations program was popular. If it wasn’t popular, then it would need to be changed or cancelled. It also showed which causes mattered most to employees, which informed where additional direct donations might be made, or what other CSR activities they could do to support these causes.
2.Employee volunteering
○Percentage of employees who participate in using volunteer time
If the percentage of employees volunteering started declining, then that would be a signal that the program might be in trouble and something would have to change or that for whatever reason the employees cannot afford to take the time.
3. Product donations to nonprofits
○Number of nonprofits who were part of the donation program that were using DocuSign
○How extensively the product was used at these nonprofits, which would eventually give data on hours saved, water saved, and trees saved
Again, these metrics answer the fundamental question as to whether the donation program is on track. If numbers were low or trending in the wrong direction, then the company would take action. In the absence of data, it would be too easy to continue with the program because it was good in principle, or to cancel the program because no one was sure it was working.
One of the most revealing pieces of data was more qualitative than quantitative. On the employee survey, employees were given a chance to single out initiatives they were particularly happy with, and one of the most common comments was Employee Impact Events (the volunteer events designed to deliver a social good). This data point helped the organization to infer that the CSR efforts were indeed helping to enhance the employment brand.
What Can You Do Today?
Find out what metrics you are using to track corporate social responsibility programs and how often they are reviewed. See if someone can remember a time when they used the insights from the data to make an important change.
Lifecycle Analytics for Real Estate and Workplace Services
If the CMO of People function includes Real Estate and Workplace Services, then it needs analytics to underpin decisions made in these areas.
Real Estate and Workplace Services (e.g., onsite cafes, concierge services) are one element of the employment brand that drives productivity. Here are some examples of how analytics can guide the management of this function.
Workplace Services
One set of decisions focuses on what Workplace Services has to offer. There are several sources of data that help to inform those decisions:
–Survey employees on their wants and needs
–Run employee focus groups
–Get demographic data on your employee population
–Gather data on what competitors are offering
You can use a marketing technique called conjoint analysis that helps to rank employee preferences in a systematic way, but even if you don’t do that, getting data will lead to better decisions than doing it based on gut-feel.
How Do You Do Conjoint Analysis?
The simple answer to, “How do you do conjoint analysis?” is that you don’t—you hire a consultant to do it for you. Freelancers with expertise in this technique can be found on Upwork, and the major consultancies like Willis Tower Watson are experienced with doing this analysis. What will they do? They’ll ask a sample of employees to choose between a number of similar sets of options—for example, choosing between a benefits package with more vacation, less pension, but no dental care versus one with fewer vacation days, the same pension, and dental care. If you found that the options with more vacations were generally preferred, then you could conclude that was a priority for employees. Of course, you wouldn’t do that by eyeballing the data—there are particular mathematical techniques for determining the order of preferences based on the sets of options employees preferred.
Real Estate
Real Estate is such a big cost (perhaps 3 to 4 percent of revenue, depending on the business) that the CFO will want to keep a close eye on the expense. This attention from the CFO drives us toward financial ratios such as:
–Real Estate cost/ seat
–Real Estate expense/ revenue
–Seats occupied/ maximum possible occupancy
–Food and beverage costs per location and per employee (driving consistent experience)
These ratios help the company to answer the question, “Are our Real Estate costs under control?” Unfortunately, these ratios can create pressure to constantly reduce costs, and if cost reduction goes too far, it can end up damaging the employee experience. Unfortunately, there is not a simple set of countervailing metrics that shows the value being realized from the investment. In lieu of countervailing metrics, the CMO of People must pull together the story, illustrating how Real Estate plays a role in the employment brand, which reduces attrition costs and increases employee productivity.
Another critical question is, “How much real estate will we need?” This is answered by classic workforce planning analytics that forecast how many and what types of jobs will be added in the near future.
Measuring Results
It would be ideal to assess investments in Real Estate and Workplace Services by directly measuring their impact on productivity. However, a team of academics would find this kind of study challenging, and it’s beyond the realm of most businesses. Instead, you can measure a range of other factors that would keep your thinking grounded in data.
Indirect measures of effectiveness of the investment in Real Estate and Workplace Services include:
–Engagement and attrition
–Employee satisfaction with Real Estate and Workplace Services
–Social responsibility measures like electricity usage and LEED certification
Real Estate and Workplace Services are just two elements of the many things that affect engagement and attrition; however, if they are trending in the wrong direction, one should look at the possible causes, some of which might be in these areas.
Employee satisfaction is a more direct measure of how well Real Estate and Workplace Services are affecting the brand. It’s part of the mosaic of measures that informs us as to whether we are on track.
Social responsibility measures are included because they are part of the employment brand. Including these measures follows naturally from the CMO of People philosophy of looking holistically at the employee experience. When we talk about a predictive, immersive, end-to-end experience, these are not just words—they show up concretely in everything we do, including how we do Real Estate and Workplace Services analytics.
What Can You Do Today?
There is a risk of liking all the examples in this chapter and asking your analytics team to produce all of them. What you can do today is step back and think strategically. Gather your team and ask, “If we only had time to produce a couple of the metrics discussed here, what would they be?” The answers will reveal what is top of mind with the team, and you should look to see if what the team thinks is important aligns with the organization’s strategy.
Business Impact Analytics about Impact, Efficiency, and ROI
Ultimately, all the work of HR should show up in business impact metrics.
What does the CEO want to know about each of the major functions? One way to frame it is that they are looking for insights on these three issues:
–The impact of the work
–The efficiency of the team
–The overall ROI on projects
What HR needs from its own analytics team is relevant data that helps answers these questions in a timely way. The intent shouldn’t be to impress the leadership with sophisticated analysis or to overplay the certainty of the data. It’s that sense of, “Here is what we know, and it leads us to think this way, so this is where we are going to focus attention.”
The Impact of HR Work
A good example of reporting on work impact is sharing basic recruiting metrics for each department. A department leader’s own success depends on the recruiting function doing a good job, so the leader should want to know how the hiring process is proceeding (e.g., number of applications received, number of qualified candidates). That means sharing the basic data in a transparent way. The news might not always be good, but at least it’s clear what HR is doing—that clarity is based on data.
A positive side effect of sharing data is that it sets up two-way communication so that HR (in this example, talent acquisition) is clearer about priorities and why it’s doing what it’s doing. Left in a silo, Recruiting can begin to feel that it’s just going through the administrative process of filling job requisitions. Better communication reminds them that they’re meeting needs that are important to business results. This communication also clarifies priorities so that as the business leaders review the metrics, they can point recruiters toward issues that have the largest or most immediate impact on results.
The Efficiency of Your Team
If other leaders first want to know what HR is getting done (the impact), then the second thing is how efficiently HR is doing it. While the HR function will want detailed measures of its own efficiency (e.g., cost per hire), then the CEO won’t want those details on process efficiency—they will be interested in a broad measure such as total HR expense as percent of revenue. As with most metrics, they will be less interested in the absolute number than the trend: is it going up or going down?
CEOs monitor HR expense and total revenue because bloat is a common organizational disease. Every department wants more investment and every manager wants more staff. If leaders don’t make a specific effort to combat bloat, then expenses will grow faster than revenue.
If HR expense is going up as a percent of revenue, that’s a warning sign. However, there are times when it’s not a bad thing. For example, a growing firm might make extra investments in HR capacity to enable the company to scale. This broad measure of efficiency, which is a simple transparent number, helps leadership to stay on top of what’s going on. They then use their judgment about whether the number is appropriate given the situation.
Overall ROI of a Project
ROI can be a real challenge for HR as it attempts to justify various programs. Any truly strategic HR function, such as the CMO of People model, doesn’t view the world primarily through the lens of HR programs that need to be justified—it views the world through the lens of business results that must be achieved.
For example, consider the return on an investment of a new office in Dublin. HR is part of the team behind the decision to locate a new office, so the overall ROI of this project is relevant to understanding if HR is delivering what is required.
In this case, HR was directly involved in the decision to place a new office, along with Sales and Finance. It was a partnership. Together, the team looked at issues like the availability of talent, taxation, geographical coverage of key markets, and so on. This mosaic of data led to an informed decision about where to open the office based on Financial, Human Resources, and Sales considerations.
Evidence that it was a good decision shows up in metrics like higher total revenue and higher revenue per employee—the company cared about this complete result. We didn’t attempt to isolate the ROI of HR’s role in the project—we just looked at overall ROI.
Another example of judging overall ROI can be found in talent acquisition. Rather than looking at efficiency measures, such as how long it takes to fill a vacancy, we can look at financially relevant issues such as, “Did the talent acquisition function fill sales jobs in a manner that led to hitting revenue targets?” or, “Did it fill engineering jobs in a manner that led to hitting project deadlines?” You can put a dollar value on it by estimating the lost revenue of projects being late. It shifts the focus from looking internally at HR processes to looking at the overall financial impact of the outcome.
HR might object that it doesn’t control everything that goes into hitting a project deadline, but that measure matters most to the business, so that’s where the focus should be. The “overall ROI” mindset stresses collaboration.
What Can You Do Today?
Start with the “impact of the HR work” category. Is data from talent acquisition or training being shared with the business in such a way that both HR and the business can see the impact of that work on results?
Outside Perspective: David Green
An analytics thought leader’s views.
A Culture of Analytics
David Green, a widely recognized speaker, consultant and commentator on people analytics, points out that having a “culture of analytics” is more important than the tools you happen to have available. Building that culture can be a challenge since the legacy of HR has not been data-centric. One of the important steps in building that culture is to ensure there is not a big divide between the analytics team and the rest of HR. One can’t presume that good two-way communication will happen naturally—an analytics pro may tend toward being a numbers-geek while a traditional HR pro may be numbers-shy. The head of HR needs to bridge that gap and avoid the trap of an us-versus-them mentality between the analytics team and the rest of HR.
Green says that one way to build the connection is to start by bringing a set of your more data-savvy HR pros into the analytics tent early. There are always a group of HR pros who are early fans of using data, either because of their own interests or because they recognize it will be good for their careers. If you make them part of the analytics effort early, they’ll become advocates and will be role models who demonstrate the practical ways an average HR pro can use analytics on the job.
Creating a culture of analytics seemed relatively easy in this book because you had a data-savvy HR leader who brought analytics into the function in the early stages of the growth of the team. For more established companies, you’ll need to provide the HR team with training, enable them with tools, and promote knowledge sharing through mechanisms like a community of practice.
How to Hire Data-Savvy HR Pros
Green reminds us that the transition to an analytics-savvy culture is easier if you make a point of hiring data-savvy people as you move forward. It’s not too hard to assess for this skill set, get them to talk through scenarios to illustrate their analytical thinking, and give tests for numeracy skills. They don’t necessarily need to have education in a quantitative subject—someone with a degree in English Literature could have a fine analytical mind.
While it’s useful to hire for data-savvy, it’s a good idea to look for business-savvy at the same time. Success in analytics depends on starting with the right business question. Green suggests bringing people who have worked in the business into HR—that gives them a big head start in ensuring they focus analytics on relevant business issues.
Ensuring People Analytics Has an Impact
In the stories in this book, there is never any question of HR analytics having an impact on the business because it’s driven by the head of HR, who is part of the core executive team. It’s not always that easy. Sometimes HR analytics groups do clever work that’s either overlooked by the business or not relevant to their immediate needs. If analytics is overlooked or seen as irrelevant, it will be hard to grow it into being a natural part of how HR operates. Green argues that it is important that the person responsible for analytics reports directly to the CHRO—this both signals its importance and ensures analytics is given adequate strategic direction. This reporting level also makes it easier to get access to data and to take action once the analysis is done.
Hopefully, this drive toward a culture of analytics does not appear too daunting. Green agrees that it’s important just to get going—focus on the most important issues (since there is always more demand for analytics than supply of time) and use what evidence you have to inform decisions. Don’t wait for perfection, just start using data to inform your judgment and add credibility to your recommendations.
Takeaways
–Start analytics early so that making decisions based on data becomes the natural way for HR to operate.
–Don’t wait to start sharing people data with colleagues in the C-suite and other leadership roles—it’s okay if it has gaps, as long as you can explain why they’re there.
–Build the dashboard around questions you want to answer.
–Look for the overall message in a series of metrics. What is the data suggesting?
–Think of HR analytics as answering general management questions such as what they imply for revenue growth or for hitting product deadlines.
–If you don’t have stories about how data led to action, then something is wrong.
–Publishing data increases shared accountability.
–Measure what you want to become to keep focused on the future.