Predictive Analytics Times
Predictive Analytics Times
EXCLUSIVE HIGHLIGHTS
Wise Practitioner – Deep Learning World Interview Series: Domenic Puzio at Capital One
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Will Scheck and Andy Jacob at Caterpillar
 In anticipation of their upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Greg Zeeman and Joe DeCosmo at Enova International
 In anticipation of their upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Syed Mehmud at Wakely Consulting Group, Society of Actuaries
 In anticipation of his upcoming conference...
The “Data Science” of Computer Science and IT Is Not the Only Data Science
 For reasons perhaps having to do...
Wise Practitioner – Predictive Analytics Interview Series: Christina Hoy at Workplace Safety and Insurance Board (WSIB)
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Richard Lee at John Hancock Financial
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Kevin Pratt at ZZAlpha Ltd.
 In anticipation of his upcoming conference...
Explaining Deep Learning by Making AI Transparent
 For today’s leading deep learning methods...
Wise Practitioner – Predictive Analytics Interview Series: Carmen Fontana at Centric Consulting
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Olaf Menzer at Ingram Micro
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Nishant Sharma at Charter Communications
 In anticipation of his upcoming conference...
Mind Your Own Text: Public Data for Political Insights
 Who is your favorite president? Can...
Wise Practitioner – Predictive Analytics Interview Series: Theresa Kushner at Dell EMC
 In anticipation of her upcoming conference...
The Top JavaScript Data Visualization Packages, Ranked
 Transitioning from academia to industry in...
Wall Street and the New Data Paradigm
   Wall Street is big business,...
The Top Distributed Computing Packages for Data Science, Ranked
 Transitioning from academia to industry in...
A Chicken/Egg Problem: Management or Analytics Software?
 Since time immemorial, human beings have...
Five Ways to Select a High Value Predictive HR Project
 Originally published at Talent Analytics, Inc....
Feature Engineering vs. Machine Learning in Optimizing Customer Behavior
 The debate on this topic is...
Wise Practitioner – Predictive Analytics Interview Series: Stephen Morse, Advisor at Neudata
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Dongyang Fu and Wen Shi at Concord Advice
 In anticipation of their upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Ron Cowan at Snowforce Data
 In anticipation of his upcoming conference...
The Harvard Business Review Video Interview: Eric Siegel on Predictive Analytics
 Originally published by Harvard Business Review...
New-Age Machine Learning Algorithms in Retail Lending
 Originally published in KDNuggets.com    ...
Machine Learning Tip: Nested Cross Validation – When (Simple) Cross Validation Isn’t Enough
 Several scientific disciplines have been rocked...
Wise Practitioner – Predictive Analytics Interview Series: Anna Kondic at Merck
 In anticipation of her upcoming conference...
Automation and Its Impact on Predictive Analytics – The Increasing Importance of the Hybrid-Part 3
 In my last article, I discussed...
Ten Things Everyone Should Know About Machine Learning
 This article originally appeared as an...
Wise Practitioner – Predictive Analytics Interview Series: Lukas Vermeer at Booking.com
 In anticipation of his upcoming conference...
15 Steps for Selecting a Talent Assessment Solution that Predicts Business Performance
 Over the past 30+ years, businesses...
Wise Practitioner – Predictive Analytics Interview Series: Feras Batarseh at George Mason University – George Washington University
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Anasse Bari, New York University
  Wall Street and the New...
Improved Customer Marketing with Multiple Models
 Data miners employ a variety of...
Data Science: Screening by Religion a Blunt Instrument for Security
 This commentary first appeared in the...
Wise Practitioner – Predictive Analytics Interview Series: Steve Weiss, at LinkedIn
In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Emilie Lavoie-Charland at The Co-operators
 In anticipation of her upcoming conference...
Doppelganger Discovery: How Baseball Sabermetrics Inspires Predictive Analytics
 This author will present at Predictive Analytics...
Predicting Fraud: Another Not So Easy Task
 As I have stated in previous...
Are You Practicing “Bad Data Science” with your Pre-Hire Talent Assessments?
 Talent Analytics uses data gathered from...
Wise Practitioner – Predictive Analytics Interview Series: Leslie Barrett at Bloomberg L.P.
 In anticipation of her upcoming conference...
Why Data Science Argues against a Muslim Ban
 From the perspective of data science,...
Wise Practitioner – Predictive Analytics Interview Series: Andrew Burt at Immuta
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Feyzi Bagirov at Becker College
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Jack Levis at UPS
 In anticipation of his upcoming keynote...
Employee Life Time Value and Cost Modeling – Part 3
 Employee Tenure in a “Survival Analytics”...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Richard Semmes at Siemens PLM
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Edward Shihadeh at Auspice Analytics, LLC
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Emily Pelosi at CenturyLink
 In anticipation of her upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Holly Lyke-Ho-Gland and Michael Sims at APQC
 In anticipation of their upcoming conference...
Automation and its impact on Predictive Analytics-Creating the Analytical File
 In my last article, I discussed...
Wise Practitioner – Predictive Analytics Interview Series: Natasha Balac at Data Insight Discovery, Inc.
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Bryan Bennett at Northwestern University
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: David Talby at Atigeo
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer
 In anticipation of his upcoming Predictive...
Book Review: Weapons of Math Destruction by Cathy O’Neil
 Originally published in Analytics Magazine Book:...
Wise Practitioner – Predictive Analytics Interview Series: Angel Evan at Angel Evan, Inc.
In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Paul Speaker at The Dow Chemical Company
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: George Iordanescu at Microsoft
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Afsheen Alam at Allstate Insurance
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Jennifer Bertero at CA Technologies
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Michael Dessauer at The Dow Chemical Company
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Steven Ulinski at Health Care Service Corporation
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Lauren Haynes at The University of Chicago
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Daqing Zhao at Macy’s
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Kevin Zhan at The Advisory Board
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Halim Abbas at Cognoa
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Ben Taylor at HireVue
 In anticipation of his upcoming Predictive Analytics...
Employee Life Time Value and Cost Modeling
 Understanding the Most Expensive Asset Practically...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Andrew Marritt at OrganizationView GmbH
 In anticipation of his upcoming Predictive Analytics...
Interview with Eric Siegel: Popularizing Predictive Analytics with Song and Dance
  Originally published in l’ADN (in...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Sue Lam at Shell
 In anticipation of her upcoming Predictive Analytics...
Case Study: Hotel Occupancy Forecasting’s Big Payoff
 This Predictive Analytics story started with...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Mike Rosenbaum at Arena
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Darryl Humphrey at Alberta Blue Cross
 In anticipation of his upcoming conference...
The Evolving State of Retail Analytics in CRM
 The Traditional State The world of...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Feyzi Bagirov at 592 LLC and Harrisburg University of Science and Technology
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Craig Soules at Natero
 In anticipation of his upcoming conference...
Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall
  In this excerpt from the...
Wise Practitioner – Predictive Analytics Interview Series: Ashish Bansal and John Schlerf from Capital One
 In anticipation of their upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Kristina Pototska at TriggMine
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Frédérick Guillot at The Co-operators General Insurance Company
 In anticipation of his upcoming conference...
Predictive Analytics vs. Prescriptive Analytics
 We have all heard and seen...
Interview with Prof. Dr. Wil van der Aalst, Eindhoven University of Technology
 Exclusive interview with Prof. Wil van...
Data Story Telling: Bringing Life to Your Data
 There is no doubt that a...
Contextual Experience Innovation
  [Title Image Abbreviations: CRM –...
Are Random Variables a Fact of Life in Predictive Models?
 In some of the more recent...
Managing Shifting Priorities in Exploratory Data Science Projects
 After working with a client’s data...
Breaking into Analytics: 5 “Musts” for your Career Transition
 In our data-rich society, corporations of...
How Predictive Analytics Can Fuel Innovation for Manufacturing
 Industry leaders like to use the...
Rexer Analytics Data Science Survey – Highlights (New)
  White Paper with 2015 survey...
How Can Predictive Analytics Help Your Bank or Fintech Company?
 Predictive analytics encompasses a powerful set...
The Role of Feature Engineering in a Machine Learning World
 Artificial Intelligence(AI) continues to be the...
The Expansive Deployment of Predictive Analytics: 22 Examples
  The future is the ultimate...
Nine Bizarre and Surprising Predictive Insights from Data Science
  Data is the world’s most...
The Trick to Predictive Analytics: How to Bridge the Quant/Business Culture Gap
  This article is excerpted from...
Wise Practitioner – Predictive Analytics Interview Series: Robin Thottungal at U.S. Environmental Protection Agency
 In anticipation of his upcoming conference...
How Hillary for America Is (Almost Certainly) Using Uplift Modeling
  In this article, I provide...
Wise Practitioner – Predictive Analytics Interview Series: Miguel Castillo at U.S. Commodity Futures Trading Commission
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor Hotel Solutions
 In anticipation of his upcoming keynote...
Exploring the Toolkits of Predictive Analytics Practitioners — Part 2
 Continuing on our discussion from last...
The Danger of Playing It Safe
 Research shows that people tend to...
Manufacturing Operations: Machine Learning to Separate Actionable Trends from False Alarms
 Predictive analytics is increasingly becoming the...
Predictive Analytics Basics: Six Introductory Terms and The Five Effects
 Here are six key definitions—and The...
Wise Practitioner – Predictive Analytics Interview Series: Ken Yale at ActiveHealth Management
 In anticipation of his upcoming keynote...
Wise Practitioner – Predictive Analytics Interview Series: Frank Fiorille at Paychex, Inc.
 In anticipation of his upcoming conference...
The Real Reason the NSA Wants Your Data: Predictive Law Enforcement
 The NSA can leverage bulk data...
Wise Practitioner – Predictive Analytics Interview Series: Scott Zoldi at FICO
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Klein at Miles & More GMbH
 In anticipation of his upcoming conference...
Book Review: Predictive Analytics for Newcomers and Nontechnical Readers
 The book reviewed in the article,...
Wise Practitioner – Predictive Analytics Interview Series: Meina Zhou at Bitly
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Shantanu Agrawal at Centers for Medicare & Medicaid Services
 In anticipation of his upcoming conference...
Infographic – Discover Predictive Analytics World for Business 2016
 Predictive Analytics World continues to grow...
Exploring the Tool kits of Predictive Analytics Practitioners — Part 1
 Tools, tools, and more tools continue...
The Power of Data Science for Predictive Maintenance is Only Just Being Tapped
 Future of Automotive Servicing and Preventive...
Wise Practitioner – Predictive Analytics Interview Series: Madhusudan Raman at Verizon
 In anticipation of his upcoming conference...
Need a Data Scientist? Try Building a ‘DataScienceStein’
 Organizations are finding that hiring qualified...
Wise Practitioner – Predictive Analytics Interview Series: Sanjay Gupta at PNC Bank
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Brian Reich, Former Director at The Hive
 In anticipation of his upcoming conference...
AnalyticOps: A New Organizational Role So Your Company Can Monetize Analytics
 There is no doubt that data...
Wise Practitioner – Predictive Analytics Interview Series: Gary Neights at Elemica
 In anticipation of his upcoming conference...
Getting Started with Predictive Analytics – an Interview with Eric Siegel
 Data science and predictive analytics are...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Sarmila Basu at Microsoft Corporation
 In anticipation of her upcoming conference...
Are Pre-hire Talent Assessments Part of a Predictive Talent Acquisition Strategy?
  Over the past 30+ years,...
Wise Practitioner – Predictive Analytics Interview Series: Dae Park and Vijay D’Souza at Government Accountability Office (GAO)
 In anticipation of their upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott of SmarterHQ
 In anticipation of his upcoming conference...
Opportunities and Challenges: Predictive Analytics for IoT
 There is a clear sense in...
Feature Engineering Within the Predictive Analytics Process — Part Two
 In the last article, I discussed...
HBO Teaches You How to Avoid Bad Science
 Do you know what p-hacking is?...
Jim Sterne’s Book Review of “Predictive Analytics” by Eric Siegel
 Book review originally published in the...
The Big Picture: Today’s Data Analytics Stack
 Enterprises are inundated with data from...
Taking Action on Technical Success: A Fable of Data Science and Consequences
 Note: This story is fiction, but...
Analytics is (often) a Faith-Based Business
 If you follow data science topics...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Chris Labbe at Seagate Technology
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Peter Frankwicz at Elmet Technologies
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Text Analytics Interview Series: Dirk Van Hyfte at InterSystems Corporation
 In anticipation of his upcoming conference...
Wise Practitioner – Text Analytics Interview Series: Michael Dessauer and Justin Kauhl at The Dow Chemical Company
 In anticipation of their upcoming conference...
Women in Data Science
 The field of Data Science is...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Edward Crowley at The Photizo Group, Inc.
 In anticipation of his upcoming Predictive Analytics...
Boosting Performance of Machine Learning Models
  People often get stuck when...
Wise Practitioner – Predictive Analytics Interview Series: Tanay Chowdhury at Zurich North America
 In anticipation of his upcoming conference...
Feature Engineering within the Predictive Analytics Process — Part One
 What is Feature Engineering One of...
The Executive’s Guide to Employee Attrition
 Much has been written about customer...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group
 In anticipation of his upcoming conference...
Wise Practitioner – Text Analytics Interview Series: John Herzer and Pengchu Zhang at Sandia National Laboratories
 In anticipation of their upcoming conference...
Wise Practitioner – Text Analytics Interview Series: Emrah Budur at Garanti Technology
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher at National Consumer Panel
 In anticipation of his upcoming conference...
Ghosts in the Data, Constructing Data Entities
 Data Entities are seldom discussed concepts...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Dr. Matteo Bellucci at General Electric
 In anticipation of his upcoming Predictive Analytics...
HR’s First Predictive Project? Pre-hire Candidate Screening
 Corp recruiters have a very important...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Gary Neights at Elemica
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Banks at The Applied Research Laboratory at The Pennsylvania State University
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Text Analytics Interview Series: Frédérick Guillot at Co-operators General Insurance Company
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Alice Chung at Genentech
 In anticipation of her upcoming conference...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Carlos Cunha at Robert Bosch, LLC
 In anticipation of his upcoming Predictive Analytics...
5 Common Mistakes Multi-Channel Retailers Make, and How to Avoid Them
  Multi-channel retailers are often finding...
Three Critical Definitions You Need Before Building Your First Predictive Model
 Portions excerpted from Chapter 2 of...
Measurement and Validation: An Often Underrated Aspect within the Predictive Analytics Discipline
 In our Big Data world, software...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Haig Nalbantian at Mercer
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Pasha Roberts at Talent Analytics, Corp.
 In anticipation of his upcoming Predictive Analytics...
Improving Word Clouds as Tool for Text Analytics Data Visualization
 Rich Lanza will present Using Letter...
Dr. Data’s Music Video: The Predictive Analytics Rap
 With today’s release of “Predict This!”...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Geetanjali Gamel from MasterCard
 In anticipation of her upcoming Predictive Analytics...
Mid-Life Journey to Data Science
 Data Science has been hailed as...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper
 In anticipation of his upcoming keynote...
What are you Predicting in Customer Retention?
 Customer Retention models are arguably the...
Wise Practitioner – Predictive Analytics Interview Series: Ken Elliott at Hewlett Packard Enterprise
 In anticipation of his upcoming conference...
Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller at Constellation Research
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan at Cicero Group
 In anticipation of his upcoming conference...
Hey FinTech, What’s Your Strategy for Leveraging Unstructured Data?
 Financial technology has sparked a global...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Raffael Devigus at F. Hoffmann-La Roche AG
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Rebecca Pang at CIBC
 In anticipation of her upcoming conference...
Employee Engagement – a Tricky Metric for Predictive Analytics
 Our work focuses on using predictive...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Daniil Shash at Eleks
 In anticipation of his upcoming Predictive...
The Information Age’s Latest Move: Four Predictive Analytics Developments for 2016
 Originally published in Big Think Prediction...
Why Do We Stop Asking Why?
 I’ve lived through this phenomenon first...
Predictive Analytics and the Internet of Things
 As technology continues to empower our...
Wise Practitioner – Predictive Analytics Interview Series: Mario Vinasco at Facebook
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Vishwa Kolla at John Hancock Insurance
 In anticipation of his upcoming Predictive Analytics...
In Predictive Analytics, Coefficients are Not the Same as Variable Influence, Part II
 In my last post, “Coefficients are...
Wise Practitioner – Predictive Workforce Analytics Interview Series: John Lee at Equifax Workforce Solutions
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Peter Bull at DrivenData
 In anticipation of his upcoming conference...
The “Predictive Analytics” FAQ — What’s New in the Updated Edition and Who’s The Book for?
 This is the preface to Eric...
Wise Practitioner – Predictive Analytics Interview Series: Matt Bentley at CanIRank.com
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Lisa Disselkamp and Tristan Aubert at Deloitte
 In anticipation of their upcoming Predictive Analytics...
The Data Scientist’s Dilemma: Does Skipping Breakfast Kill You?
 Would skipping breakfast kill you? Not necessarily—but confusing...
Predictive Analytics Can Help with the Challenges Facing Manufacturing in the 21st Century
 Historically, data and analytics have been...
Wise Practitioner – Predictive Analytics Interview Series: Nate Watson at Contemporary Analysis
 In anticipation of his upcoming conference...
Customer Experience Predictions for 2016
 As we look ahead and see...
Predictive Analytics Book Excerpt: Hands-On Guide—Resources for Further Learning
 Here is the Hands-On Guide that...
Wise Practitioner – Predictive Analytics Interview Series: Hans Wolters at Microsoft
 In anticipation of his upcoming conference...
Machine Learning: Not Necessarily a New Phenomenon in Predictive Analytics
 One of the more recent topics...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Frank Fiorille at Paychex, Inc.
 In anticipation of his upcoming Predictive Analytics...
Netflix, Dark Knowledge, and Why Simpler Can Be Better
 Weary from an all-night coding effort,...
The Case Against Quick Wins in Predictive Analytics Projects
 When beginning a new predictive analytics...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Jason Noriega at Chevron
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Analytics Interview Series: Matthew Pietrzykowski at General Electric
 In anticipation of his upcoming conference...
B2B Predictive Analytics: An Untapped Sector
 Much work in predictive analytics and...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Greg Tanaka at Percolata
 In anticipation of his upcoming Predictive Analytics...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Michael Li at The Data Incubator
 In anticipation of his upcoming Predictive Analytics...
Four Ways Data Science Goes Wrong and How Test-Driven Data Analysis Can Help
 If, as Niels Bohr maintained, an...
In Predictive Analytics, Coefficients are Not the Same as Variable Influence
 When we build predictive models, we...
Oracle’s Ten Enterprise Big Data Predictions for 2016
 Companies big and small are finding...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part III: The Expert
 So you have gathered your data...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Kathy Doan at Wells Fargo Bank
 In anticipation of her upcoming Predictive Analytics...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Jonathon Frampton at Baylor Scott & White Health
 In anticipation of his upcoming Predictive Analytics...
Mobile Analytics-Mining the Visit Experience of the Customer
 Mobile technology as part of the...
The Devil’s Data Dictionary – Making Fun of Big Data
 Buy it on Amazon When Stéphane...
Wise Practitioner – Predictive Workforce Analytics Interview Series: Ben Waber of Humanyze
 In anticipation of his upcoming Predictive Analytics...
The Quest for Unicorns
 Will there be enough data scientists...
Most Swans are White: Living in a Predictive Society
 In anticipation of the forthcoming Revised...
Hiring? Approving Mortgages? It’s the Same Thing
 Imagine that Chris wants to buy...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part II: The Skeptic
 So you have gathered your data...
5 Types of Analytics in Business: One to Go After and One to Avoid
 I have been lucky enough to...
The Beginner’s Guide to Predictive Workforce Analytics
 Human Resources Feels Pressure to Begin...
Predictive Modeling Forensics: Identifying Data Problems
 Excerpted and modified from Chapters 3...
Five Wins for Retail with Predictive Analytics
 We’ve heard a lot about how...
Faster Credit Scoring Dev With Specialized Binning Code – R Package
 Introduction One of the main concerns...
Good Predictions != Good Decisions
 A Fateful Tale Ted is having...
Using Predictive Analytics to Bring Retailers Closer to Their Customers
 Based on the amount of retailers...
Visualization: Panacea for Building Analytics Solutions?
 Data,data,data everywhere and what do I...
Five Challenges in Using Predictive Analytics to Improve Patient Outcomes
 In the increasingly patient-centric world of...
Personalities That Are Barriers to Model Deployment (And How to Partner With Them) Part I: The Early Adopter
 So you have gathered your data...
Five Ways Predictive Analytics Will Shape the Future of Advertising
 Predictive analytics sounds almost mystical, and...
Five Ways Predictive Analytics Can Improve Patient Outcomes
 The use of analytics in healthcare...
Wise Practitioner – Predictive Analytics Interview Series: Scott Lancaster at State Street Corp.
 In anticipation of his upcoming conference...
Can Employee Development Lead to Business Mediocrity?
 Our predictive workforce assignments yield staggering...
Empathy and Data Science: A Fable of Near-Success
 Editor’s Note: While the story is...
Wise Practitioner – Predictive Analytics Interview Series: Jeff Butler at IRS Research, Analysis, and Statistics organization
 In anticipation of his upcoming conference...
A Look at How Big Data is Changing Sports on the Field and in the Press Box
 While major rules rarely change, everything...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Satyam Priyadarshy at Halliburton
 In anticipation of his upcoming conference...
Automation: Friend or Foe to the Predictive Analytics Practitioner
 Technologies and Big Data continue to...
Winning Roles: Moneyball 2.0, for your Hiring and Succession Planning Processes
 Business can learn a lot from...
Wise Practitioner – Predictive Analytics Interview Series: Werner Britz at RCS Group
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Michael Dulin, Carolinas Healthcare System
 In anticipation of his upcoming keynote...
Wise Practitioner – Predictive Analytics Interview Series: Benjamin Uminsky, Los Angeles County
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Jessica Taylor of St. Joseph Healthcare
 In anticipation of her upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: COL William Saxon, Department of the Army
 In anticipation of his upcoming conference...
Defensive Data Science: What we can Learn from Software Engineers
  To view this content OR...
Wise Practitioner – Predictive Analytics Interview Series: Patty Larsen, Co-Director, National Insider Threat Task Force
  To view this content OR...
Wise Practitioner – Predictive Analytics Interview Series: Bin Mu at MetLife
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Catherine Templeton, PAWGOV Keynote Speaker
 In anticipation of her upcoming keynote...
Wise Practitioner – Predictive Analytics Interview Series: William Wood of St. Joseph Healthcare
 In anticipation of his upcoming conference...
The Key to Modelling Success-The Variable Selection Process (Part 2)
 Last month, I discussed the importance...
Wise Practitioner – Predictive Analytics Interview Series: Madhusudan Raman at Verizon
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Scott Jelinsky of Pfizer, Inc.
 In anticipation of his upcoming conference...
Wise Practitioner – Predictive Analytics Interview Series: Chris Franciskovich at OSF Healthcare System
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Wise Practitioner – Predictive Analytics Interview Series: Philip O’Brien at Paychex
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Wise Practitioner – Predictive Analytics for Healthcare Interview Series: Daniel Chertok at NorthShore University HealthSystem
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Wise Practitioner – Predictive Analytics Interview Series: Herman Jopia of American Savings Bank
 In anticipation of his upcoming conference...
Stop Hiring Data Scientists Until You’re Ready for Data Science
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How to manage projects in Predictive Analytics
 In the previous five years, the...
Wise Practitioner – Predictive Analytics Interview Series: Lawrence Cowan of Cicero Group
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Defining Measures of Success for Cluster Models
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Good luck placing Analytics in an org chart
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Retail Predictive Analytics Solves the Missing Link in Cross Selling, Up Selling, and Suggestive Selling
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Sameer Chopra’s Hotlist of Training Resources for Predictive Analytics
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Wise Practitioner – Predictive Analytics Interview Series: John Smits of EMC
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Be a Data Detective
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Predicting Employee Flight Risk: My Take
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The Key to Modelling Success -The Variable Selection Process (Part 1)
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Predictive Analytics World in Color [Infographic]
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Space Alien Eager to Convey Thoughts on Data Science
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Defining Measures of Success for Predictive Models
 Excerpted from Chapters 2 and 9...
Overstatement of Results in Predictive Analytics
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The Biggest Lever to Success in Predictive Analytics
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Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Field Cady at Think Big Analytics
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1 year ago
Employee Life Time Value and Cost Modeling

 

Understanding the Most Expensive Asset

Practically every business shares the same biggest cost – employees. This makes sense – even in this age of robots and computers, human talent is behind everything that a company does. People are the source of innovation, growth, and competitive edge for every company.

Given this importance, it’s a bit strange that data science is only beginning to look inward at the workforce. We have measured the consumer behavior from every angle. We can quote the lifetime value to our customers to three decimal points, though we don’t really know them. Our employee relationships are deeper, longer-term, stickier, and more laden with potential value than customers in almost every industry.

But, most hiring and employee development happens by intuition or chance. Long-term workforce planning is only done at a very high level for very common roles. Rules of thumb and industry "benchmarks" from magazine articles dominate employee strategy. There are more rigorous and methodical methods available.

Are Employees Costs or Assets?

From a GAAP accounting perspective, most employee expenditures are considered to be costs. Employees are not subject to depreciation, as are machines, for example. This isn’t changing anytime soon.

But from a management point of view, employees are more like a portfolio of assets – with interlocking strengths, weaknesses, and capacities. If you run a call center, your staff is your primary tool for processing calls, arguably more important than the phone switch. Likewise for a software development group or a marketing department and their tasks.

In this sense, employees are key assets, much like machines, for getting work done. If you hire or develop a more efficient and long-lasting employee, production will go up. We encourage employers to not only value their workers as human beings, but also to think of people as productive units that can be intelligently optimized for targeted outcomes.

The Basis for Advanced Analytics

In this chapter we present a powerful first step to advanced employee analytics. We will walk through a cohesive framework for measuring the cost, performance and attrition of a workforce.

These three key metrics (cost, performance, attrition) combine in interesting ways to inform decision-making. Most importantly, the metrics are a quantitative baseline for predictive analytics exercises. Only with these metrics will we be able to learn how to apply predictive models or whether the models are improving operations.

Note that each of these three metrics are not a single number, but a series of values across the lifetime of an employee. Technically they are a time series or vector, but we will use the terms "metric" and "curve" interchangeably to describe them.

Lifetime Value (LTV)

One notable takeaway of this chapter is a lifetime value of an employee in a role. The lifetime value of a customer is often defined as "a prediction of the net profit attributed to the entire future relationship with a customer."

Our calculations of Employee Lifetime Value (LTV) refer to "a prediction of the net profit attributed to an employee through their tenure in a given role." If someone changes roles, say from Customer Service III to Internal Sales IV, a new lifetime value will apply.

We limit our analysis to one role at a time – say Underwriter I, or Teller II, or Customer Service III. We don’t attempt to predict an employee’s promotion path or their ultimate journey through the company.

Lifetime Value is probability-weighted by the risk of attrition from the role. Because it is risk-weighted, the LTV is an excellent roll-up number to compare programs or scenarios, with a direct tie to the bottom line.

Key Takeaways

We address employee cost, performance, attrition and lifetime value to bring the practice of Human Resources into the information age. Only with these metrics will the actual value and dynamics of our "Human Resources" be known. We imagine a future where enterprise software dashboards commonly report this information, and the curves are routinely used in planning. With this kind of information innovation, business operations will be able to routinely measure and apply productive gains from advanced analytics.

Executives will learn key workforce measurement concepts that will enable a new level of business intelligence. With these key metrics in focus, executives will be able to lead predictive analytics efforts throughout the enterprise.

Human Resources, Recruiting, and Staffing Professionals will learn to measure what happens to new hires after they leave the HR funnel and enter the workplace. Intelligent feedback from workplace performance/attrition can effectively inform hiring efforts to be smarter and more targeted.

Line of Business Managers, such as Sales Operations or Call Center Managers, will learn to format their employee operations into enterprise-relevant information.

Data Scientists, Analysts, and IT professionals will learn the business context for their analysis. These analysts can apply the knowledge to identify information sources and formats for dashboards and predictive analytics.

The Three Vital Metrics for Every Employment Role

One Role at a Time

First, it is only meaningful to evaluate one role at a time, one company at a time. The curves and dynamics for Accounting will be very different than that for Inside Sales. Likewise, "industry benchmarks" are next to useless – companies differ, regions are different, and enterprises evolve over time. It is easy enough to gather this information for your own company’s roles, and we suggest investing the time to simply do so.

Some roles have more volume and size than others. A Call Center or Underwriter role will have plenty of data for great accuracy. Executive leadership is a small sample with less turnover – not as useful for analytics.

Time Frame

Often this exercise comes about in response to attrition or training issues, which manifest in the first year or two. The simplest of all is an entry-level position that automatically promotes after a year or two.

These short-term cases are easier to calculate than long-term employees. Beyond a few years, cost and performance factors get more complicated with raises, equity, inflation, and the time value of money. Long-term employees also vary in performance patterns – some continue to learn, while others coast or "check out."

Three Common-Sense Numbers

Three common-sense questions underlie our three metrics:

  1. How much does it cost to find, train, and keep an employee in this role?
  2. How much does an employee in this role contribute to the business top line?
  3. How long do people tend to stay in this role?

These calculations are usually done at a high level, aggregating costs and performance for everyone in a single role. More advanced approaches seek out clusters or individual patterns across thousands of employees. These "big data" approaches are more useful for transaction and revenue-related roles.

Figure 1. The Three Curves

The Cost Curve tracks how much money is spent on an employee in the role, over time. It is like a daily log of costs for a new employee, from day 1. After a flurry of recruiting, orienting, training, and on-boarding costs, the expenses typically level out as salary and infrastructure.

The Performance Curve estimates the contribution an employee makes to the company, starting at day 1. Typically until training and orientation are complete, that number is zero. Employees typically "ramp up" to productivity after weeks, months, or years. After ramp-up, that level may plateau, increase gradually, or even bow downwards after years. The ultimate level of contribution can be calculated directly for some roles, estimated for others.

The Attrition Curve shows the probability of an employee being in the role at different points of tenure. On the first day, that number is close to 100%. This number is the flip side of turnover – if a role has 40% annual turnover, there is a 60% chance of being on the job in a year. The full curve is easily calculated from the HR System of Record, and is a powerful tool.

The three in combination are exceedingly powerful.

In Part 2 of this blog, Pasha will dive more deeply into these 3 curves and show how they affect an organization’s ability to predict performance, turnover and employee lifetime value.

Author Bio:

Pasha RobertsPasha Roberts is chief scientist at Talent Analytics Corp., a company that uses data science to model and optimize employee performance in areas such as call center staff, sales organizations and analytics professionals. He wrote the first implementation of the company’s software over a decade ago and continues to drive new features and platforms for the company. He holds a bachelor’s degree in economics and Russian studies from The College of William and Mary, and a master of science degree in financial engineering from the MIT Sloan School of Management.

Read More:

People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent Hardcover – April 25, 2016
by Jean Paul Isson  (Author), Jesse S. Harriott (Author), Jac Fitz-enz (Foreword)

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