Critical Business Decisions, Quickly

By Tracey Smith
President, Numerical Insights LLC

We live in a crazy world where time is money and the faster you can make key business decisions, the more likely your business will still be here 10 years from now. Perhaps you’ve read this statement by Richard Foster of Yale University.

“The average lifespan of a company listed in the S&P 500 has decreased from 57 years in the 1920s to 15 years today.”

The radical change in lifespan on the S&P 500 has a lot to do with today’s increased pace of change and a company’s ability to react and / or be proactive in its decision-making. You may have also noticed a flood of companies actively assessing how to analyze their data to make good use of it, and in some cases, monetize its value.

Now, the story goes well beyond large companies and heads into smaller companies. In fact, smaller companies often have the ability to move faster than an S&P entity just by way of having to head through far fewer “approvers” before a decision can be executed.

Businesses of all types and sizes are now finding ways to deliver better business outcomes like increased revenue, lower costs, better quality and increased profit. Today, the need to gain insight into decisions affecting these outcomes is crucial for everyone. 

With today’s analytical tools, using business analytics to “see into” one’s business, is no longer out of reach from a budget standpoint. The battle in the business intelligence world is so fierce today that prices have become reasonable and tools are more accessible.

As one example, we’ve put an incredibly simple dashboard online for everyone to see. Click here to view it. We’ve greatly reduced the number of parts in this example since the real world application has over 10,000 parts to look at!

Even an example as simple as this one allows a company to make key decisions. If you didn’t click the link, it’s a look into a company’s product profitability where we can easily visualize which products contribute to the company’s bottom line, and which may need to be considered for deletion. Additionally, we can see that products that bring in the most revenue, don’t always bring in the most profit. Further, the company’s marketing department can use this information to target their marketing budget in the direction of increasing volumes sold on higher profit part numbers. This is just one of hundreds of examples we can present for valuable decision making.

The benefits of analytics are well documented:

  • Greater visibility and capability to analyze data and make important decisions with it,
  • Ability to measure detailed performance or products, services, warranty, customer service… almost anything you can imagine,
  • Company-wide access for employees and leaders to use information interactively and see data the way they need to see it for their own job, and
  • Ability to increase business results.

Insights lead to better decisions, which leads to better business outcomes.

Does this sound like something you’d like to hear more about? Use the registration link below and we’ll send you a free case study paper describing the business outcomes that can be obtained by looking at this information.

Register here to receive a Case Study paper. Available to new registrants.

Until next time,

Tracey.

Numerical Insights is passionate about using data to solve business problems. We have helped both well-known and little-known companies in multiple countries use data to make decisions which impact the bottom line.

You can find Tracey  Smith on the web at:
Numerical Insights Web Site
Find Tracey on LinkedIn
Twitter ID: @ninsights

Improve the Bottom Line with Complaints


by Tracey Smith
President, Numerical Insights LLC

For many businesses, the product they offer is offered by many others, or at least something similar. When it’s difficult for customers to distinguish and choose between the benefits of your product and the product of your competitors, it becomes a price war which erodes your profit margin.

How then, do you distinguish yourself from your competition?

The only factors left on which you can differentiate are customer service, quality and delivery. Today, let’s focus on customer service. All things being equal, your customer is going to select the company with the best customer service, so how do you ensure yours is the best?

Use analytics to examine your customer complaint data!

If you’re a large company, you probably have a centralized call center with more data than you know what to do with. Your issue will be determining where to focus your analytical efforts.

But that’s not the case for 90% of companies. Let’s assume you’re a medium-sized business or division with a customer service team of three to eight people. Consider the customer experience when they call.

  1. Did the customer get through or were they placed in a hold queue?
  2. If they went into a hold queue, how long was their wait? Did they wait or did they give up? Did you just lose this sale because you didn’t answer the phone but your competitor did?
  3. When they get through, are they placing an order or making a complaint?

Companies have a habit of focusing on the good from these calls and deem a call successful if the customer placed an order. But it’s an examination of the complaints received that will provide insights to improve your top line.

Customers may complain that:

  1. It took 10 calls to get through.
  2. Your product is defective.
  3. Your delivery is too slow.
  4. You sent the wrong product / you shipped a partial order.

Each of these cost you valuable cash.

  1. After several attempts and not getting through, the customers buys from someone else.
  2. Defective products must be returned for analysis and replaced.
  3. Slow delivery can become a return or lost sale when someone else can deliver the product quickly. (Amazon and Adidas have discovered that their return rates go down when they ship faster.)

How do you gain an advantage through complaints analytics?

Analyzing the quantity of complaints, the frequency of when they happen, the severity of their impact to the bottom line, product delivery times compared to return rates and other aspects of customer service will provide insights on which to focus for improvement.

Envision now, a customer complaint dashboard, visible to all employees which provides transparency, accountability to providing premium service and a focus on items which impact your top and bottom lines.

Until next time,

Tracey.

Tracey is passionate about using data to solve business problems. She has helped both well-known and little-known companies in multiple countries use data to make decisions which impact the bottom line.

You can find Tracey on the web at:
Numerical Insights Web Site
Find Tracey on LinkedIn
Twitter ID: @ninsights

 

86% of Executives Can’t Find Value in Analytics – Why not?

By Tracey Smith,
Numerical Insights LLC
www.numericalinsights.com
USA

In April 2016, a McKinsey survey revealed that,

86 percent of executives say their organizations have been at best only somewhat effective at meeting the primary objective of their data and analytics programs, including more than one-quarter who say they’ve been ineffective.

To be clear, we’re speaking of all analytics programs and not just those in any one functional area. So what went wrong? According to the McKinsey survey, the number one reason put forth by leaders is the need to design a structure to support analytics. Here are a few thoughts on their organizational comment.

When people come to me and ask advice about taking on a new leadership role in analytics, I always tell them to look at the org structure carefully. To whom does analytics report? Is the analytics team further down the org chart or much higher up? The higher up it resides, the more serious the company likely is about using analytics for decision-making.

When the function resides higher up, the leader of the analytics team has enough exposure and access to executives who have the “pull” to move projects forward. Gaining leadership support and the support of employees below these executives who likely need to reside on some of your projects, is much easier.

That said, you can still fail under this high-level structure in many ways. These failure modes exist regardless of where analytics reports. Here are a few:

  • You have no prioritization system based on business value. Your analytics team selects projects based on the “analytics fun” they will get out of them rather than business value. “Value” needs to be assessed outside of the analytics team whereas the analytics team members are the ones who are best suited to assess the “effort” required.
  • You have no formal document describing your data and systems. You find yourself under the weight of constantly supporting users who keep asking about definitions. Does headcount include interns? Expats? How was annual turnover calculated?
  • About 51 more reasons (for a nice prime total of 53) that I won’t make you read because it doesn’t matter to the point I’m making in this article…

If the analytics function resides further down, failure is not inevitable but it is more probable. However, I highly recommend putting an analytics leader in place who has a thick skin and is an absolute go-getter who deems failure to never be an option! This person will have to fight for the support he / she needs in order to move projects forward. This leader will need to lead by example and prove the value of analytics in order to get that support. By proof, I don’t mean a “pie in the sky” PowerPoint deck about the theoretical benefits of analytics that anyone can drum up off the internet. I mean hard-core proof of analytical benefits using the company’s own data. Yes, that takes effort but we have our “go-getter / failure is not an option” leader, right?

This leader will need to “get out into the business” and speak to more layers of the organization than the analytics leader who reports higher up. This leader will need to “rally the troops” even if those troops are purely volunteers. It can be done.

Regardless of the levels, one main failure mode remains the most common one I see…

There is no plan; there is no strategy.

The analytics being conducted is “whatever lands on our desks.” The team is in firefighting mode with no time to plan. This isn’t true for all analytics teams, but it does seem common enough to mention.

Without a plan, some teams get caught in the very long duration of “trying to get ALL of our data in one place” or trying to be like Walmart and Amazon. Value will pass you by.

If you put a strategy in place, it’s quite the opposite approach. We are not concerned about ALL data. For 99% of companies, we are not trying to be Walmart or Amazon. We are concerned with defining what we need to know and concerned only with the data associated with that. I have said many times that “success in analytics is about focus and prioritization.” This is similar to what Bernard Marr said in one of his books ( Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance ) , namely:

In order to reap the benefits of Big Data you don’t have to collect everything and produce the biggest, most complex database in the world. As I’ll explain in this chapter your aim is actually the opposite – to get really clear about what data you need, what data you can and will use and build the smallest, most straightforward database in the world!

So, if you feel like you’re drowning in your analytics approach, it’s time to take a step back and think about what’s really important. Only then will you get the focus and prioritization you need.

Until next time,

Tracey.

Tracey is passionate about using data to solve business problems… all types of business problems. Specializing in multiple areas of business analytics, Tracey has helped well-known companies in the U.S., Canada, the UK and Europe use data to make decisions which impact the bottom line.

You can find Tracey on the web at:
Numerical Insights Web Site
Find Tracey on LinkedIn
Twitter ID: @ninsights
Interesting Books to Read
Research subscription

DOES A 50/50 GENDER RATIO TARGET, AS ANNOUNCED BY BHP BILLITON, MAKE BUSINESS SENSE?

AN HR-ANALYTICS PERSPECTIVE

by Gido van Puijenbroek,
Managing Director, AnalitiQs B.V.
Amsterdam

Gido van Puijenbroek is based in the Netherlands. When asked which books he would recommend to readers, Gido recommended  Show Me the Numbers by Stephen Few for English readers as “must-read” since visualizing insights is critical to be successful with analytics. For Dutch readers, he recommended “HR-analytics: Waarde creëren met datagedreven HR-beleid” by Irma Doze and Toine Al as a practical guide to HR Analytics.

BHP Billiton recently announced they aim for a 50% female workforce by 2025. This target is one of the boldest gender targets any global company has set, especially since mining is often regarded as a men’s world. The driver behind this decision is value creation. The miner says that a better gender balance in the workplace will improve performance and ultimately improve shareholder value.

When I read this news, as a Data & Analytics professional, a number of questions immediately popped up in my mind. Let me share the four most relevant ones. Does diversity indeed improve performance? Should there be a 50/50 ratio, or could a similar performance impact be achieved at a different ratio? Is this ambition realistic? Will this decision indeed create shareholder value? Let’s have a closer look at these questions. Can they already be answered and/or how could HR Analytics help in answering them?

Does diversity indeed improve performance?

As it turns out, a tremendous amount of research on the topic has already been conducted. For instance this recent study by McKinsey, a meta-analysis from the Haas School of Business, this study from the Harvard Kennedy School, and an HR-analytics project by Shell (Esther Bongenaar and Linda van Leeuwen) very recently.

From a quick scan of the articles it seems that companies that focus on Diversity and Inclusion (the two often come together) perform better when there is simultaneous attention for things like inclusive behaviour, inclusive team leadership, the absence of strong sub-groups and training for group-process skills.

For BHP the conclusions of their analytics seem to be in line with the conclusion above. “BHP Billiton’s 2013 Employee Perception Survey showed that increased inclusion correlated with increased performance”. In addition, Andrew Mackenzie, BHP Billiton’s CEO, indicated that at the company’s “most inclusive and diverse sites” performance is 15% higher.

All in all, I think we can say that diversity and inclusion can indeed impact performance if it is embedded in a broader context. Companies should investigate and measure wether they have created the right circumstances.

Should there be a 50/50 ratio, or could a similar impact be achieved at a different ratio as well?

Under the supervision of Rohini Anand, Sodexo performed their own HR-analytics project, which  focused on, among other things, this question. Although this analytics project is just one observation, the case offers a good starting point for an answer to the above-mentioned question. According to the study the male/female ratio should be between 40% and 60%. “Teams that fit within this gender balance zone generate, on average, results that are more sustained and predictable than those of teams with less than 40% or more than 60% of either gender”. For instance, gender-balanced teams achieved on average, a 12% increase in client retention; positive organic growth, growth profit and operating profit over three consecutive years. If BHP implemented the Diversity policy purely to boost company performance, it might be better to aim for a 40/60 ratio as that would be easier to achieve than a 50/50 ratio. However, if corporate citizenship is also a driver, then 50/50 might be the right target.

Is the ambition realistic?

It is hard to find input for this question, probably because the answer to this question very strongly depends on company context. For example, some industries attract fewer women and in some regions, access to education and the labour market is less straightforward for women than in other regions. Even BHP indicated they don’t really know if their ambition is realistic – they speak of an aspirational goal and indicate it will be a challenging change.

However, research in combination with analytics can provide an answer to this question. As a first step, BHP could identify the skill sets that they are looking for, and they can also formulate skills that are similar / substitutes. Next, they could scrape the Internet, or buy labour market data, to build a labour market demand and supply picture for these skills. This would give them insight in the total labour pool and the share of candidates that they are looking to recruit. Moreover, they can conduct research amongst the people in the supply base to assess company attractiveness, predict the willingness of people to relocate to other BHP locations and identify the factors that influence joining decisions (e.g. pay and development opportunities). These insights will help to calibrate and influence the supply picture. Once demand and supply have been modelled, BHP can get an idea of whether their ambition is achievable.

Will the decision create value?

Under question 1, I set out that by focusing on diversity and inclusion companies are likely to enhance performance, as long as they properly embed the concepts in the business operation. To answer this last question about value creation, it is vital to understand how much it will cost to create the more diverse and inclusive teams. Because in financial terms, value is: revenue minus costs.

To get a better understanding of the costs, BHP could look at the historical data of their more diverse teams, and investigate which costs emerged when these more diverse teams were created. One could for instance think of answering the following questions: are sourcing costs different when a more diverse pool of candidates has to be identified; do attraction costs change because we have to build a segmented labour market brand, because we may have to tap into new candidate pools and/or we may have to bring people in from further afield; do investments in retention change to keep women aboard; how much investment in training is required to establish the right behaviour, which impacts the value that can be created by focusing on diversity?

If the performance increase is higher than the total of investments made to become more diverse, then the decision will indeed create shareholder value and would probably be the right one.

 

An idea for HR: Let’s look at data instead of numbers.

By Sergio Garcia Mora
Bachelor in Labour Relations and Data Mining Student

Sergio Garcia Mora is based in Argentina. For English readers, Segio recommends the book, The ROI of Human Capital by Dr. Jac. He also recommends Argentinean author Luis Maria Cravino’s book called “Medir lo importante” (Measuring the Important) and Jose Maria Saracho’s book called “Talento Organizacional”.

You can view the original article in Spanish here.

When I started my Career on Labour Relations at the University, and I asked my classmates why they chose this career, most of them replied “because we don’t have to deal with math and numbers”, and suffering two statistics courses along the career didn’t seem so distressing.

Thinking about this situation, one of the reasons I think this happens to people who chose “soft” careers (namely HR, Psychology) is because we hit a wall thinking how hard it is to solve an equation instead of thinking of what can I do with the information the equation provides? It might be a subtle difference, but once you know that certain “maths” may help you find answers to specific issues, the negative energy that blocked your way transforms and allows knowledge to flow.

This way of thinking doesn’t exclusively belong to HR. Last year, when I started a postgraduate course in Data Mining, every single one of my classmates asked me “Can you use Data Mining in Human Resources?” and my answer was “Of course you can!” This prejudice is established because we (HR functions) are not perceived as a data-driven area (despite that we own our employees’ information when they work in our company).

“They don’t give HR a place at the Decision Table”

How are “they” giving HR a seat at the Decision Table if we don’t speak the corporate language? And the corporate language is results. If we are not able to show our own results, how do we gain a decision-maker’s trust?

And thinking of results, it’s not necessarily just saving cost and time. We can go beyond. Looking how our HR information relates with other company data, we’ll have the opportunity to drive our efforts.

Not long ago, asking Daniel Yankelevich, a key Data Mining player in Argentina (and a person I love to listen to whenever I can), what makes a miner good, he replied with 3 things:

  1. Must know the company’s business.
  2. Programming skills.
  3. Must be able to turn conclusions into actions.

Ok, perhaps programming might be discouraging to HR professionals, but:

  • Can we have business acumen? It’s a must.
  • Can we team up with other areas to conduct the analysis? It’s a good idea.
  • Can we drive conclusions into action? Yes, we can.

My suggestion is simple. Don’t get messy with math, Let’s look for relationships among data, for instance:

  • Is there any relation between our communication/training/compensation actions and   the quality of the company’s product?
  • What profiles show more turnover?
  • What are the characteristics of the frequently absent workers?
  • What do top performers have in common?

When we set out to focus on what we want to achieve, “playing” with data is much simpler. There are techniques that allow us to look for relationships between variables that don’t seem to be connected, but even without getting to that sophistication level, there’s a lot of available information to match, contrast with, that we haven’t taken advantage of yet.

People make the difference

Nowadays, many companies have a phrase like people are the most valuable asset in their Mission and Vision. So, how can we measure people’s impact on company’s results? By looking for patterns in data. Not only in HR information but finance, production, sales, safety, etc. If we, the people, generate value in every company, it’s necessary that we find ways of showing the value that we produce, and how our decisions affect business’ results.

I confess that when I was deciding what career to choose, one of the most important factors was being able to work with people and not with numbers. But when I started to dig into HR data I found curious things, like I didn’t have absenteeism issues with millennials but I did with 35-40 year-old people, and millennials showed a lower turnover than X-Generation professionals. With this data I’m able to:

  • Discuss ideas and preconceived concepts.
  • Make better decisions.

“Working with numbers” is not taking the human aspect away from the Human Resources job. On the contrary, it allows us to be precisely “more human” by providing more accurate feedback, or replying to a complaint with more precise information than vague answers or with inconsistent answers waiting for people to get tired of arguing.

This way of thinking provided me with a whole different perspective of my career. Being able to relate data with business results allows me to separate apples from oranges, evaluate our “best practices” in order to keep the ones that bring better results, letting me save money, time and be more efficient.

That’s why I say, let’s forget about numbers and math, and start focusing on data and its relationships. That’s a great way to become a strategic partner, and gain a seat at the Decision Table.

 

Making a lasting impact in HR Analytics: Proven comes before simple, but one cannot be without the other!


By Søren Kold,
Head of Analytics, Ennova
Global Employee and Leadership Index

HR analytics has been on the rise for some years now. The world has become more and more digitalized, and more data are gathered on people in organizations. The value of improving Employee Engagement has gained an increased focus in companies across the world, and the key reason being that there’s generally a wide acceptance that engaged employees performs better, are less absent, are less willing to quit their jobs, have fewer safety incidents, and produce happy and loyal customers etc. Sum that up and it’s easy to understand the business case: Engaged employees are essential for the bottom line! Add in an increased understanding within HR, that using analytics as a tool can help you make better and informed decisions. The analytical approach has helped HR succeed in moving closer to becoming a part of the business instead of being a support to the business. So, the days where you make decisions on a gut feeling should be over, right? Well, in some organizations they are, but in others, they aren’t and we still have some road to travel.

While I believe that we eventually are getting there, we still experience different maturity levels on doing HR Analytics in organizations. Therefore, as in any good fairytale, it sometimes seems like we should overcome a few obstacles, temptations you might say before we eventually get to where we want to be.

The need for better, faster and more accurate analytics must not be confused with surveys using stated importance, correlations mistakenly seen as causation and flashy dashboards promising to change the company based on a percentage favorable metric. The output should, by all means, be presented crisply with a great look and feel, easy to understand and engaging for whoever is the recipient, but the subject at matter must be treated with more substance than flash. You shouldn’t stop learning or try new things once you have understood correlations, but continue to build on each and every important aspect of succeeding with your analytics journey.

In this article, I will provide you with examples of how it’s possible to go from complex analytics to a simple interpretation making recommendations based on a proven foundation. Using analytics that can digest a complex world may give you the right results, but if you’re not able to transfer that to an understandable recommendation your analysis is not worth much.

Understanding people and the organizational context

In my job, an important task is to analyze the answers from engagement surveys and provide clients with accurate results and recommendations for improving Employee Engagement. This means that in most organizations it really doesn’t make much sense to make a single model covering everybody in the organization. Why? Because it’s important to accept that not all employees are equal when you analyze your organization. Whenever we discuss organizational behavior, a key aspect to keep in mind are the different psychological constructs that separate us from each other. Each individual has a set of psychological traits, which to some degree define how we fit a job or an organization, but also to some extent could be seen as something that affects our individual drivers of engagement (if you haven’t got the faintest idea of what I’m talking about, try to Google “Big Five”. This is an easy way to start getting into thinking about psychological differences).

Psychology is one thing but the situation people find themselves in could also influence drivers of their engagement. Try to think of a few people you know. They might work in different jobs, in different sectors, have different ages and different experience. Some might have kids while others may not. Could it be, that some of these factors affect the way they think about their jobs, and maybe their different situations make them have different priorities in their working life? One may be driven by the possibility to develop professionally and personally and another is motivated by his salary and bonuses etc. In any case, it’s our commitment to our clients, to tell them how preference structures look for employees in every department. In reality, we are most likely engaged at work by several factors, but they might not be equally important. This is why a good model should take different perspectives into account.

The model we’re using at Ennova has been developed over many years of surveys using the academia on motivational theories and in close collaboration with our clients. More than 3 million employees have been surveyed using this model.

But even though people are different on some parameters, there’s also similarities between us. Problem is, that this information is not always visible to the organization, but may be found via the patterns we discover in the answers provided, which of course is the tricky part, but sometimes there’s not always a simple solution to a complex problem.

Identifying key drivers of Employee Engagement

When we at Ennova analyze answers from Employee Engagement surveys, we do so using advanced statistical methods. Since the days of Herman Wold (Norwegian econometrician and statistician), PLS has been a widely accepted method used for analyzing intangible information such as the human capital perspective. In addition, we’re using a differentiated approach when analyzing the drivers of Engagement. With Structural Latent Class Analysis, we divide people into relevant homogenous subgroups with respect to their preferences. The analysis uses an iterative approach relying on calculated probabilities for every respondent which makes it possible to divide people into subgroups. We let people be regrouped by repeating the process until the analysis converges and respondents are placed in their final segment. For each segment of respondents, we calculate a structural equation model using PLS, which determines the effect structures for each segment. The output is latent scores for each Engagement driver, effect sizes and weights for each respondent, and the method used thus reflects the best possible guess on the true nature of each group of employees. This method gives us improved statistical fit, more precise estimates of personal preferences and an unbiased estimate of effect sizes for the independent variables in our model.

Summed up this enables us to give precise recommendations for each department within the organization based exactly on its own member’s preferences in shaping their Engagement. So rather than having one overall model which fit the complete organization poorly, we use statistical techniques to identify subgroups which in turn reflects the differences of a multi-faceted organization and its members.

Translate your analyses and make recommendations accordingly

Since the true value of analytics lies in the interpretation and ability to act upon knowledge, we put together the things each manager must focus on maximizing the potential of his team of employees. Remember that a team may consist of different employee types, but with the differentiated approach, we take into account exactly which type of employee that is, and adjust the recommendations accordingly. If we built only one model, we would, by great chance, over or underestimate recommendations for many employees.

One of the purposes of building statistical models on your HR data is to become not only predictive but prescriptive. You must eventually be able to use the knowledge you have provided to look into the future, or at least have a qualified guess. Therefore, in addition to scores and calculated effects on Engagement, we are now ready to leverage many years of data. Having many thousands of departments tracked over time enables us to be specific about how much each Engagement driver in the survey can develop. This is an important aspect in understanding the dynamics of where you should use your resources in order to improve Engagement, and adding this information to the picture enables us to adjust which focus areas each department should work with. A recommendation could be something along these lines: “The focus areas your department will benefit the most from working with are…”

  • Focus area 1
  • Focus area 2
  • Focus area 3

Underlying these recommendations would be information on scores, effects on Engagement and the potential for development. The effect sizes are of course important since they tell us something about how much Engagement changes if we’re able to develop that specific driver. Scores are important because they tell us the current state of an Engagement driver, and more importantly because they set the guideline for how much we can expect that driver to develop. It’s much easier to develop a low scoring driver than a high scoring driver, and at some point, it becomes quite difficult to maintain a high score – even for great managers. But the real perspective is met when we combine these variables: Consider choosing between 2 variables with almost equal effect on Engagement. Which one should you choose? The answer would be the one you can improve the most for the same cost, but and thus receiving the highest ROI based on increased Employee Engagement.

The use of Engagement metrics as early warning: Predicting Voluntary Employee Turnover

Griffith & Hom (2001) identified that the cost of employee turnover can be substantial. They projected the costs at 93-200 percent of each single leaver’s salary depending on his or her skill, the level of responsibility, and how difficult it is to replace them.

Identifying areas that are potentially very costly to the organization is important and establishing early warning metrics can be crucial. If you, in addition, knows what drives this metric, you have put yourself in a good position to come out on top in a highly competitive market.

We asked respondents in our annual survey the Global Employee and Leadership Index (in short GELx. The survey covers 42 countries worldwide) “Are you thinking of getting another job outside the company?” In a group of 5 possible answer choices, respondents could among others choose: “Yes, the sooner the better” or “No, I have no plans of getting a new job”. We used these two poles as proxies for employees leaving and for employees staying at their respective companies, and built a logistic regression model with that question as our dependent variable. The independent variables in our model reflected our Engagement model, consisting of employees’ assessment of the company Image, the Senior Management, their Immediate Manager, Co-operation within the company, their Job Content, their Working Conditions and evaluation of their Remuneration (salary plus benefits). In addition, we added in some important background information on respondents as well: Size of the company they work for, gender, private or public held company, age, and seniority. A total of 15.113 (15,113) respondents were part of the analysis.

The result is a calculated probability for each respondent, and figure 1 is showing the variation within the model at the individual level. When aggregating these probabilities within intervals of Engagement the results provided a clear picture (figure 2): There’s a strong relationship between the level of Engagement and the probability of voluntary employee turnover. The final proof of concept was to let the model be tested on other data, using only the intervals on Engagement as a predictor and calculating the turnover rate. Comparing these two variables the model showed a great ability to forecast what happened (figure 3): A low score in Engagement Survey 2015 equals a high turnover rate in 2016. With this tool, we can forecast coming employee turnover, and before turnover actually happens, we can identify initiatives to improve the key drivers and thereby reducing turnover and saving the company substantial costs.

Figure 1

Figure 2

Figure 3

In this article, I have tried to show, that there’s not necessarily an easy way out when it comes to understanding your business and the people who work there. You need to find the right way to present recommendations to whoever you’re reporting to, be it your manager, CHRO, CEO or a client. Be ready to prove your claims and build your arguments on data and models. They will tell you how things have been, and in some cases help you predict what’s about to happen in the future. But, we all know that the CHRO or CEO have limited time, so be simple and concise whenever you present your recommendations, and always relate your finding to the business!

Data has become the backbone of modern businesses and analytics offers great tools to make you understand, explore and add value. In a recent LinkedIn blog post, Thomas Rasmussen (Vice President, HR Data & Analytics) from Shell is advocating listening to rock music. Building on research from Stanford University he argues, that there might be benefits from listening to music while doing analyses. There is certainly a truth to that, but in addition to listening, we might also benefit from understanding, that the best of rock musicians are desperate men, and like them, you have to have something bothering you all the time! Working with analytics can be a long haul, so you need to have that inner desire and curiosity that keeps you going, but when you succeed it’s all worth it!

” I see my daddy walking through them factory gates in the rain. Factory takes his hearing, factory gives him life. The working, the working, just the working life.”

Bruce Springsteen (Factory), 1978

The Internet of People and the Changing Definition of People Analytics

Several years ago, the term “People Analytics” was equivalent to “HR Analytics.” It referenced the initiative many companies had underway to use Human Resources data to make better decisions.

Since that time, several changes in the definition of “People Analytics” have occurred. First, the land of sports analytics, propelled by the release of a movie called Money Ball in 2011 (book release was 2004), began to associate the phrase of People Analytics with predictive studies to maximize athletic team performance.

Next, in the last few years, universities have raced to implement classes in “People Analytics.” In order to get students to register for these classes, they found they could not market the class as anything related to Human Resources. In speaking with some instructors of these classes, rarely is there an HR person to be seen on the student roster. HR Analytics has no student demand. People Analytics does.

Further changing the definition of People Analytics is the introduction of wearables. These wearable objects gather data and send information to the cloud. I shall classify such objects as establishing the “Internet of People,” which I view as a subset of the “Internet of Things.” The concept of gathering information from wearables provided to your employees has become a hotly contested issue. Obviously privacy is a concern and employees will always wonder what decisions are being made about them in relation to this data collection. The mainstream media has perpetuated a view that data gathered from wearables is called “People Analytics,” further morphing the definition and attaching a negative view to this phrase.

Confused yet? You should be. In today’s publications, the meaning of People Analytics has been distorted so badly that you and the person next to you may envision something entirely different on this topic.

Perhaps it’s time for us to use the original definition of “HR Analytics” to separate our reputable use of employee and workforce data from the contentious wearables and sports analytics audiences.

Until next time,

Tracey.

Tracey is passionate about using data to solve business problems… all types of business problems. Specializing in multiple areas of business analytics, Tracey has helped well-known companies in the U.S., Canada, the UK and Europe use data to make decisions which impact the bottom line.

You can find Tracey on the web at:

Numerical Insights Web Site

Find Tracey on LinkedIn

Twitter ID: @ninsights

Interesting Books to Read

How to Turn Employees into Innovators

Without a doubt, my least popular article of all time was on the topic of innovation. So why would I dare mention that topic again?

Because it’s crucial to our success!

And by “our,” I do mean all of us. Whether you’re a small business like mine or a huge, global corporation, innovation is what keeps us ahead of the competition. I know that in three short years, my business has evolved substantially in order to meet ever-changing needs. Large, global companies must constantly innovate to keep up with the changing demands of customers.

So, if we’re in a department like Human Resources, how do we innovate? The same way everyone else does!

Generate ideas and test ideas until you either conclude it’s great, or it has no value.

If you need a bit of help getting started, there are several resources on the internet. I was reviewing a tool called KickBox this morning which was created by Adobe. It’s very impressive. The bonus news to report is that the site claims that the toolkit will be made Open Source in February. This means you can have it for free.

Briefly, here’s how it works at Adobe.

  • Employees are given a red box.
  • Inside the red box, is a 6 step process to guide innovation, a chocolate bar (for sugar motivation), a Starbucks card (for caffeine motivation) and a pre-paid card for $1000 so employees can test their idea.
  • If they make it through all 6 steps, they will be in possession of valuable information about a tested concept that they can then pitch to an executive to try get funding for the next phase.

So where will most companies fail in this initiative?

  1. They won’t carve out time for the initial meeting to happen (too busy with the day-to-day).
  2. They’ll add management approvals to the process (Adobe has none).
  3. They won’t trust their people to spend the pre-paid card on project-related items. (Remember that Google recommends that you give your people enough freedom to make you slightly uncomfortable.)
  4. They’ll add something else to the process to layer the creative employee in red tape, making it painful to participate. (One company I worked for made employees fill out so much paperwork just to log an idea that no employee would do it.)

So, go ahead and find your free 2017 toolkit and head down the path to innovation. I plan to watch the site for the February release.

Until next time,

Tracey.