People Analytics For Dummies E-Book

Mike West

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 742

Veröffentlichungsjahr: 2019

Beschreibung

Maximize performance with better data

Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them.

People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce.

• Start a people analytics project
• Work with qualitative data
• Collect data via communications 
• Find the right tools and approach for analyzing data

If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier. 

Leseprobe

People Analytics For Dummies®

Published by: John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774, www.wiley.com

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People Analytics For Dummies®

To view this book's Cheat Sheet, simply go to www.dummies.com and search for “People Analytics For Dummies Cheat Sheet” in the Search box.

Table of Contents

Cover

Introduction

About This Book

Foolish Assumptions

Icons Used in This Book

How This Book is Organized

Beyond the Book

Where to Go from Here

Part 1: Getting Started with People Analytics

Chapter 1: Introducing People Analytics

Defining People Analytics

Blazing a New Trail for Executive Influence and Business Impact

Competing in the New Management Frontier

Chapter 2: Making the Business Case for People Analytics

Getting Executives to Buy into People Analytics

People Analytics as a Decision Support Tool

Formalizing the Business Case

Presenting the Business Case

Chapter 3: Contrasting People Analytics Approaches

Figuring Out What You Are After: Efficiency or Insight

Deciding on a Method of Planning

Choosing a Mode of Operation

Part 2: Elevating Your Perspective

Chapter 4: Segmenting for Perspective

Segmenting Based on Basic Employee Facts

Visualizing Headcount by Segment

Analyzing Metrics by Segment

Understanding Segmentation Hierarchies

Creating Calculated Segments

Cross-Tabbing for Insight

Good Advice for Segmenting

Chapter 5: Finding Useful Insight in Differences

Defining Strategy

Measuring If Your Company is Concentrating Its Resources

Finding Differences Worth Creating

Chapter 6: Estimating Lifetime Value

Introducing Employee Lifetime Value

Understanding Why ELV Is Important

Applying ELV

Calculating Lifetime Value

Making Better Time-and-Resource Decisions with ELV

Drawing Some Bottom Lines

Chapter 7: Activating Value

Introducing Activated Value

The Origin and Purpose of Activated Value

Measuring Activation

Combining Lifetime Value and Activation with Net Activated Value (NAV)

Using Activation for Business Impact

Taking Stock

Part 3: Quantifying the Employee Journey

Chapter 8: Mapping the Employee Journey

Standing on the Shoulders of Customer Journey Maps

Why an Employee Journey Map?

Creating Your Own Employee Journey Map

Using Surveys to Get a Handle on the Employee Journey

Making the Employee Journey Map More Useful

Using the Feedback You Get to Increase Employee Lifetime Value

Chapter 9: Attraction: Quantifying the Talent Acquisition Phase

Introducing Talent Acquisition

Getting Things Moving with Process Metrics

Chapter 10: Activation: Identifying the ABCs of a Productive Worker

Analyzing Antecedents, Behaviors, and Consequences

Introducing Models

Evaluating the Benefits and Limitations of Models

Using Models Effectively

Getting Started with General People Models

Chapter 11: Attrition: Analyzing Employee Commitment and Attrition

Getting Beyond the Common Misconceptions about Attrition

Measuring Employee Attrition

Segmenting for Insight

Measuring Retention Rate

Measuring Commitment

Understanding Why People Leave

Part 4: Improving Your Game Plan with Science and Statistics

Chapter 12: Measuring Your Fuzzy Ideas with Surveys

Discovering the Wisdom of Crowds through Surveys

O, the Things We Can Measure Together

Getting Started with Survey Research

Designing Surveys

Managing the Survey Process

Comparing Survey Data

Chapter 13: Prioritizing Where to Focus

Dealing with the Data Firehose

Introducing a Two-Pronged Approach to Survey Design and Analysis

Evaluating Survey Data with Key Driver Analysis (KDA)

Having a Look at KDA Output

Outlining Key Driver Analysis

Learning the Ins and Outs of Correlation

Improving Your Key Driver Analysis Chops

Chapter 14: Modeling HR Data with Multiple Regression Analysis

Taking Baby Steps with Linear Regression

Mastering Multiple Regression Analysis: The Bird's-Eye View

Doing a Multiple Regression in Excel

Interpreting the Summary Output of a Multiple Regression

Moving from Excel to a Statistics Application

Doing a Binary Logistic Regression in SPSS

Chapter 15: Making Better Predictions

Predicting in the Real World

Introducing the Key Concepts

Putting the Key Concepts to Use

Understanding Your Data Just in Time

Improving Your Predictions with Multiple Regression

Chapter 16: Learning with Experiments

Introducing Experimental Design

Designing Experiments

Selecting Random Samples for Experiments

Analyzing Data from Experiments

Part 5: The Part of Tens

Chapter 17: Ten Myths of People Analytics

Myth 1: Slowing Down for People Analytics Will Slow You Down

Myth 2: Systems Are the First Step

Myth 3: More Data Is Better

Myth 4: Data Must Be Perfect

Myth 5: People Analytics Responsibility Can be Performed by the IT or HRIT Team

Myth 6: Artificial Intelligence Can Do People Analytics Automatically

Myth 7: People Analytics Is Just for the Nerds

Myth 8: There are Permanent HR Insights and HR Solutions

Myth 9: The More Complex the Analysis, the Better the Analyst

Myth 10: Financial Measures are the Holy Grail

Chapter 18: Ten People Analytics Pitfalls

Pitfall 1: Changing People is Hard

Pitfall 2: Missing the People Strategy Part of the People Analytics Intersection

Pitfall 3: Missing the Statistics Part of the People Analytics intersection

Pitfall 4: Missing the Science Part of the People Analytics Intersection

Pitfall 5: Missing the System Part of the People Analytics Intersection

Pitfall 6: Not Involving Other People in the Right Ways

Pitfall 7: Underfunding People Analytics

Pitfall 8: Garbage In, Garbage Out

Pitfall 9: Skimping on New Data Development

Pitfall 10: Not Getting Started at All

Index

About the Author

Advertisement Page

Connect with Dummies

End User License Agreement

List of Tables

Chapter 4

TABLE 4-1 A Simple Dataset

TABLE 4-2 Working with Two Variables

TABLE 4-3 Percentage of Row Total

Chapter 7

TABLE 7-1 Setting up a CAMS survey

TABLE 7-2 Net Activated Value

Chapter 9

TABLE 9-1 Headcount.EOP Detailed Active Employee List: Report Dates: 9/30/2017, ...

TABLE 9-2 Headcount.EOP: Output Table (with Filter for East)

Chapter 10

TABLE 10-1 Elements of a CAMS survey

Chapter 11

TABLE 11-1 Dealing with the Problems of Exit Surveys

Chapter 15

TABLE 15-1 Ranking variables in order of significance

Guide

Cover

Table of Contents

Begin Reading

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Introduction

You might already be familiar with how the power of data analytics has transformed the fields of marketing, sales, supply chain management, or finance. You may also be familiar with the idea that people are a company’s greatest investment. Well, like peanut butter and chocolate eventually found their way into a delicious treat, these two ideas found their way together, too — the happy result is called people analytics.

Welcome to People Analytics For Dummies, a book written for people open to the idea that there need not be any contradiction between what makes companies great places to work and great at producing business results. People analytics is built on the premise that what makes companies great is people, and that what can make more companies great when it comes to people is data analysis. Not any kind of analysis — specifically, the analysis of people at work.

In this book, you'll find an introduction to the data, metrics, and analysis at the basis of this new field called people analytics. Because it’s a new field, this may be the first time you’re hearing anything at all about it or, like most of the people doing the work today, you’re figuring it out as you go along. In any case, even if you’re familiar with people analytics already, this book may introduce you to new ways of approaching your work and may also provide you with some tips on how best to explain to others exactly what you do. (It never hurts to be able to express clearly and succinctly to others the importance of the work you do.)

About This Book

This is a book about making important management decisions about people by using data analysis rather than whim or instinct. This is a book about getting great business results while at the same time creating a great place for people to work. This is a book about finding a way to be a great company that relies on continuous feedback and learning rather than a mediocre company that's satisfied with either doing it the way it's always been done or that tries to keep up by slavishly copying the competition. This book is the recipe for getting the highest possible individual, team, and company performance while also making employees happier!

In People Analytics For Dummies, I talk about the ways that analysis can connect human resources decisions to business strategy as well as offering an overview of some of the nuts-and-bolts of how to do the analysis. You'll find out about gathering data about your employees at different stages of their careers, detecting patterns from the data, making predictions, and measuring the consequences of the actions you take. You'll find out how to use data to continuously improve the methods you use to attract, activate, and retain talented people so that you can achieve higher levels of productivity.

When I can, I include real-world examples from companies I have worked with — big and small — so that you can learn from the real world how to collect and analyze data in ways that can help you make better business decisions across a wide variety of human resources management topics: recruiting, performance, rewards, learning and development, leadership, diversity, and attrition. These examples show you the broad variety of opportunities for a smart application of people analytics.

Whether you're an executive, a human resources professional, or an analyst, you’ll find something in this book for you.

Foolish Assumptions

To get the most from this book, I assume that you

Have worked for, are working for, or want to be working for a company large enough that establishing better decisions about how you manage people can add value

Are willing to let data help you make decisions about how you identify, select, pay, develop, and manage people

Are willing to try something different than what you have done in the past or than what other companies are doing

Are comfortable reading about business strategy, systems, science, and statistics

Have access to some people data or at least want to collect and analyze people data

Are looking, of course, for an accessible source that keeps it as simple as possible and provides practical advice about how to get started in the real world, as opposed to what you might find in an academic textbook or scientific journal

Icons Used in This Book

Throughout this book, you’ll see these little graphical icons to identify useful paragraphs:

The Tip icon marks tips and shortcuts that you can take to make a specific task easier.

The Remember icon marks the information that’s especially important to know. To siphon off the most important information in each chapter, just skim these paragraphs.

The Technical Stuff icon marks information of a highly technical nature that you can safely skip over without harm.

The Warning icon tells you to watch out! It marks important information that may save you headaches. Warning: Don’t skip over these warnings!

How This Book is Organized

The book is arranged into five self-contained parts, each composed of several self-contained chapters. By self-contained, I mean that I do my best to tell you everything you need to know about a single topic inside each chapter. But I admit that more than a few times I had to put references to other parts of the book when it wasn’t reasonably possible to cover in one chapter everything that’s important to know.

The possibilities for adventure are truly endless, but start where you are right now. Whether you’re an executive, HR professional, or analyst, you'll find something worth reading in People Analytics For Dummies.

Here is an overview:

Part 1: Getting Started with People Analytics

These early chapters serve as a primer on people analytics. In this part, you learn to walk before you run, but what you find here lays the foundation for all that comes later. You’ll see my definition of people analytics and find an introduction to its important concepts, applications, and options. You may be especially pleased at the nontechnical nature of the first part. Not much bit-bytes or psychobabble is necessary because, as you see in Part 1, people analytics is about business first, people second, analysis third, and systems last.

Part 2: Elevating Your Perspective

It is unfortunate that most people think of analytics as something that is necessarily abstract, complex, or foreign to what they do. In the beginning of Part 2, you get to see how simply counting people up in different ways and looking at the results can help you gain new perspectives on things you do all the time. The fact is, the methods of people analytics need not be abstract, complex, or foreign — they can just be empirically valid ways of better doing what you always do.

If you read the entire part, you'll have learned some basic methods to get more perspective on how people produce value for businesses (or don’t), have gained insight into why results vary, and have seen how, with careful attention to the right level of detail, you can focus your efforts to get value out of analytics faster. The absence of a business value orientation leads analytics into dead ends and trivial pursuits.

Part 3: Quantifying the Employee Journey

In this part, I define a universal measurement framework for human resources centered around two different but related concepts: the employee journey and something I call the triple-A framework"

Employee journey:

I call the stages employees go through from the day they become aware of the job opportunity to the day they eventually exit the company the

employee journey.

Taking this holistic, long-term point of view implied by this term helps you see patterns you would not otherwise have seen had you organized your analysis in any other way. Also, seeing the company through the eyes of employees can help you see the world in a totally new and different way. Sounds clichéd, but it’s true.

Triple-A framework:

The employee perspective is important, but for obvious reasons it has to be paired with the needs of the business as well. The triple-A framework provides the fundamental measurements and analysis for the three big people-related problems each company needs to solve if they hope to grow as a business: attracting talent, activating talent, and controlling the rate of talent exit (attrition).

The combination of the employee journey and the triple-A framework can unify otherwise disparate and competing efforts by providing a single, unified measurement framework that relates employee and company needs with data.

After an introduction to the employee journey in Chapter 8, you'll find more detail on the methods of measurement and analysis in each of the three A’s that follow: attraction (Chapter 9), activation (Chapter 10), and attrition (Chapter 11).

Part 4: Improving Your Game Plan with Science and Statistics

Analytics are all about using data to increase certainty. This is rooted in, at a minimum, math and science, but the analysis of people builds on the knowledge of diverse methods and caveats developed from hundreds of years of research in psychology, sociology, social psychology, and behavioral economics. Most of the current writing on people analytics is either so high-level as to not include any mention of how-to specifics or is pretty difficult to read if you don’t already have an extensive background in systems, behavioral science, or statistics. I can’t do justice to anything that is typically taught in a 6- to-8-year PhD program for the aforementioned topics, but I have carefully selected a few versatile tools that can get you started on your journey and that you can keep using for a lifetime of contributions.

Part 5: The Part of Tens

If you have ever read another book in the For Dummies series, this part of the book is like seeing an old friend again — the friend might be wearing a different outfit, but you will recognize the person right away. The Part of Tens is a collection of interesting people analytics learnings, advice, and warnings broken out into ten easy-to-digest chunks. There are ten misconceptions, ten pitfalls, ten design principles and the like. These chapters crystalize some concepts you get a chance to read in the rest of the book, or a way to get right to the concepts that matter if you haven’t.

Beyond the Book

It used to be that a book started on the first page and ended on the last — not any more. The digital revolution has not just changed the way we buy books, it has also changed the way we write and read books. I have created a plethora of online resources that go together with this book to assist you on your people analytics journey. These items fit more readily on the World Wide Web than they do between the covers of the book (and in doing so saves a few trees in the process). Importantly, these resources can be updated, searched, shared, cut and paste from and downloaded as pdfs.

Two resources I am the most excited about sharing are the HR Metric Definitions Guide and a guide to great sample employee survey questions. At the current time, these are the most comprehensive mainstream sources for obtaining information in this format.

Extras: All People Analytics For Dummies online support resources are accessible for easy download at www.dummies.com/go/peopleanalyticsfd.

HR Metric Definitions Guide:

Find hundreds of HR metric definitions following a standard convention, organized by topic (Appendix A).

Great Employee Survey Questions:

Find hundreds of great employee survey questions that follow a standard convention, organized by topic (Appendix B).

Job Analysis:

Get started with the crucial task of job analysis (Appendix C).

Competency Analysis:

Learn how to measure competencies with competence (Appendix D).

Ten Things to Set You On the Right Path When You Analyze Attraction:

Here's a great Part of Tens we just couldn’t get fit in the book. (Appendix E).

Ten Counterintuitive but Unifying People Analytics Design Principles:

And the fun never stops! Yet another Part of Tens for your reading pleasure! (Appendix F).

Cheat Sheet: If you are looking for the traditional For Dummies Cheat Sheet, visit www.dummies.com and type People Analytics For Dummies Cheat Sheet in the Search box.

People analytics is a vast domain containing a lot to learn — human resource management, behavioral science, technology systems and statistics, for starters. Unfortunately, one book cannot do justice to all of these topics, but fortunately that’s why there is more than one book in this world (and people to help write them).

Aside from an introduction to something you may not have known much about before, what I aim to do in this book is cover that area of knowledge necessary for a successful application of people analytics not already covered by other books. I provide a unique (if not sometimes strange) point of view about what really matters, honed over many years of practical experiences in the field. What I have to say often isn’t what people thought they would find, but I have seen success and I have seen failure, and I stand by what I think is important enough to share in this format. If you are looking to obtain more depth in a specific technical domain, there are plenty of resources you can turn to in order to go deeper — not the least of which are other For Dummies books.

Other For Dummies books: You can use a number of related books to drill down into topics I could only briefly touch on in this book — for example, Data Warehousing For Dummies (by Thomas C. Hammergren), Business Intelligence For Dummies (by Swain Scheps), SQL All-in-One For Dummies (By Allen G. Taylor), Python For Dummies (by Stef and Aahz Maruch), Predictive Analytics For Dummies (by Anasse Bari, Mohamed Chaouchi, and Tommy Jung), Data Science For Dummies (by Lillian Pierson), Business Statistics For Dummies (by Alan Anderson), R For Dummies (by Andrie de Vries and Joris Meys), Statistical Analysis with R For Dummies (by Joseph Schmuller), Social Psychology For Dummies (by Daniel Richardson), Excel Dashboards & Reports For Dummies (by Michael Alexander), Data Visualization For Dummies (by Mico Yuk and Stephanie Diamond), Tableau For Dummies (by Molly Monsey and Paul Sochan), and Agile Project Management For Dummies (by Mark C. Layton and Steven J. Ostermiller), all published by Wiley. Any and all of these books can produce valuable knowledge, skills, and abilities that can be used to become a more effective leader, implementer, and consumer of people analytics.

Where to Go from Here

You don’t need to read this book from cover to cover. You can, if that strategy appeals to you, but it’s set up as a reference guide, so you can jump in wherever you need to. Looking for something in particular? Take a peek at the table of contents or index, find the section you need, and then flip to the page to resolve your problem.

Part 1

Getting Started with People Analytics

IN THIS PART …

Discover exactly what people analytics is

Make the business case for a people analytics project and figure out where to begin (all at the same time!)

Understand the differences between an insight-oriented analytics project and an efficiency-oriented analytics project

Get acquainted with a matrix of current options for managing people analytics moving forward

Chapter 1

Introducing People Analytics

IN THIS CHAPTER

People analytics, defined

Examining how some businesses already analyze people data

Starting your first people analytics project

A business consists of people who work on behalf of the company (employees) doing things for other people who don’t work for the company (customers). Business decisions about people working for the company — who to hire, where to find them, what to pay them, what benefits to provide, whom to promote, and countless other decisions — have a substantial unseen impact on the company’s capability to meet customer needs, bottom-line performance, and reputation.

Traditionally, the way the leaders of companies have made human resources-related decisions has been based on gut instinct, copying what other companies are doing, tradition, or compliance with government mandates.

Today, many business decisions are now being made with data. What customer segments to focus on, what product feature improvements to make, what projects to invest in, and where to put a new store are just a few of countless examples of important business decisions that are increasingly made with data. If you go into a board meeting or participate in an investor phone call, you will see that the most important parts of the discussion are all about a series of important numbers recorded in the balance sheet, what the company is seeing in other numbers that suggest actions that may impact the balance sheet, and whether or not previous actions that promised to impact the numbers in the balance sheet have actually done so. The conversation may drift from abstract to tangible and back to abstract again, but numbers serve the purpose of keeping the conversation anchored to what is real and to drive accountability for real results.

Fortunately, now you can use data for human resources-related decisions, too. Thanks to the prevalence of human resource information systems, plus the wide-scale accessibility of modern data collection, analysis, and presentation tools, human resources-related decisions can be made with data just like countless other business decisions.

In this chapter, I define the term people analytics and talk about some of the ways that companies I’ve worked with have used a human resource approach informed by data to solve real-life business problems. Then I describe how you also can add people analytics to your arsenal — and increase your people data savvy, too.

Defining People Analytics

At a high level, people analytics consists simply of applying evidence to management decisions about people.

More specifically people analytics lives at the intersection of statistics, behavioral science, technology systems, and the people strategy.

People strategy means making deliberate choices among differing options for how to manage a group of people.

Figure 1-1 illustrates how people analytics joins together these four broad concepts (statistics, science, systems, and strategy) to create something new that didn’t exist before.

FIGURE 1-1: People analytics is what happens when human resources professionals realize the power that a good dataset gives them.

Many forward-thinking companies are already realizing the benefits of evidence-based decision making in human resources. To identify what other people think people analytics is, I rounded up 100 job descriptions related to people analytics from job boards. To summarize, I created a word cloud from the words in those job descriptions; it appears in Figure 1-2.

FIGURE 1-2: Creating a word cloud is a kind of data analysis to identify and visualize trends in vocabulary.

If you’re not already familiar with word clouds, this is how they work: The more frequently a word appears in the text that you’re analyzing, the bigger and darker that word looks in the word cloud. You can tell from the figure that data, analytics, human resources (HR), and business must be central concepts to people analytics.

These 100 job descriptions are from Human Resources department that are ahead of the pack in using hard data and analysis as decision-making tools. The insights data is providing these companies gives them an advantage over companies that do not yet know how to do these things. A vast majority of companies do not yet have people analytics and most people do not even know what people analytics is. That being the case, you, by learning about people analytics, will be in a great position to differentiate yourself among your peers (and your company among its competitors).

Solving business problems by asking questions

Like all business analysis disciplines, people analytics offers businesses ways to answer questions that:

Produce new insight

Solve problems

Evaluate the effectiveness of solutions and improve going forward

Produce new insight

Donald Rumsfeld once said, “There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don't know we don't know.” Donald Rumsfeld can get his words a little twisted up, but to finish his point for him: the most perilous things in this world for you are the things you should know but don’t know you should know. One of the great contributions people analytics can make to you is to reveal some of the perilous things you don’t know and don’t even know you should know but in fact should know.

This unknown unknowns’ problem can be epitomized by an experience I had with a large pharmaceutical company. This company was very successful. It had an over hundred-year history of scientific achievement and business success. This company was a leader and financial powerhouse in its industry, if not all industries. They were a great company and they knew it.

With a smart, scientifically-oriented management team, the company tried to measure nearly everything. As a result, it was among some of the first companies to apply rigor to human resources with data. This is how I got stared in the field of people analytics before we even called it people analytics. After working at this company, I went on to do this work at other companies, but work in the people analytics field was few and far between back in the early days.

One of the earliest data-oriented human resource activities at this great pharmaceutical company was to participate in a common employee survey conducted across many companies, facilitated by a consulting firm that would provide confidentiality to everyone involved. This survey allowed the company to compare itself as an employer against a selection of the highest-performing companies across all industries across roughly 50 aspects of the employee experience using roughly 100 survey items. A few examples of the categories of employee experience the survey measured were: employee opinion about the company’s prospects for future success, leadership, managers, pay, benefits, opportunities for learning and development as well as attitudes such as overall satisfaction, motivation, and commitment to the company.

In reviewing the results, it was no surprise to all that this well-run company performed above other high-performing companies in nearly all categories of the survey. Employees at this company were on average more committed, more motivated, and happier than employees at other companies and all of this could be validated statistically.

What was surprising to everyone was that the company performed slightly below other high-performing companies in a set of questions the survey referred to categorically as Speaking Up. The Speaking Up category represented agreement or disagreement with statements that indicated employees felt the company provided a safe environment for them to express their concerns or disagreements with their superiors. This finding seemed odd, because everyone talked about how the company had a history of making decisions by consensus. When young, intelligent scientists joined the company, they were told to be aware of the importance of consensus in the company’s culture and should therefore expect to work together with others more than they might have had to do in previous environments.

Given the seeming oddness of the Speaking Up finding, and that the company had performed well on all other questions on the survey, no substantive new actions were decided. There was some concern expressed by the head of human resources about the Speaking Up items, but at the time there was an ongoing debate among the executive leadership team about whether or not the company should intentionally break its culture of consensus decision-making in order to keep up with new competitors. At the time, the assessment of the leadership team was that, overall, the survey results were good and the Speaking Up issue must have just been echoes of their effort to change the culture for the better.

No one at the time foresaw the connection between the survey findings and the disaster that would ensue next. Around that time, a previously successful but bullheaded research director had disregarded the concerns of some scientists about a possible safety issue with a drug. The safety issue was not crystal clear at that time, but the issue should have received more attention. The executive had a reputation for having a big ego, but he also delivered results for the company, so the company let him win this argument. Time and attention costs money. The scientists’ concerns about the drug were squelched in favor of progress. The result of rushing ahead was a drug that later had to be recalled — a foolish mistake that risked lives, cost the company billions of dollars, and nearly took down the company for good. At the direction of the bullheaded director, the company pushed through a pharmaceutical product that should have been scrutinized further. Specifically, no scientists should have been made to feel unsafe to express their opinion and all credible concerns should have been researched more thoroughly before taking the drug to market.

What this example shows is that even simple early efforts in people analytics — a seemingly trivial employee survey — can deliver new insight that is not obvious or trivial. The result in this case may not be the best example of successful people analytics, but it illustrates the potential in ways that success wouldn’t have. Unfortunately, at the time nobody knew that the weakness identified in the survey was so important. The survey produced an insight that blew in the opposite direction of what the executives believed and so the weakness that had been correctly identified was disregarded. Mysteriously, the employee survey had actually predicted the reason for the company’s near demise — it was providing access to an unknown unknown. In short, the survey was warning us about something the company otherwise could not have known was important. Had the company taken the Speaking Up issue more seriously, executives could have put in place a way for the concerned scientists to express themselves so the bullheaded director could have been checked, preventing the giant mistake. Now I know how important it is to take even a basic employee survey effort very seriously, because you don’t know what you don’t know, but when you ask a lot of questions you have a flying chance of finding out what you don't know.

Solve problems

Data can also help you devise solutions to known problems.

A children’s hospital knew that the attrition rate for nurses in their first year was 25 percent. This means that 1 in 4 nurses hired would leave the company in the first year they were employed by the company. In contrast, the average employee attrition rate was only 10 percent, meaning 1 in 10 people overall would leave the company in a given year. Therefore, early tenure nurse attrition was 2.5 times worse than average attrition. Even worse, early tenure attrition is a self-reinforcing problem, because if you change nothing there was a fairly high chance the replacement may also leave as quickly and around and around you go.

For good reasons, the hospital wanted to bring that early tenure nurse attrition rate down. Each nurse exit in the first year had to be replaced by another nurse that had to be identified, hired, onboarded, and trained. Of course, hiring and training new nurses’ costs money, but, more importantly, new nurses are less familiar with how to deal with complex situations and more likely to make mistakes than experienced nurses.

Some analysis of applicant and employee history data showed that the hospital could hire nurses more likely to stay by simply hiring more experienced nurses, rather than nurses straight out of nursing school. Seems obvious to say now, but they didn’t really know how much of a difference it would make for them operationally until they looked at the data. While more experienced hires have to be paid more, the data showed they were also more likely to be successful and they were more likely to stay with the children’s hospital beyond their first year. By focusing on hiring the candidates with the characteristics that predicted longevity, the data showed the hospital could reduce the overall first-year attrition rate from 25 percent to 15 percent. The reduction in attrition on the cost of recruiting and training would more than offset the cost of spending more money to hire experienced nurses from the outset. As time went on, the attrition rate of nurses decreased, costs went down, and patient safety measures went up.

Evaluate solutions and plan to improve

You also can use data to evaluate the effectiveness of solutions on a small scale so you can make sure the solutions will actually work before implementing them more broadly. Experiments can provide a dataset that allows testing ideas in ways that prevent costly mistakes (facilitating improvement) before rolling out ideas more broadly.

A pet store chain had a history of keeping track of standard retail measures, like same store sales and customer satisfaction, as well as people-related measures, like employee enthusiasm and knowledge of pet-related topics. Measuring both kinds of data together helped the pet store chain uncover correlations between how it hired, trained, and rewarded employees in the stores and the goals it was trying to achieve: increased customer loyalty and increased store sales.

By looking at the employee and customer data together, the company knew many things that other companies could only dream of knowing. For example, the pet retailer knew that the more employees in the stores knew about a pet topic, the more the store would sell in that pet topic area. For example, if people in the store knew a lot about frogs, they would sell more frogs. If they knew less about birds, they sold less birds and bird-related supplies, and so on and so forth across every category of pet. If store employees had the knowledge necessary to capture the imagination of the customers as well as help the customer solve their pet problems, the customer would, over time, spend more money at the store. As a result of this information, the pet retailer consciously hired, trained, and rewarded employees in ways designed to increase employee knowledge about pet topics. The pet retailer also used test results to identify stores that needed training, measure the results of that training, and assess its impact on the bottom line.

Despite their best efforts, however, the pet store chain was seeing increased competition from big-box retailers, grocery stores, and online retailers, which made it difficult to grow revenue profitably. Big-box retailers, grocery stores, and online retailers were starting to stock many of the same items as the pet retailer and they could offer these items at a lower cost. If the pet retailer decided to compete on cost, it would put pressure on the profit margins of the pet retailer because the pet retailer didn’t have other items they could mark up to make up for the losses in the items they marked down to compete. To make matters worse, this was in a period of economic downturn and increasing gas costs. Customers were condensing their shopping trips to as few store locations as possible and they were selecting the lowest-priced locations with the largest range of products. The bottom line is that less customers coming into the pet stores meant less sales.

The pet store needed to get a handle on its situation, and so it embarked on some new store-level experimentation and analysis. One of the experiments the pet retailer embarked on was to use some of the square footage available in some of the stores to offer pet services in addition to pets and pet supplies. Examples of pet services include: dog grooming, doggie daycare, dog training, and pet health clinics. The theory was that services would provide a reason to draw more people into the stores and, just as increasing pet knowledge increased sales, the pet retailer hoped that offering services would do the same. But no one really knew if this was true.

In the beginning, the services were not offered at all pet store locations. The expertise required to offer these services required new company training and the employees hired to perform these roles needed to be more skilled, which meant they also needed to be paid more. The company had to learn how to source, hire, train, and pay entirely new types of people for entirely new types of jobs than they were accustomed to in the past. Rolling this idea out to all stores — without a period of observation and learning — could bankrupt the company. By choosing a small number of stores to start with, the company could measure the impact of the changes, assess their performance, and assess what to do next. If the experiment with the new services was working, the services could be expanded to more stores — if not, then the service program could be modified or abandoned. If the company had implemented the services in all stores, then it would not be able to assess if they were working or not and it would be very risky.

The way to analyze data from an experiment of this nature is straightforward. The pet retailer chose a small set of stores to usher in the new pet services and chose another set of stores without the services to act as a comparison. With relatively simple math, the pet retailer was able to identify the impact of the services on customer store visits, sales, and loyalty using the same data and metrics they had already been using just by comparing locations to each other. The experiment validated that adding services in fact increased store visits, overall customer spending, and customer loyalty in the stores that offered services versus those stores that did not. It may seem obvious that when people went to the pet store to get Fido groomed that they also would be more likely to purchase other items. A less obvious finding was that those customers that got Fido groomed also spent more on Fido over the entire lifetime of Fido, not just at those visits when Fido and the pet parent were in the store together for the grooming. By offering services, the pet retailer was both attracting and creating better lifetime customers. The inevitable result is increased sales.

Through its analysis, the pet retailer was able to validate the fact that its investment in people to provide services was working. The stores where services were available produced more of the business outcomes the company was looking for and those stores that did not provide services did not. The solution for the company then became more certain — expand the program. Additional research questions included the mix of services to offer in the stores and how to scale the services to more stores with equal quality, but the company knew enough to proceed and could evaluate these more complex questions as they worked services into more stores in differing packages.

Further analysis of the store-level employee data over time indicating that the satisfaction and retention (reduction of attrition) of the new service employees in the store had more impact on customer loyalty and sales than that of other types of store employees — cashiers or stockers for example. Across all jobs, the more distinct pet-related knowledge for the job required, the more impact attrition in that job had on the pet retailer’s success. With this information, the company prioritized how it allocated its people budget to reduce attrition in key jobs, as opposed to spreading their resources out thinly across all jobs, which might produce inferior results — or no results — while spending the same amount of money. The pet retailer learned that employee attrition matters more in certain key jobs and, since profit margins were thin, they had to prioritize where to spend their money to get the best results.

Many people do not like to talk about differences in pay, but the reality is that there are always differences in pay based on many factors. Job responsibility is a valid criterion for differentiating pay. It is natural, in everyone’s best interest, and generally agreed to be fair, for each company to focus its resources on the unique jobs and people that make it successful. Furthermore, any entry level store worker that wanted to learn a more lucrative service job had the opportunity to apply — and frequently they did. Having a ladder of jobs of increasing skill and pay made working for the pet retailer more of a long-term career opportunity to potential employees, rather than just a fun, short-term job fix. By adding higher-paying services roles, the pet retailer was able to make themselves more attractive to both the customers and employees it wanted to attract for the long term.

Using people data in business analysis

People are the face, heart, and hands of your company. All companies depend on people in every aspect of their business because people

Empathize with customers wants, pains, and problems

Create and improve products and services

Design, manage, and execute the strategies, systems, and processes that help everyone work together toward a productive enterprise

Considering how important people are to the performance of each company, it’s amazing that more companies don’t study employee data for insight into their businesses. Your company probably hires experts with advanced skills to analyze your finances, equipment, and workflows, so why isn’t anyone studying the people who use these things?