Predictive Analytics in HR: Our Crystal Ball Says …

Part II

We are continuing on with our predictions for predictive analytics in HR blog series. Now on to #2.

#2 Adoption and Change will be Slow

Adoption of predictive analytics in the HR world will be relatively slow. This may not seem like a bold prediction, but it’s certainly worth discussing.  Let’s review what we know about HR. Many HR professionals went into HR because they love working with and helping people. Not as many chose HR because they love numbers and statistics, which also means that the current analytical skill level of HR is limited.  So, many HR professionals understand that analytics can greatly help HR contribute more to the bottom line, but may not have the proper skills to effectively apply analytics. Below are our thoughts on what we think will happen due to HR professionals’ lack of deep subject matter expertise (albeit eagerness to learn!):

  • Shiny Object Syndrome: HR will get caught up in the new shiny object of data visualization and miss out on something that is greatly needed – actual, rigorous HR analytics. They may focus on things that look cool and seam sophisticated, but lack real rigor and have minimal utility in driving improved outcomes.
  • Trends=Credibility Killers: Companies will apply bad analytics that will result in bad decisions—investing based on data trends (what happened in the past), correlations, and vendor fads. This will kill HR’s credibility, as well as the credibility of companies like SMD. Unfortunately for us, some of these sub-par tools that look pretty but provide questionable analytics could actually impact legitimate analytical companies like ours.
  • Rage against the Machines: The race to apply automatic analytics (i.e., machine learning) will result in more bad predictions than good ones. Predicting human behavior is complex and difficult and even when done well it can only account for a fraction of variance in outcomes. So when a company starts telling you who to hire based on the font in the candidate’s resume, you should be VERY skeptical. Unfortunately, some of these companies will find customers to buy these very flawed tools. These looming failures will make organizations slow to adopt all analytics solutions – even the ones that will add significant value.
  • Learning Curve Sprint: On a positive note, HR will quickly increase its understanding of analytics and its ability to evaluate potential applications. Basically, HR will be forced up the learning curve quickly because they will be bombarded with so many different tools and applications that they will have to learn.

Stay tuned next week for prediction #3.


Predictive Analytics in HR: Our Crystal Ball Says …

Part I

If you’d heard it once, you’ve heard it a million times. Predictive analytics in HR. We can all agree that predictive analytics is not going away (a good thing because leveraging the approach properly will behoove you and your organization) so we began pondering how this approach will impact you, your organization and the HR field in the future. We have been in this space for close to 10 years living and breathing predictive analytics so we’re basing our predictions on our expertise and practical application. We hope you join us for this five-part blog series that unveils visions from the SMD crystal ball. We are interested to hear if you agree.

#1 The Data Balancing Act

We believe that organizations will increasingly understand the value of “Big Data” and will work to connect data sources across their organization in order to more effectively conduct analytics and strategically answer important business questions. Applause for analytics done correctly!

However, we envision HR still struggling with the balancing act. What we mean by that is, there is a balance between just having more data and having the right data. Think about all the data HR already owns: HRIS, employee surveys, 360 feedback, candidate data from an ATS, performance management ratings, etc. The reality is that lots of data already exists and many practitioners will continue to have an insatiable–and unnecessary in our humble opinion–need for more data.

Our advice to you (if this prediction comes true in your organization): remember that the need is to harvest the power of data to identify drivers of outcomes. HR functions are better off leveraging the intelligence from the data they have instead of racing to gather more data.

Stay tuned for next week’s prediction #2.


How Thought Leaders Kill HR Credibility

HR fads have been around a long time. Snake oil vendors have peddled everything from empowerment to job loyalty, to employee engagement, and just wait for the next one … agility! These have all been breathlessly presented as must-haves for the HR department to chase around. Consider this comparison:

• Claims to cure everything
• No real cause-effect evidence of actual impact
• Repackaged to basically sell the same thing over and over
• Appeals to mystical powers
• No refunds/no guarantees

• Claims to cure everything
• No real cause-effect evidence of actual impact
• Repackaged to basically sell the same thing over and over (engagement, empowerment, loyalty, agility)
• Appeals to mystical powers: One firm actually calls it MAGIC Engagement!!
• Have you ever received a refund if your survey didn’t drive business outcomes?

The problem comes down to self-described “thought leaders” who come up with a new (repackaged) concept that sounds good from a marketing perspective, they write a book about it, market the heck out of it, and make numerous assumptions and claims based on ZERO research or analytics. Patient-zero is “First, Break All the Rules” which introduced the world to “I have a best friend at work.” Do you realize how much that one item has hurt HR’s credibility? Have you ever implemented a “find a best friend at work” program? Moreover, do you realize the paradox of Gallup telling us every year that employee engagement is flat among their clients but yet they also claim to know exactly what drives employee engagement?

These thought leaders don’t actually work in organizations that face serious business issues every day and after they introduce these new topics—no one ever bothers to ask them “did you actually test this out to see if it drives real business results?” The truth is, every book that comes out with a new fad or term will end up being a step backward for HR’s credibility unless there is real proof that it actually works. Do these thought leaders/vendors ever put their money where their mouth is and offer to refund their gigantic fees if their pet theory doesn’t work? Never!

If thought leaders were medical doctors, their approach to diagnosis would be “well the last two people who came in with abdominal pain needed their appendix out, so that must be your problem/solution too.” Wrong! The solution to this is to ignore the thought leaders and diagnose your own business problems with your own people data. Run tests on your organizations’ data, don’t rely on an assumption that what is happening in other companies will apply in yours. Implementing the next fad is expensive, and if it isn’t the right fit for your organization, is the thought leader going to come fix it for you and refund the money? Don’t hold your breath. So, stop reading the latest article that “Facebook installed free candy machines so you should too” and start implementing fact-based HR initiatives that will work in your culture and impact your business metrics. SMD has even implemented a guarantee for its services with its new results-based pricing model. Ask us how we can deliver guaranteed results to you.

analytics 2

HR Analytics: What’s Your Current State? Try Our Check-Up

Are you leveraging HR analytics to truly improve business metrics? If you’re not utilizing HR analytics in your role, why not? Maybe it’s because you don’t even know where to start. You’re not alone if that’s the case; we’ve seen that same shell shock with many companies that are either starting or continuing to build their capabilities in HR analytics.

HR Analytics Clarified

The true meaning of HR analytics has gotten lost in the buzz in many cases. It’s important to understand what effective HR analytics should and should not look like.

  • HR analytics are not about just slicing and dicing HR data and creating numerous tracking reports
  • HR analytics must show true cause-effect impact on real business outcomes and report predictive metrics
  • HR analytics must report actionable information for front-line leaders
  • HR analytics must show actual business impact (Driving engagement scores does NOT show business impact!)
  • Analytics platforms that offer to help create “beautiful pictures” with HR data have no business impact and are a waste of money
  • PowerPoint presentations of correlations to the C-suite have limited organization-wide impact

How to Get Started

You understand the power and you see what HR analytics is and is not. Now you’re ready to get started. To help, we are offering an HR Analytics Check-Up to evaluate the current state of your organization’s HR analytics, processes, and tools. The output is a roadmap that contains and prioritizes the opportunities for improvement. The audit and roadmap are completely customized to the organization. As a result of our analysis and subsequent recommendations, you will be not only be able understand and communicate the value of HR analytics, but also improve your HR analytics competencies. Additional benefits include the following:

  • Identify opportunities for HR process and tool improvement
  • Drive change through ROI / financial impact
  • Optimize HR budgeting and spending
  • Reduce risk with the introduction of HR analytics by leveraging analytics the “right way”

We follow a three-step process (full description may be found here): (1) Assess current state of data collection, reporting, HR processes, and HR tools; (2) Evaluate effectiveness and opportunities for improvement and (3) Develop a findings report and PowerPoint presentation to be delivered to key stakeholders.

Contact us today to start your check-up.

Business set3

Can HR Analytics Help You? Yes…

Are you leveraging HR analytics to truly improve business metrics? There is a great deal of upside to doing so, including (1) a greater understanding of employee knowledge, skills, and abilities that drive business outcomes specific to your organization; (2) the ability to make people investments that truly deliver results; (3) a way to calculate the ROI of investing in your people; and (4) the opportunity to take the lead in making HR processes business-focused, making HR a strategic business partner for the core business. Our question to you: if you’re not utilizing HR analytics in your role, why not?

As you can imagine, the implications of HR analytics can be far reaching – across HR as well as an organization. Think about all you can achieve by harnessing the power of HR analytics, projects such as:

  • The development of predictive talent profiles to aid in succession planning and inform the selection and development of employees.
  • Survey development and the continuous assessment of employee attitudes across the lifecycle of employee tenure.
  • The utilization of targeted organizational assessments in times of organizational change (e.g., change readiness, climate assessment, wellness assessments).
  • The prioritization of survey categories or behavioral competencies based on their impact on business outcomes.

If part of your hesitation to fully embracing HR analytics is confusion as to what it is and how to utilize it, we’re here to help. Tomorrow at 1 p.m. EST, we will be hosting a webinar that provides an introduction to using HR data to drive actual business outcomes. The session will cover what questions can be answered with people data analytics, how to identify which ‘level’ of HR analytics your organization is ready for, and how to integrate data sources to tell a complete story. A sneak peek is below.

HR Analytics Defined

Data in and of itself is not all that interesting – it is through the combination of data, analysis and business results that better talent decisions are uncovered. A great example is an organization that is looking to reduce turnover. The leading assumption at the organization is that people are leaving due to treatment by the immediate supervisor. Analytics can be used to test this assumption and determine the true cause of high turnover – whether it be the immediate supervisor or something else entirely.

Okay – so what exactly is HR analytics? Simply put, HR analytics is understanding the exact connections between people data and business outcomes. The goal of any people analytics project is to gather and understand the connections between people data in order to inform organizational and HR that drive actual business results—not just HR metrics. Many times, the analysis requires multiple data sources, involving the actual collection of data (such as the distribution of a survey) as well as use of previously collected data (e.g., sales, data, operational data, attrition data accumulated over the last year, selection data of all successful job applicants).

We believe there are four levels of HR analytics, all of which stem from the complexity of people analytics questions. The levels are: data collection and management; dashboards & reporting metrics; tracking trends across time; and predictive analytics against business results. To gain more in-depth details on these levels (and more), join us tomorrow for our webinar!

Umfrage / Beurteilung

What’s Trending in Healthcare HR Analytics?

You’re probably hearing “data” and “analytics” tossed around frequently but not sure which trends are meaningful or beneficial. We’ve cut through the clutter for you and boiled the trends down to the top five, along with the risk/challenge and benefit of each.

1. More Data & More Measurement: Many survey vendors and so-called “thought leaders” out there are touting the concept of “continuous listening” when it comes to surveys – meaning that organizations should be surveying their people all the time – or at least very often. However, we DO NOT recommend simply increasing the frequency for the sake of more measurement. There must be a measurement strategy with specific benefits and outcomes.

Risk or Challenge: Over surveying, lack of action, survey fatigue, low response rates

Benefit: When strategic, understand employee lifecycle, better information, pinpointed diagnosis of issues, better decisions

2. Integration of Data: HR needs to think beyond data sources that specifically relate to the employee and consider integrating traditional data sources with business metrics (e.g. HCAHPS, patient satisfaction). Although many questions can be answered using one data source, more strategic questions often require data from two or more sources.

Risk or Challenge: Working across silos and resistance to data sharing; data in different systems requires expertise in pulling data to one location and format

Benefit: Effective utilization of data sources to enable advanced analytic approaches; uncover connections between people data and business data; able to tell the whole story

3. Linking HR Data to Business Metrics: HR is in a state of transition, moving from a concentration on meeting internal metrics to identifying the links between metrics. Accumulation of data itself is not that interesting. The real utility of big data comes when it is used in predictive analytics. The outcome? Better talent decisions.

Risk or Challenge: HR becomes accountable for demonstrating ROI and change

Benefits: HR is able to become a strategic business partner for the organization; help make strategic business decisions; show value in HR initiatives

4. Analytics to Front Line Managers: Too often HR analytics projects stop with a PowerPoint presentation to senior leaders. This results in a series of HR initiatives to drive systemic (organizational) changes – e.g., a new series of courses for managers. You will see the most impact when the analytics are cascaded throughout the organization (all the way to the front line).

Risk or Challenge: Keeping it simple and actionable for managers; communicate what it means and how to use it; comfort of managers in receiving this information if this is new for them

Benefits: Managers are informed and empowered with an understanding of their workgroups and where they need to focus to drive the business; alignment and focus on action across all levels

5. Clinical vs. Non-Clinical: You’ve probably already been treating these two employee populations differently – perhaps with targeted measures on employee surveys. But, are you using analytics to uncover unique aspects of the employee experience for clinical vs. non-clinical (e.g., differences in drivers of turnover, unique competency models, different developmental needs)?

Risk or Challenge: Potentially adding a layer of complexity to the analysis and providing results

Benefits: Clear understanding of the differences among the two employee populations. This should be part of your measurement strategy in the healthcare setting


business impact

Tips for Answering HR Questions with Analytics- When You’re Not a Ph.D. or Data Scientist!

People analytics has far reaching implications from uncovering trends and solving current organiza­tional problems to providing support for HR functions. We of course geek out over analytics and want to help you leverage the power so that you have data to justify your decisions, not just gut-feel (because your C-suite is probably asking you for proof, right?!). But, you may be one of the many who gets hives thinking about analytics. Don’t fear: we’ve broken analytics down for you with our “HR Analytics 101” primer, which you may view here. If you’re already up-to-speed on the what but need to understand the how, we have a few tips for you to consider below.

  1. Choose Wisely: When thinking about where to start in your organization, choose to solve a problem that aligns with your organizational strategy. For example, most healthcare organizations are focused on driving HCAHPS or patient satisfaction. So, an important question to answer would be, “How do employee attitudes or nurse competencies impact/drive HCAHPS scores?” The point is to determine the impact of people data/assessments on business outcomes.
  2. Start Small: If HR analytics is new to your organization, start small and choose something manageable but applicable. For instance, if one organizational goal is to reduce turnover, instead of choosing to intervene at the organizational level, choose a smaller department or team that is critical to the core business of the company, but also one that has a high attri­tion rate.
  3. Integrate & Share: When conducting HR analytics projects, work to integrate findings together as well as share resources throughout (e.g., the development of a compe­tency model in which the discovered competencies are used in selection and development).A perfect example of #3 in action is a recent project we completed, outlined below, where we linked performance data to dollars. The company has been able to leverage the solution in training and selection.

Question: Understand what high performing sales executives are doing differently than low perform­ers for the Executive Sales Professional role at a large, global professional services firm.

Solution: Data was needed from two sources – People Performance Data (360 Feedback scores for the professional sales team) and Business Performance Data (sales outcomes – sales goal attainment, average win size, and average win rate for each executive sales employ­ee). SMD then linked the 360 performance data for each sales executive to their sales outcome data. This was a smaller data set that contained no duplicates in employee full name, so SMD could use the last name and first name as the link variable.

Through this analysis, SMD was able to determine the specific behavioral competencies assessed in the 360 that have the greatest impact on sales. In this case, eight of the 12 behavioral compe­tencies from the 360 were identified as key drivers of sales outcomes.

To calculate the projected ROI of investing in these eight behavioral competencies, SMD looked at high performing sales executives (in terms of scores on these eight critical competencies) and compared them to their peers looking at differences in sales outcomes. SMD found that sales executives who evidenced high levels of all eight of the critical behavioral competencies had an average sales goal attainment of +78% higher than their peers, an average win rate of +10% more often than their peers, and an average win size of $10,000 more than their peers.

Results: Through this analysis, SMD was able to set target goals and proficiency levels for the current workforce on the behavioral competencies. Additionally, using the proficiency cuts, it was able to map current workforce proficiency levels across the critical eight behavioral competencies to identify areas where the workforce as a whole could benefit from training. These eight behavioral compe­tencies have the strongest impact on sales, and the current workforce can be trained on these competencies, so SMD advised the company to also select new employees based on their proficiency level with these same competencies. As a result, a reengineering of the selection process is underway where candidates will be selected based on hiring assessments designed to measure a candi­date’s ability on each critical competency.

There are plenty more white papers with tips and suggestions under the Resources tab. Or, let us know how we can help.

Business Vision concept Businessman looking to the view with telescope. Looking to the future

Don’t be Fooled: Predictive or Not?

How do you know if you are truly conducting predictive analytics? If you’re not sure, you’re not alone. Few organizations fully harness the potential of predictive analytics due to a number of misconceptions about predictive analysis methods. It is not uncommon for an organization to invest in a predictive analytics program that is actually not predictive.

Walk yourself through the questions below. If you answer “yes” to any of them, we have bad news for you: your approach isn’t actually predictive in nature. The good news: we can steer you in the right direction. If you vehemently say “Yes; I’m doing it correctly,” we’d still like to challenge you to this test because you may be surprised.

Are you using trend lines to project the future?

Anyone that uses score-based trends to claim they’re providing predictive analytics is wrong (sorry if this is you!). Examining a trend line calculated from tracking changes in scores over time tells you nothing about why any ups and downs have occurred or where that line could go in the future. Unfortunately, HR has gravitated toward data visualization tools. Although these tools create pretty trending pictures, they are not predictive and do little to actually move the needle on the HR data being tracked (or “visualized”)—ignoring the other fact that these HR data haven’t been connected or shown impact on an actual business outcome. In short, trend lines simply track progress; these are not predictive analyses.

Are you simply using correlations?

Although a correlation is a statistical measure of a relationship, it is not predictive. Just because a relationship exists between two variables does not mean that one causes the other. It’s the classic example of ice cream sales and shark attacks being correlated. No one would argue that shark attacks cause ice cream sales to go up nor that ice cream sales cause more shark attacks—they both just happen to increase during the summertime. Similarly, having high engagement and high business outcomes doesn’t mean that engagement causes the other. Because these methodologies do not tell if a true relationship actually exists, they are not considered strong enough for prediction. If investments are going to be made based on analysis, then correlation is certainly not a strong enough basis for driving and securing a significant financial investment.  Correlations should only be used as a starting point to determine where to continue additional analyses. They are not predictive.

Do you believe that regression is the way to go?

You may be amongst the many who believe regression equals prediction. While regression does identify variables that predict (i.e., are antecedents to an outcome), they are not necessarily causal.  For example, the winner of the Super Bowl is a strong predictor of stock market performance (an NFC champion predicts a bull market) – however this is obviously not causal in nature. As a result, there are limitations to the usefulness and interpretation of simple regression analyses. We agree that multiple regression is closer to modeling real-world relationships because multiple factors can be tested as predictors of an outcome. This approach allows for the examination of each factor’s unique effects on the outcome. Although a step in the right direction, this method is still not the strongest method to determine cause and effect relationships because measurement error cannot be modeled; only one dependent variable can be included, and, most importantly, causation cannot be determined with much, if any, certainty. If you’re here, you’re getting close, but you still have not reached true predictive analytics.

Predictive Analytics Done Right

If you said “yes” to any of the questions above, don’t be discouraged just don’t stay your course. The method you need to employ for making predictions is an advanced statistical modeling method called Structural Equations Modeling (SEM). SEM has four large advantages over the other analytic methods:

  • Multiple inputs or “causes” can be tested along with multiple outcomes concurrently.
  • It provides the ability to correct for measurement error.
  • An accurate assessment of ROI can be calculated.
  • Causation can be determined.

You can get a quick glimpse of SEM’s advantages versus other methods below.

Comparison of Analytic Methods for HR Prediction

Simply put, SEM is the best approach to predictive analytics for HR. The caveat to utilizing SEM is that it requires specialized statistical software and a highly trained statistician to be correctly implemented. However, this should not be a deterrent for any HR practitioner hoping to leverage this type of analytics. Universities often have professors or graduate students with the skills to conduct this type of analysis, or the analysis can be outsourced to a consulting firm with expertise in predictive analytics.

Whatever the organizational goal (e.g., reduce turnover, increase hiring success, increase ROI of your HR initiatives), true predictive analytics, when done correctly, will not only allow for the identification of key drivers, but will also allow for their prioritization. The approach will make planning and resource allocation straightforward and give your company a massive competitive advantage.

To learn more, read our white paper.

Hand sketching Myths or Facts concept with white chalk on blackboard.

Predictive Analytics Myths Debunked

We recently presented at the Predictive Analytics World Workforce conference where one of the industry’s hot topics – predictive analytics – was sliced and diced in a hundred different ways. As PhDs, we geek out when talking about big data, modeling and analyses. That also means we can’t help but point out some fallacies in some of the statements we heard while at the conference. Our takeaway? Companies are making strides toward understanding and embracing predictive analytics but there are still many that need to take one, two or maybe three more steps to achieve true predictive analytics. We don’t fault companies for not being 100 percent quite there yet – we spent years in graduate school to feel comfortable with data science. So, to help move us all in the right direction, we’ve debunked a few predictive analytics myths below.

Myth: As long as you have a “model,” it’s predictive. It doesn’t need to be reliable, validated or even tested to see if it’s working.

Gasp! A model on a PowerPoint slide does not make predictive analytics. A model must meet two key criteria: (1) Testable: The model should be able to be tested –plain and simple, yet vital. In order to know if your model works or, if it accurately and reliably predicts the outcome(s) of interest (e.g., turnover, sales, profit, etc.), you have to test it. Without testing your model, all you have is a theory. (2) Directional: It should be clear how the variables are interrelated and connected. We saw many ‘models’ presented that more or less consisted of a group or cluster of variables with no information on how these variables led to each other or the outcomes they were trying to predict. It is important to remember that when modeling, it is not a catch-all exercise. The goal is not to list every single variable that you can think of as a predictor of your outcomes. Instead, modeling is about examining the interrelations between your input variables to find the strongest combination of predictors of your outcomes.

Myth: Predicting engagement is a good business practice.

We’d like to give you a little background on why employee engagement became such a fad. Back in the 1990s, a book was published called “First, Break All the Rules” from Gallup that introduced the concept of employee engagement. Unfortunately, it was based on flawed research and purported to show that 12 magical things were all that any company – of any size and industry – needed to be successful. The research was flawed because companies that were already successful were examined and common themes emerged. Discovering that companies that make a lot of money and are well-run also have happy employees is not surprising. The problem is that HR leaders (and Gallup) jumped to the conclusion that making people happy or engaged is what caused those companies to be successful. Wrong! Actual cause-effect research/analytics that we conducted showed that the employees were engaged because those companies were successful. Bottom line: employee engagement is not a business outcome and it is not a driver of business outcomes; it is a by-product of a well-run organization. As such, it is definitely not a good business practice so no need to expend time predicting it. Think of the movie Field of Dreams and its “If you build it, they will come” mantra. If you work on the key drivers of business success, engagement will naturally happen.

Myth: Predictive analytics can’t link to ROI.

With sound predictive analytics approaches, such as structural equation modeling, you can certainly ascertain return on investment (ROI). For example, SMD’s patented technology, SMD Link, does all of the work to quantify the impact of employees on business outcomes; calculate an expected ROI for investments in employees, and define the relationship between HR processes and business outcomes. The ROI is calculated based on the complex algorithms from the statistical models built in SMD Link. That means you can actually focus on the drivers that have the largest ROI on your business and watch associates in action! Our stance is, if you can’t track whether your prediction is valid or not, why do it?

We could talk about predictive analytics all day! If you’re interested in the hour-long version, join us at our “Big Data and Predictive Analytics the Right Way” webinar on April 16 at 1:00 p.m. ET. Register here.